WO2025217757A1 - Encoding method, decoding method, bit stream, decoder, encoder, and storage medium - Google Patents

Abstract

Provided in the present application are an encoding method, a decoding method, a bit stream, a decoder, an encoder, and a storage medium. At a decoding end, the decoding method comprises: decoding a bit stream, and determining first syntax element information of the current point; on the basis of the value of the first syntax element information, determining the optimal prediction mode of the current point from candidate prediction modes as a target cross-attribute prediction mode; on the basis of the target cross-attribute prediction mode, determining one or more attribute reconstruction values of a target neighbor point of the current point; on the basis of the one or more attribute reconstruction values of the target neighbor point, determining a correlation coefficient of the target neighbor point; and on the basis of an attribute reference value of an attribute to be decoded of the current point and the correlation coefficient, determining an attribute prediction value of said attribute of the current point. The present application can improve the utilization rate of a bit rate, such that the encoding and decoding efficiency is improved.

Description

Coding and decoding method, code stream, decoder, encoder and storage medium Technical Field

The embodiments of the present application relate to the field of video coding and decoding technology, and in particular to a coding and decoding method, a bit stream, a decoder, an encoder, and a storage medium.

Background Art

In the attribute coding framework of Geometry-based Point Cloud Compression (GPCC/G-PCC), intra-frame prediction and inter-frame prediction are introduced to remove temporal and spatial redundancy in point cloud encoding. However, when G-PCC encodes point cloud sequences with multiple attributes (such as color and reflectivity), the redundancy between these attributes has not been fully explored and removed.

In related technologies, one type of already encoded attribute information is often used to guide the encoding of another type of attribute information. However, when one type of attribute information is already encoded and another type of attribute information is encoded, there is still a lot of redundancy between the two types of attribute information, which results in a waste of bit rate and affects the encoding and decoding efficiency.

Summary of the Invention

The embodiments of the present application provide a coding and decoding method, a code stream, a decoder, an encoder, and a storage medium, which can improve the utilization of the code rate and thus improve the coding and decoding efficiency.

The technical solution of the embodiment of the present application can be implemented as follows:

In a first aspect, an embodiment of the present application provides a decoding method, applied to a decoder, the method comprising:

Decode the code stream and determine the first syntax element information of the current point;

Determining, according to the value of the first syntax element information, the best prediction mode for the current point from the candidate prediction modes as the target cross-attribute prediction mode;

Determining one or more attribute reconstruction values of target neighboring points of the current point according to the target cross-attribute prediction mode;

Determining a correlation coefficient of the target neighboring point based on one or more attribute reconstruction values of the target neighboring point;

Determine an attribute prediction value of the attribute to be decoded at the current point according to the attribute reference value of the attribute to be decoded at the current point and the correlation coefficient.

In a second aspect, an embodiment of the present application provides an encoding method, applied to an encoder, the method comprising:

Determining a candidate cross-attribute prediction mode for the current point from candidate prediction modes;

Determining one or more attribute reconstruction values of candidate neighboring points of the current point according to the candidate cross-attribute prediction mode;

Determining a correlation coefficient of the candidate neighboring points based on one or more attribute reconstruction values of the candidate neighboring points;

Determining an attribute prediction value of the attribute to be encoded at the current point according to the attribute reference value of the attribute to be encoded at the current point and the correlation coefficient;

Performing a coding decision based on the attribute prediction values of the to-be-coded attribute of the current point corresponding to one or more candidate cross-attribute prediction modes, and determining the best prediction mode of the current point as a target cross-attribute prediction mode;

Determining, according to the optimal prediction mode, a value of the first syntax element information of the current point;

The first syntax element information is coded and the obtained coded bits are written into a bitstream.

In a third aspect, an embodiment of the present application provides a code stream, which is generated by bit encoding based on information to be encoded; wherein the information to be encoded includes at least one of the following:

First syntax element information, second syntax element information, and third syntax element information; the first syntax element information is used to indicate the prediction mode adopted by the current point, the second syntax element information is used to indicate the attribute reconstruction order of the current point, and the third syntax element information is used to indicate whether the current point allows the use of a cross-attribute prediction mode.

In a fourth aspect, an embodiment of the present application provides a decoder, comprising a decoding part and a first determining part, wherein:

The decoding part is configured to decode the code stream and determine the first syntax element information of the current point;

The first determination part is configured to determine, from the candidate prediction modes, the best prediction mode of the current point as the target cross-attribute prediction mode according to the value of the first syntax element information; determine one or more attribute reconstruction values of the target neighboring points of the current point according to the target cross-attribute prediction mode; determine the correlation coefficient of the target neighboring points according to the one or more attribute reconstruction values of the target neighboring points; determine the attribute reference value of the attribute to be decoded at the current point and the correlation coefficient according to the attribute reference value of the attribute to be decoded at the current point; Attribute prediction value.

In a fifth aspect, an embodiment of the present application provides an encoder, comprising an encoding part and a second determining part, wherein:

The second determination part is configured to determine the candidate cross-attribute prediction mode of the current point from the candidate prediction modes; determine one or more attribute reconstruction values of the candidate neighboring points of the current point according to the candidate cross-attribute prediction mode; determine the correlation coefficient of the candidate neighboring points according to the one or more attribute reconstruction values of the candidate neighboring points; determine the attribute prediction value of the attribute to be encoded at the current point according to the attribute reference value of the attribute to be encoded at the current point and the correlation coefficient; make a coding decision based on the attribute prediction value of the attribute to be encoded at the current point corresponding to the one or more candidate cross-attribute prediction modes, and determine the best prediction mode of the current point as the target cross-attribute prediction mode; determine the value of the first syntax element information of the current point according to the best prediction mode;

The encoding part is configured to perform encoding processing on the first syntax element information and write the obtained encoded bits into a bitstream.

In a sixth aspect, an embodiment of the present application provides a decoder, comprising a first memory and a first processor, wherein:

The first memory is configured to store a computer program that can be executed on the first processor;

The first processor is configured to execute the method according to the first aspect when running the computer program.

In a seventh aspect, an embodiment of the present application provides an encoder, comprising a second memory and a second processor, wherein:

The second memory is configured to store a computer program that can be executed on the second processor;

The second processor is configured to execute the method according to the second aspect when running the computer program.

In an eighth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed, it implements the method described in the first aspect or the second aspect.

The embodiment of the present application provides a coding and decoding method, a code stream, a decoder, an encoder and a storage medium. On the decoding side, the decoding method includes: decoding the code stream, determining the first syntax element information of the current point; determining the best prediction mode of the current point as the target cross-attribute prediction mode from the candidate prediction modes according to the value of the first syntax element information; determining one or more attribute reconstruction values of the target neighboring points of the current point according to the target cross-attribute prediction mode; determining the correlation coefficient of the target neighboring points according to the one or more attribute reconstruction values of the target neighboring points; determining the attribute prediction value of the attribute to be decoded of the current point according to the attribute reference value and the correlation coefficient of the attribute to be decoded of the current point. On the encoding side, the candidate cross-attribute prediction mode of the current point is determined from the candidate prediction mode; on the one hand, by determining the attribute prediction value of the attribute to be decoded of the current point through the target cross-attribute prediction mode and the correlation coefficient of the target neighboring points, the attribute value of the current point can be predicted more accurately, avoiding the transmission of redundant information, thereby reducing the amount of data to be transmitted during encoding, thereby reducing the waste of bit rate. On the one hand, in the process of predicting the attribute reconstruction value of the attribute to be decoded at the current point, it is determined based on the correlation coefficient of the target neighboring point of the current point and the attribute reference value of the attribute to be decoded at the current point, which can improve the utilization of the code rate, avoid the waste of code rate, reduce the redundancy and cost during data transmission, and thus improve the efficiency and performance of decoding.

On the encoding side, the encoding method includes: determining one or more attribute reconstruction values of candidate neighboring points of the current point based on candidate cross-attribute prediction modes; determining the correlation coefficient of the candidate neighboring points based on the one or more attribute reconstruction values of the candidate neighboring points; determining the attribute prediction value of the attribute to be encoded at the current point based on the attribute reference value and correlation coefficient of the attribute to be encoded at the current point; making an encoding decision based on the attribute prediction value of the attribute to be encoded at the current point corresponding to one or more candidate cross-attribute prediction modes, and determining the optimal prediction mode for the current point as the target cross-attribute prediction mode; determining the value of the first syntax element information of the current point based on the optimal prediction mode; encoding the first syntax element information and writing the resulting coded bits into the bitstream. On the one hand, by determining the attribute prediction value of the attribute to be encoded at the current point through the candidate cross-attribute prediction modes and the correlation coefficient of the candidate neighboring points, the attribute value of the current point can be predicted more accurately, avoiding the transmission of redundant information, thereby reducing the amount of data required to be transmitted during encoding, and thus reducing bit rate waste. On the one hand, in the process of predicting the attribute reconstruction value of the attribute to be encoded at the current point, it is determined based on the correlation coefficient of the candidate neighboring points of the current point and the attribute reference value of the attribute to be encoded at the current point, which can improve the utilization of the code rate, avoid the waste of code rate, reduce the redundancy and cost during data transmission, and thus improve the efficiency and performance of coding.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1A is a schematic diagram of a three-dimensional point cloud image provided in an embodiment of the present application;

FIG1B is a partially enlarged view of a three-dimensional point cloud image provided in an embodiment of the present application;

FIG2A is a schematic diagram of six viewing angles of a point cloud image provided by an embodiment of the present application;

FIG2B is a schematic diagram of a data storage format corresponding to a point cloud image provided in an embodiment of the present application;

FIG3 is a schematic diagram of a network architecture of point cloud encoding and decoding provided by an embodiment of the present application;

FIG4A is a schematic diagram of a composition framework of a G-PCC encoder provided in an embodiment of the present application;

FIG4B is a schematic diagram of a composition framework of a G-PCC decoder provided in an embodiment of the present application;

FIG5 is a schematic diagram of a PT encoding process provided in an embodiment of the present application;

FIG6 is a schematic diagram of a distance-based LoD generation process provided in an embodiment of the present application;

FIG7 is a first flow chart of a method for determining an optimal prediction mode according to an embodiment of the present application;

FIG8 is a flowchart diagram of a decoding method provided in an embodiment of the present application;

FIG9 is a schematic diagram of a correlation between brightness and reflectivity provided in an embodiment of the present application;

FIG10 is a second flow chart of a decoding method provided in an embodiment of the present application;

FIG11 is a schematic diagram of a flow chart of an encoding method provided in an embodiment of the present application;

FIG12 is a second schematic diagram of a process for determining an optimal prediction mode according to an embodiment of the present application;

FIG13 is a schematic diagram of a flow chart of an encoding end implementation provided in an embodiment of the present application;

FIG14 is a schematic diagram of a process flow implemented by a decoding end according to an embodiment of the present application;

FIG15 is a schematic diagram of the structure of a decoder provided in an embodiment of the present application;

FIG16 is a schematic diagram of a specific hardware structure of a decoder provided in an embodiment of the present application;

FIG17 is a schematic diagram of the structure of an encoder provided in an embodiment of the present application;

FIG18 is a schematic diagram of a specific hardware structure of an encoder provided in an embodiment of the present application;

FIG19 is a schematic diagram of the composition structure of a coding and decoding system provided in an embodiment of the present application.

DETAILED DESCRIPTION

In order to enable a more detailed understanding of the features and technical contents of the embodiments of the present application, the implementation of the embodiments of the present application is described in detail below with reference to the accompanying drawings. The attached drawings are for reference only and are not used to limit the embodiments of the present application.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

It should also be pointed out that the terms "first\second\third" involved in the embodiments of the present application are only used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present application described here can be implemented in an order other than that illustrated or described here.

Point Cloud is a three-dimensional representation of the surface of an object. Point Cloud (data) on the surface of an object can be collected through acquisition equipment such as photoelectric radar, lidar, laser scanner, and multi-view camera.

A point cloud is a set of irregularly distributed discrete points in space that express the spatial structure and surface properties of a three-dimensional object or scene. Figure 1A shows a three-dimensional point cloud image and Figure 1B shows a partially enlarged view of the three-dimensional point cloud image. It can be seen that the point cloud surface is composed of densely distributed points.

In a two-dimensional image, each pixel contains information and is distributed regularly, so there's no need to record its location. However, the distribution of points in a point cloud in three-dimensional space is random and irregular, so recording the location of each point in space is necessary to fully represent the point cloud. Similar to a two-dimensional image, each location in the acquisition process has corresponding attribute information, typically an RGB color value, which reflects the object's color. For a point cloud, in addition to color information, each point's corresponding attribute information often includes reflectance values, which reflect the surface texture of the object. Therefore, point cloud data typically includes both point location information and point attribute information. Point location information can also be referred to as point geometric information. For example, point geometric information can be the point's three-dimensional coordinates (x, y, z). Point attribute information can include color information and/or reflectance. For example, reflectance can be one-dimensional reflectance information (r). Color information can be information in any color space, or it can be three-dimensional color information, such as RGB. Here, R represents red (R), G represents green (G), and B represents blue (B). For another example, the color information may be luminance and chrominance (YCbCr, YUV) information, where Y represents brightness (Luma), Cb (U) represents blue color difference, and Cr (V) represents red color difference.

For example, a point cloud generated using laser measurement principles can include both its 3D coordinate information and its reflectivity. For another example, a point cloud generated using photogrammetry principles can include both its 3D coordinate information and its 3D color information. For another example, a point cloud generated using a combination of laser measurement and photogrammetry principles can include both its 3D coordinate information, its reflectivity value, and its 3D color information.

Figures 2A and 2B show a point cloud image and its corresponding data storage format. Figure 2A provides six viewing angles of the point cloud image, while Figure 2B consists of a file header and data. The header includes the data format, data representation type, the total number of points in the point cloud, and the content represented by the point cloud. For example, the point cloud is in ".ply" format, represented by ASCII code, with a total of 207,242 points. Each point has 3D coordinate information (x, y, z) and 3D color information (r, g, b).

Point clouds can be divided into the following categories according to the acquisition method:

Static point cloud: the object is stationary and the device that acquires the point cloud is also stationary;

Dynamic point cloud: The object is moving, but the device that obtains the point cloud is stationary;

Dynamic point cloud acquisition: The device used to acquire the point cloud is in motion.

For example, point clouds can be divided into two categories according to their usage:

Category 1: Machine perception point cloud, which can be used in scenarios such as autonomous navigation systems, real-time inspection systems, geographic information systems, visual sorting robots, and disaster relief robots;

Category 2: Human eye perception point cloud, which can be used in point cloud application scenarios such as digital cultural heritage, free viewpoint broadcasting, 3D immersive communication, and 3D immersive interaction.

Point clouds can flexibly and conveniently express the spatial structure and surface properties of three-dimensional objects or scenes. Moreover, since point clouds are obtained by directly sampling real objects, they can provide a strong sense of reality while ensuring accuracy. Therefore, they are widely used, including virtual reality games, computer-aided design, geographic information systems, automatic navigation systems, digital cultural heritage, free viewpoint broadcasting, three-dimensional immersive remote presentation, and three-dimensional reconstruction of biological tissues and organs.

Point clouds are primarily collected through computer generation, 3D laser scanning, and 3D photogrammetry. Computers can generate point clouds of virtual 3D objects and scenes; 3D laser scanning can obtain point clouds of static real-world 3D objects or scenes, generating millions of point clouds per second; and 3D photogrammetry can obtain point clouds of dynamic real-world 3D objects or scenes, generating tens of millions of point clouds per second. These technologies reduce the cost and time required to acquire point cloud data while improving data accuracy. While changes in point cloud data acquisition methods have made it possible to acquire large amounts of point cloud data, the processing of this massive amount of 3D point cloud data is facing bottlenecks due to storage space and transmission bandwidth constraints, as application demands grow.

For example, taking a point cloud video with a frame rate of 30 frames per second (fps), each frame contains 700,000 points, and each point has coordinate information (xyz, float) and color information (RGB, uchar). Therefore, the data volume of a 10-second point cloud video is approximately 0.7 million × (4 bytes × 3 + 1 byte × 3) × 30 fps × 10 seconds = 3.15 GB, where 1 byte is 10 bits. For a 1280 × 720 2D video with a YUV sampling format of 4:2:0 and a frame rate of 24 fps, the data volume for 10 seconds is approximately 1280 × 720 × 12 bits × 24 fps × 10 seconds ≈ 0.33 GB. The data volume of a 10-second two-view 3D video is approximately 0.33 × 2 = 0.66 GB. This shows that the data volume of a point cloud video far exceeds that of 2D and 3D videos of the same length. Therefore, in order to better realize data management, save server storage space, and reduce the transmission traffic and transmission time between the server and the client, point cloud compression has become a key issue in promoting the development of the point cloud industry.

That is to say, since the point cloud is a collection of massive points, storing the point cloud not only consumes a lot of memory, but is also not conducive to transmission. There is also not enough bandwidth to support direct transmission of the point cloud at the network layer without compression. Therefore, the point cloud needs to be compressed.

Currently, point cloud coding frameworks that can compress point clouds can be the Geometry-based Point Cloud Compression (G-PCC) codec framework or the Video-based Point Cloud Compression (V-PCC) codec framework provided by the Moving Picture Experts Group (MPEG), or the AVS-PCC codec framework provided by AVS. The G-PCC codec framework can be used to compress the first type of static point clouds and the third type of dynamically acquired point clouds, and can be based on the Point Cloud Compression Test Platform (Test Model Compression 13, TMC13). The V-PCC codec framework can be used to compress the second type of dynamic point clouds, and can be based on the Point Cloud Compression Test Platform (Test Model Compression 2, TMC2). Therefore, the G-PCC codec framework is also called the Point Cloud Codec TMC13, and the V-PCC codec framework is also called the Point Cloud Codec TMC2.

An embodiment of the present application provides a network architecture of a point cloud encoding and decoding system including a decoding method and an encoding method. FIG3 is a schematic diagram of a network architecture of a point cloud encoding and decoding system provided by an embodiment of the present application. As shown in FIG3 , the network architecture includes one or more electronic devices 13 to 1N and a communication network 01, wherein the electronic devices 13 to 1N can perform video interaction through the communication network 01. During the implementation process, the electronic device can be various types of devices with point cloud encoding and decoding functions. For example, the electronic device can include a mobile phone, a tablet computer, a personal computer, a personal digital assistant, a navigator, a digital phone, a video phone, a television, a sensor device, a server, etc., which is not limited by the embodiment of the present application. Among them, the decoder or encoder in the embodiment of the present application can be the above-mentioned electronic device.

Among them, the electronic device in the embodiment of the present application has a point cloud encoding and decoding function, generally including a point cloud encoder (ie, encoder) and a point cloud decoder (ie, decoder).

The following describes the related technologies using the G-PCC encoding and decoding framework as an example.

Related technology 1:

In the point cloud G-PCC codec framework, the point cloud data to be encoded is first divided into multiple slices through slice partitioning. In each slice, the geometric information of the point cloud and the attribute information corresponding to each point are encoded separately.

Figure 4A shows a schematic diagram of the G-PCC encoder's architecture. As shown in Figure 4A, during the geometry encoding process, the geometric information is transformed so that the entire point cloud is contained within a bounding box. Quantization is then performed. This quantization step primarily serves a scaling purpose. Due to quantization rounding, the geometric information of some point clouds becomes identical. Parameters are then used to determine whether to remove duplicate points. This process of quantization and removing duplicate points is also known as voxelization. The bounding box is then partitioned into an octree or constructed as a prediction tree. During this process, arithmetic coding is performed on the points within the leaf nodes of the partition to generate a binary geometry bitstream. Alternatively, arithmetic coding is performed on the intersection points (vertices) generated by the partition (surface fitting is performed based on the intersection points) to generate a binary geometry bitstream. During the attribute encoding process, after the geometry encoding is completed and the geometry information is reconstructed, color conversion is performed to convert the color information (i.e., attribute information) from the RGB color space to the YUV color space. The reconstructed geometry information is then used to recolor the point cloud so that the unencoded attribute information corresponds to the reconstructed geometry information. Attribute encoding is mainly performed on color information. In the process of color information encoding, the main There are two transformation methods. One is a distance-based lifting transform that relies on the level of detail (LOD) division, and the other is a direct region adaptive hierarchical transform (RAHT) followed by arithmetic coding of the quantized coefficients to generate a binary attribute bitstream.

Figure 4B shows a schematic diagram of the composition framework of a G-PCC decoder. As shown in Figure 4B, for the acquired binary bit stream, the geometric bit stream and attribute bit stream in the binary bit stream are first decoded independently. When decoding the geometric bit stream, the geometric information of the point cloud is obtained through arithmetic decoding-reconstruction of the octree/reconstruction of the prediction tree-reconstruction of the geometry-coordinate inverse conversion; when decoding the attribute bit stream, the attribute information of the point cloud is obtained through arithmetic decoding-inverse quantization-LOD partitioning/RAHT-color inverse conversion, and the point cloud data to be encoded (i.e., the output point cloud) is restored based on the geometric information and attribute information.

Related technology 2:

In the point cloud G-PCC codec framework, predictive transform (PT) encoding of point cloud attribute information is a technique used for point cloud data compression. It combines attribute prediction and transform coding methods to achieve efficient compression of point cloud data. Specifically, attributes in the point cloud data are first predicted. This can be done by analyzing the attribute values of neighboring points or leveraging prior knowledge. The goal of prediction is to estimate the value of each attribute in the point cloud as accurately as possible for subsequent compression and encoding. After prediction, the predicted attribute value is compared with the original attribute value to obtain the prediction error or residual. Next, entropy encoding is performed on the quantized coefficients, typically using techniques such as Huffman coding or arithmetic coding to further compress the data. The receiver decodes and decompresses the encoded data, recovering the prediction error and adding it to the predicted value to obtain the reconstructed attribute value. The resulting reconstructed attribute value can be used to restore the original point cloud data. PT encoding effectively leverages the attribute correlation and predictability in point cloud data, achieving efficient compression and transmission of point cloud data.

FIG5 is a schematic diagram of a PT encoding process provided by an embodiment of the present application. As shown in FIG5 , first, the original point cloud is divided into levels of detail (LoDs) according to the LoD generation order. Then, the attributes of the current point to be encoded are predicted using the reconstructed points (the three nearest neighbors of the i-th point). Furthermore, the prediction residual of the current point to be encoded is obtained by subtracting the attribute prediction value from the original attribute value of the i-th point. Finally, the prediction residual is quantized and entropy coded to generate an attribute code stream.

The following will give a detailed introduction to the PT prediction coding process according to the above steps.

Step 1: LoD generation:

The G-PCC software platform currently uses a distance-based LoD construction method. As shown in Figure 6, the original sequence includes the following points: P1, P2, P3, P4, P5, P6, P7, P8, P9, and P10. The LoD sequence includes three refinement levels: LoD1, LoD2, and LoD3. (R _l ) _{l = 0…L-1} represents the refinement level, and L is the number of LoD levels. The specific steps for constructing LoD are as follows:

(1) The user defines L Euclidean distances (d _l ) _{l = 0…L-1} and divides the network into L refinement levels (R _l ) _{l = 0…L-1} ;

(2) Mark all points as unvisited and set the visited point set V to an empty set;

(3) Generation of refinement layer _Rl :

In some embodiments, all points are traversed. If the current point has been visited, it is ignored. Otherwise, the minimum distance D between the current point and the point set V is calculated. If D is less than _dl , the current point is ignored. Otherwise, the current point is marked as visited and added to _Rl and V. The above process is repeated until all points have been traversed.

(4) Take the union of the refinement layers R ₀ , R ₁ , ..., R _l to get the detail layer LoD _l (i.e. );

(5) Repeat the above process until all points have been visited.

Step 2: Select the optimal prediction value:

In some embodiments, as shown in FIG7 , the process of determining the optimal prediction mode in the related art includes S11 to S17:

S11. Calculate the maximum attribute difference of the three candidate neighbors.

In some embodiments, after the LoD is constructed, the three nearest neighboring points of the current point to be encoded are first found from the encoded data points according to the LoD generation order. The attribute reconstructed values of these three nearest neighboring points are used as candidate prediction values of the current point to be encoded.

S12: Whether the maximum attribute difference is greater than a preset threshold.

In some embodiments, the maximum attribute difference max_difference of the three candidate neighbors is calculated. If the maximum attribute difference is greater than a preset threshold (adaptive threshold adaptive_threshold), S13 is executed. If the maximum attribute difference is less than or equal to the preset threshold (adaptive threshold adaptive_threshold), S14 is executed.

S13. Calculate the scores of prediction modes 0 to 3 using RDO.

In some embodiments, if the value of max_difference is greater than the adaptive threshold adaptive_threshold, the optimal prediction value is selected according to Rate-Distortion Optimal (RDO). The scores of modes 0 to 3 are calculated, and the minimum cost score is found as the optimal prediction mode.

S14. Best prediction mode = 0.

In some embodiments, if the value of max_difference is less than the adaptive threshold adaptive_threshold, the three neighboring points are considered to have attribute values close to the predicted point, and thus mode 0 weighted prediction is adopted.

S15. Find the minimum score.

In some embodiments, the mode corresponding to the minimum cost score among the scores of modes 0 to 3 is taken as the best prediction mode.

S16. Setting the optimal prediction mode.

S17, optimal prediction mode = 0 to 3.

Table 1 is a schematic table of candidate prediction modes for attribute coding provided in an embodiment of the present application. For prediction mode 0, the attribute prediction value of the point to be coded (also called the current point or the node before the point) is the weighted average of the attributes of the three nearest neighbors of the point to be coded. For prediction mode 1, the attribute prediction value of the point to be coded is the attribute value of the first nearest neighbor of the point to be coded. For prediction mode 2, the attribute prediction value of the point to be coded is the attribute value of the second nearest neighbor of the point to be coded. For prediction mode 3, the attribute prediction value of the point to be coded is the attribute value of the third nearest neighbor of the point to be coded. Exemplarily, when encoding the attribute value of point P0 in Figure 6, the prediction variable index of the attribute value of the nearest neighbor point P2 is set to 1; the attribute prediction variable indexes of the second nearest neighbor point P7 and the third nearest neighbor point P10 are set to 2 and 3 respectively; and the prediction variable index of the weighted average value of points P2, P7 and P10 is set to 0.

Table 1

In some embodiments, the weighted average for prediction mode 0 can be expressed by formula (1) and formula (2):

In formula (1) and formula (2), represents the attribute prediction value of the current point i, j represents the index of the three neighboring points (also called neighboring points), represents the attribute reconstruction value of the neighboring point i, represents the spatial geometric weight from the neighboring point j to the current point i, x _i , y _ij and z _ij represent the geometric position coordinates of the neighboring point j, and x _i , y _i and z _i represent the geometric position coordinates of the current point i.

Step 3: Attribute prediction residual and quantification:

In some embodiments, the attribute prediction value of the current point i is obtained through steps 1 and 2. (k is the total number of points in the point cloud). Let (a _i ) _i∈0…k-1 be the original attribute value of the current point, then the attribute prediction residual ( _ri ) _i∈0…k-1 can be expressed by formula (3):

In formula (3), Represents the attribute prediction value of the current point i, _ai represents the original attribute value of the current point i, and _ri represents the attribute prediction residual of the current point i.

In some embodiments, the attribute prediction residual of the current point i is quantized by formula (4):

In formula (4), _ri represents the attribute prediction residual of the current point i, Qs represents the quantization step (Qs), and _Qi represents the quantized attribute prediction residual of the current point i. The quantization step Qs can be calculated using the quantization parameter (QP) specified by the connectionless transport protocol (CTC).

Related technology 3:

The G-PCC software platform uses a LoD construction method based on Euclidean distance. However, for point cloud sequences (Adaptive Multi-resolution Fused Point Cloud Sequences, Am-fused point cloud sequences) with adaptive multi-resolution characteristics and fused processing containing multiple attributes (color, reflectivity), if one attribute of the Am-fused point cloud sequence has been encoded, the prediction of the other attribute can be guided by the already encoded attribute. For example, if the reflectivity values of two points in the reconstructed point cloud are very different, it should be assumed that the color information of the two points is also very different. Based on this idea, G-PCC modified the LoD construction method in the multi-attribute point cloud. When encoding the second attribute, the distance calculation formula of LoD can be expressed by formula (5) and formula (6):
overrallDis＝geomW×geomDis+attrW×attrDis (5)
attrW＝λ×maxGeom/maxAttr (6)

In formulas (5) and (6), maxGeom represents the sum of the length, width, and height of the bounding box of the block, maxAttr represents the maximum value of the encoded attribute, λ represents a parameter that balances the importance of geometry and attributes, the value of attrDis is the difference in the encoded attribute values between the current point and the predicted point, geomDis represents the Euclidean distance between the current point and the predicted point, and geomW is set to 1.

In the current G-PCC attribute coding framework, intra-frame prediction and inter-frame prediction are widely introduced to remove the temporal and spatial redundancy of point cloud coding. Inter-chrominance prediction is also introduced to remove the redundancy between the chrominance blue component (Cb) and the chrominance red component (Cr). However, when G-PCC encodes an Am-fused point cloud sequence with multiple attributes (color, reflectivity), the redundancy between the two attributes has not been fully mined and removed. Although there are related technologies to mine The correlation information between attributes is mined, but it only uses the encoded attribute information to guide the encoding of another attribute. When one attribute has been encoded and another attribute is encoded, there is still a lot of redundancy between the attributes, which will inevitably cause a waste of bit rate.

Based on this, on the first aspect, an embodiment of the present application provides a decoding method. On the one hand, by determining the attribute prediction value of the attribute to be decoded of the current point through the correlation coefficient of the target cross-attribute prediction mode and the target neighboring points, the attribute value of the current point can be predicted more accurately, avoiding the transmission of redundant information. This can reduce the amount of data that needs to be transmitted during encoding, thereby reducing the waste of bit rate. On the one hand, in the process of predicting the attribute reconstruction value of the attribute to be decoded of the current point, it is determined based on the correlation coefficient of the target neighboring points of the current point and the attribute reference value of the attribute to be decoded of the current point, which can improve the utilization of the bit rate, avoid the waste of bit rate, reduce the redundancy and cost during data transmission, and thus improve the efficiency and performance of decoding.

The embodiments of the present application will be described in detail below with reference to the accompanying drawings.

In one embodiment of the present application, FIG8 is a flowchart of a decoding method provided by the embodiment of the present application. As shown in FIG8 , the method may include S101 to S105:

S101: Decode a bitstream and determine the first syntax element information of the current point.

It should be noted that the decoding method of the embodiment of the present application is applied to the decoder. In addition, the decoding method can specifically refer to a method for cross-attribute prediction of lidar point clouds. Among them, in the attribute prediction mode, this is mainly aimed at improving a PT encoding method for single-neighbor cross-attribute prediction to avoid the problem of a large amount of redundancy between the two attribute information when one attribute information has been encoded and another attribute information is encoded in the related art.

In the embodiment of the present application, the current point is also called the current node, the current point to be decoded, the point to be decoded, the current point to be detected, the node to be decoded, etc., and the embodiment of the present application does not impose any limitation on this.

In the embodiment of the present application, the first syntax element information is used to indicate the best prediction mode for the current point, wherein the best prediction mode for the current point can be a cross-attribute prediction mode or a non-cross-attribute prediction mode.

In the embodiment of the present application, the value of the first syntax element information can be in parameter form or in digital form. Specifically, the first syntax element information can be a parameter written in the profile or a flag value, which is not specifically limited here.

Exemplarily, if the value of the first syntax element information is 0, the best prediction mode of the current point is determined to be prediction mode 0; if the value of the first syntax element information is 1, the best prediction mode of the current point is determined to be prediction mode 1.

S102: Determine, according to the value of the first syntax element information, the best prediction mode at the current point from the candidate prediction modes as the target cross-attribute prediction mode.

In the embodiment of the present application, the candidate prediction mode includes at least one or more candidate cross-attribute prediction modes. Here, the candidate cross-attribute prediction mode is also referred to as a cross-attribute prediction mode.

In the embodiment of the present application, the target cross-attribute prediction mode is the best prediction mode among one or more candidate cross-attribute prediction modes.

Exemplarily, the candidate prediction modes may include three candidate cross-attribute prediction modes, prediction mode 1, prediction mode 2, and prediction mode 3. When the value of the first syntax element information is 1, prediction mode 1 is used as the target cross-attribute prediction mode. When the value of the first syntax element information is 2, prediction mode 2 is used as the target cross-attribute prediction mode. When the value of the first syntax element information is 3, prediction mode 3 is used as the target cross-attribute prediction mode.

In the embodiment of the present application, the candidate cross-attribute prediction mode refers to predicting the attribute prediction value of the to-be-decoded attribute of the current point by using one or more attribute reconstruction values of the neighboring points of the current point.

The following description is made by taking one or more attribute reconstruction values including a brightness reconstruction value and a reflectivity reconstruction value as an example.

For example, the candidate cross-attribute prediction mode may represent the use of the brightness reconstruction value and reflectivity reconstruction value of the current point's neighboring points to predict the brightness reconstruction value of the current point. Alternatively, the cross-attribute prediction mode may represent the use of the brightness reconstruction value and reflectivity reconstruction value of the current point's neighboring points to predict the reflectivity reconstruction value of the current point.

It should be noted that in this embodiment of the present application, the candidate cross-attribute prediction mode uses the brightness reconstruction value and reflectivity reconstruction value of the current point's neighboring points to predict the brightness reconstruction value or reflectivity reconstruction value of the current point. In actual application scenarios, the brightness reconstruction value and reflectivity reconstruction value are not limited to these values and can also be the reconstruction values of other attributes. This embodiment of the present application does not impose any restrictions on this.

S103 : Determine one or more attribute reconstruction values of target neighboring points of the current point according to the target cross-attribute prediction mode.

In the embodiment of the present application, the target cross-attribute prediction mode corresponds to the target neighbor point, that is, the target neighbor point is the neighbor point corresponding to the target cross-attribute prediction mode among the M neighbor points of the current point.

In an embodiment of the present application, the target cross-attribute prediction mode is related to the target neighbor point. Table 2 is a schematic table 1 of a candidate prediction mode provided in an embodiment of the present application. As shown in Table 2, when the target cross-attribute prediction mode is prediction mode 1, it indicates that the first neighbor point of the current point is used to perform cross-attribute guided prediction to derive the attribute prediction value of the attribute to be decoded of the current point. When the target cross-attribute prediction mode is prediction mode 2, it indicates that the second neighbor point of the current point is used to perform cross-attribute guided prediction to derive the attribute prediction value of the attribute to be decoded of the current point. When the target cross-attribute prediction mode is prediction mode 3, it indicates that the third neighbor point of the current point is used to perform cross-attribute guided prediction to derive the attribute prediction value of the attribute to be decoded of the current point.

Table 2

In the embodiment of the present application, the first, second, and third neighbor points in Table 1 are determined according to the RoD generation order. Assuming that the current point cloud sequence includes the following points: P0, P1, P2, P3, P4, P5, P6, P7, P8, P9, and P10, the distance-based LoD construction is performed on the above 11 points to obtain the LoD sequence: P0, P2, P7, P10, P1, P5, P6, P9, P3, P4, and P8. Assuming that the current point is P0, the first neighbor point of P0 is P2, the second neighbor point of P0 is P7, and the third neighbor point of P0 is P10.

Exemplarily, prediction mode 1 may refer to predicting the brightness reconstruction value of the current point using the brightness reconstruction value and attribute reconstruction value of the first neighboring point of the current point, or prediction mode 1 may refer to predicting the reflectivity reconstruction value of the current point using the brightness reconstruction value and attribute reconstruction value of the first neighboring point of the current point. Prediction mode 2 may refer to predicting the brightness reconstruction value of the current point using the brightness reconstruction value and attribute reconstruction value of the second neighboring point of the current point, or prediction mode 2 may refer to predicting the reflectivity reconstruction value of the current point using the brightness reconstruction value and attribute reconstruction value of the second neighboring point of the current point. Prediction mode 3 may refer to predicting the brightness reconstruction value of the current point using the brightness reconstruction value and attribute reconstruction value of the third neighboring point of the current point, or prediction mode 3 may refer to predicting the reflectivity reconstruction value of the current point using the brightness reconstruction value and attribute reconstruction value of the third neighboring point of the current point.

S104: Determine the correlation coefficient of the target neighboring point based on one or more attribute reconstruction values of the target neighboring point.

In the embodiment of the present application, the correlation coefficients of the target neighbor points have the following three cases:

Case 1: The correlation coefficient of the target neighboring points represents the correlation between the reconstructed values of multiple attributes of the target neighboring points.

For example, the multiple attribute reconstruction values of the target neighboring point may include: a brightness reconstruction value and a reflectivity reconstruction value. In this case, the correlation coefficient of the target neighboring point may represent the correlation between the brightness reconstruction value and the reflectivity reconstruction value of the target neighboring point.

Case 2: The correlation coefficient of the target neighboring point represents the correlation between the attribute reconstruction value of the target neighboring point and the attribute reconstruction value of the current point.

For example, when the attribute reconstruction value of the target neighbor point is a brightness reconstruction value, the correlation coefficient of the target neighbor point can represent the correlation between the brightness reconstruction value of the target neighbor point and the brightness reconstruction value of the current point. Alternatively, when the attribute reconstruction value of the target neighbor point is a reflectivity reconstruction value, the correlation coefficient of the target neighbor point can represent the correlation between the reflectivity reconstruction value of the target neighbor point and the reflectivity reconstruction value of the current point.

Case 3: The correlation coefficient of the target neighbor point represents the correlation between the attribute reconstruction value of the target neighbor point and the attribute reconstruction value of the non-target neighbor point of the current point.

Exemplarily, the non-target neighbor point is any neighbor point other than the target neighbor point among the M neighbor points of the current point. The non-target neighbor point can be the neighbor point closest to the current point other than the target neighbor point among the M neighbor points of the current point.

For example, the correlation coefficient of the target neighbor point can represent the correlation between the brightness reconstructed value of the target neighbor point and the brightness reconstructed value of the non-target neighbor point. Alternatively, the correlation coefficient of the target neighbor point can represent the correlation between the brightness reconstructed value of the target neighbor point and the reflectivity reconstructed value of the non-target neighbor point. Alternatively, the correlation coefficient of the target neighbor point can represent the correlation between the reflectivity reconstructed value of the target neighbor point and the reflectivity reconstructed value of the non-target neighbor point.

S105 : Determine the attribute prediction value of the attribute to be decoded at the current point according to the attribute reference value and the correlation coefficient of the attribute to be decoded at the current point.

In the embodiment of the present application, the attribute to be decoded may be brightness or reflectivity.

In some embodiments of the present application, the attribute reference value of the attribute to be decoded at the current point is multiplied by the correlation coefficient to determine the attribute prediction value of the attribute to be decoded.

In the embodiment of the present application, the implementation after S105 includes S106:

S106 : Determine the attribute reconstruction value of the attribute to be decoded at the current point based on the attribute prediction value of the attribute to be decoded at the current point.

In the embodiment of the present application, the implementation of S106 may include:

Decode the code stream and determine the attribute prediction difference of the attribute to be decoded at the current point;

Determine the attribute reconstruction value of the attribute to be decoded at the current point according to the attribute prediction difference of the attribute to be decoded at the current point.

In the embodiment of the present application, the attribute prediction difference values of the attribute to be decoded at the current point are added to obtain the attribute reconstruction value of the attribute to be decoded at the current point.

The embodiment of the present application provides a decoding method, which includes: decoding a code stream, determining the first syntax element information of the current point; determining the best prediction mode adopted by the current point from the candidate prediction modes as the target cross-attribute prediction mode according to the value of the first syntax element information; determining one or more attribute reconstruction values of the target neighboring points of the current point according to the target cross-attribute prediction mode; determining the correlation coefficient of the target neighboring points according to the one or more attribute reconstruction values of the target neighboring points; determining the attribute reference value and the correlation coefficient of the attribute to be decoded of the current point according to ... The predicted attribute value of the attribute to be decoded at the current point is determined based on the predicted attribute value. The reconstructed attribute value of the attribute to be decoded at the current point is determined based on the predicted attribute value. The predicted attribute value of the attribute to be decoded at the current point is determined based on the attribute reference value and the correlation coefficient. The predicted attribute value of the attribute to be decoded is calculated based on the correlation information of the current point's neighboring points and the known attribute values. It serves as the predicted value of the attribute at the current point. The correlation coefficient helps measure the degree of correlation between the attribute to be decoded and the known attributes. A high correlation coefficient indicates a strong linear relationship between the two, and the predicted value more accurately reflects the actual value of the attribute to be decoded. Using the correlation coefficient for prediction avoids unnecessary data transmission and storage. If the correlation between the attribute to be decoded and the known attributes is low, the predicted value will be closer to the actual value of the attribute to be decoded, reducing the transmission and storage of redundant data. This improves bitrate utilization, avoids bitrate waste, and ultimately improves decoding performance.

The following describes how to perform cross-attribute prediction on the attribute prediction value of the attribute to be decoded at the current point in the three cases of correlation coefficients of the target neighbor points mentioned above. The details are divided into the following three points.

1. The correlation coefficient in case 1 represents the correlation between the reconstructed values of multiple attributes of the target neighboring points.

In some embodiments of the present application, the correlation coefficient includes a first coefficient; and the implementation of determining the correlation coefficient of the target neighbor point based on one or more attribute reconstruction values of the target neighbor point in S104 may include:

A first coefficient is determined according to a first attribute reconstruction value of the target neighboring point and a second attribute reconstruction value of the target neighboring point.

In an embodiment of the present application, in situation 1, the attribute corresponding to the first attribute reconstruction value of the target neighbor point is different from the attribute corresponding to the second attribute reconstruction value of the target neighbor point, and the attribute corresponding to the first attribute reconstruction value is correlated with the attribute corresponding to the second attribute reconstruction value.

It should be noted that the attribute corresponding to the first attribute reconstruction value and the attribute corresponding to the second attribute reconstruction value are different and correlated, meaning that the first attribute reconstruction value and the second attribute reconstruction value are reconstruction values of two different attributes (the first attribute and the second attribute), but the first attribute and the second attribute are correlated. For example, the first attribute can be brightness and the second attribute can be reflectivity.

Here, the correlation between brightness and reflectivity is explained. In terms of the optical properties of an object, brightness generally refers to the intensity or brightness of light perceived by the human eye. Reflectivity, on the other hand, characterizes the degree of light reflection from an object's surface, that is, the relative intensity of light reflected from the surface. Generally speaking, higher reflectivity results in higher brightness. According to optical theory, an object's brightness and reflectivity properties are strongly correlated. Statistics show that the reflectivity and brightness information of most points in the Am-fused point cloud also have a strong correlation, especially for neighboring points, where the correlation is essentially the same. Therefore, using the encoded attribute information of the current point to predict the unencoded attribute value can reduce residual errors and remove redundancy. For example, as shown in Figure 9, the brightness information (luma) of the current point cloud has been encoded. The current point (the point to be predicted) is P0, and its three neighboring points are P1, P2, and P3. If the target neighbor point of the current point is P1, a linear model (first coefficient) is established using the relationship between the brightness reconstruction value of P1 (luma=40) and the reflectivity reconstruction value of P1 (ref=40) as the correlation between the brightness information and the reflectivity information of the current point P0, thereby using the brightness reconstruction value of P0 and the first coefficient of P1 to predict the attribute prediction value of the attribute to be decoded (reflectivity) of the current point, or, using the reflectivity reconstruction value of P0 and the first coefficient of P1 to predict the attribute prediction value of the attribute to be decoded (brightness) of the current point.

It can be understood that the first coefficient reflects the degree of correlation between the different attributes of the target's neighboring points. If the first coefficient is close to 1, it indicates a strong positive correlation between the first and second attributes; if it is close to 0, it indicates almost no correlation between the two. This helps to assess the correlation between attributes and better understand the characteristics and patterns of the data.

In some embodiments of the present application, the first coefficient includes a ratio of a first attribute reconstruction value of the target neighbor point to a second attribute reconstruction value of the target neighbor point.

In the embodiment of the present application, the attribute corresponding to the first attribute reconstruction value and the attribute corresponding to the second attribute reconstruction are related to the attribute coding order of the current node.

In the embodiment of the present application, taking the first attribute as brightness (luma) and the second attribute as reflectivity (ref) as an example, the first coefficient includes the following two cases:

(1) When the attribute reconstruction order of the current point is that the first attribute precedes the second attribute, the attribute to be decoded is the second attribute, the first attribute reconstruction value of the target neighbor point is the reconstruction value of the second attribute of the target neighbor point, the second attribute reconstruction value of the target neighbor point is the reconstruction value of the first attribute of the target neighbor point, and the attribute reference value of the attribute to be decoded is the reconstruction value of the first attribute of the current point.

In an embodiment of the present application, when the attribute reconstruction order of the current point is brightness before reflectivity, the attribute to be decoded of the current node is reflectivity, the first attribute reconstruction value of the target neighbor point is the reflectivity reconstruction value of the target neighbor point, the second attribute reconstruction value of the target neighbor point is the brightness reconstruction value of the target neighbor point, the attribute reference value of the attribute to be decoded at the current point is the brightness reconstruction value of the current point, and the attribute prediction value of the attribute to be decoded at the current point is the reflectivity prediction value.

For example, the reflectivity prediction value of the current point can be determined according to formula (7) and formula (8):

In formula (7) and formula (8), Coeff _ref represents the reflectivity prediction value of the current point (the attribute prediction value of the attribute to be decoded), Coeff _luma represents the brightness reconstruction value of the current point (the attribute reference value of the attribute to be decoded), and _si represents the target neighbor point (the i-th neighbor of the current point). The first coefficient of the nearest neighbor point, Represents the reflectivity reconstruction value of the target neighboring point (i.e., the first attribute reconstruction value of the target neighboring point), Represents the brightness reconstruction value of the target neighboring point (that is, the second attribute reconstruction value of the target neighboring point).

(2) When the attribute reconstruction order of the current point is that the second attribute precedes the first attribute, the attribute to be decoded is the first attribute, the first attribute reconstruction value of the target neighbor point is the reconstruction value of the first attribute of the target neighbor point, the second attribute reconstruction value of the target neighbor point is the reconstruction value of the second attribute of the target neighbor point, and the attribute reference value of the attribute to be decoded is the reconstruction value of the second attribute of the current point.

In an embodiment of the present application, when the attribute reconstruction order of the current point is that reflection precedes brightness, the attribute to be decoded of the current node is brightness, the first attribute reconstruction value of the target neighbor point is the brightness reconstruction value of the target neighbor point, the second attribute reconstruction value of the target neighbor point is the reflectivity reconstruction value of the target neighbor point, the attribute reference value of the attribute to be decoded at the current point is the reflectivity reconstruction value of the current point, and the attribute prediction value of the attribute to be decoded at the current point is the brightness prediction value.

For example, the brightness prediction value of the current point can be determined according to formula (9) and formula (10):

In formula (9) and formula (10), Coeff _ref represents the reflectivity reconstruction value of the current point (the attribute reference value of the attribute to be decoded), Coeff _luma represents the brightness prediction value of the current point (the attribute prediction value of the attribute to be decoded), and _si represents the first coefficient of the target neighbor point (the i-th neighbor point of the current point). Represents the reflectivity reconstruction value of the target neighboring point (the second attribute reconstruction value of the target neighboring point), Represents the brightness reconstruction value of the target neighboring point (that is, the first attribute reconstruction value of the target neighboring point).

It can be understood that, on the one hand, the correlation between the first attribute reconstruction value and the second attribute reconstruction value of the target neighbor point can be incorporated into the prediction process through the first coefficient. If the first coefficient is large, it indicates that there is a strong correlation between the two attributes. Then, when predicting the attribute to be decoded at the current point, the relationship between the first attribute and the second attribute can be more accurately utilized to improve the accuracy of the prediction. On the other hand, combining the first coefficient and the attribute reference value can obtain a more accurate attribute prediction value of the attribute to be decoded. This prediction method based on correlation information can avoid unnecessary errors and improve the accuracy of the decoding process. On the other hand, the attribute prediction value calculation process based on the first coefficient and the attribute reference value is relatively simple and accurate, does not require excessive computing resources, can reduce redundant information in the data transmission process, and improve the efficiency of data transmission. Especially in the case of limited bandwidth or high transmission costs, the effective use of correlation information can save transmission data resources.

2. For case 2, the correlation coefficient represents the correlation between the attribute reconstruction value of the target neighbor point and the attribute reconstruction value of the current point.

In some embodiments of the present application, the correlation coefficient includes a second coefficient; and the implementation of determining the correlation coefficient of the target neighbor point based on one or more attribute reconstruction values of the target neighbor point in S104 may include:

The second coefficient is determined according to the first attribute reconstruction value of the current point and the first attribute reconstruction value of the target neighboring point.

In the embodiment of the present application, in case 2, the attribute corresponding to the first attribute reconstruction value of the target neighbor point is the same as the attribute corresponding to the attribute reconstruction value of the current point.

In some embodiments of the present application, the second coefficient includes a ratio of a first attribute reconstruction value of the current point to a first attribute reconstruction value of a target neighboring point.

In the embodiment of the present application, the attribute corresponding to the first attribute reconstruction value is related to the attribute coding order of the current point.

In the embodiment of the present application, taking the first attribute as brightness (luma) and the second attribute as reflectivity (ref) as an example, the second coefficient includes the following two cases:

(1) When the attribute reconstruction order of the current point is that the first attribute precedes the second attribute, the attribute to be decoded is the second attribute, the first attribute reconstruction value of the current point is the reconstruction value of the first attribute of the current point, the first attribute reconstruction value of the target neighboring point is the reconstruction value of the first attribute of the target neighboring point, and the attribute reference value of the attribute to be decoded is the reconstruction value of the second attribute of the target neighboring point.

In an embodiment of the present application, when the attribute reconstruction order of the current point is brightness before reflectivity, the attribute to be decoded of the current node is reflectivity, the first attribute reconstruction value of the current point is the brightness reconstruction value of the current point, the first attribute reconstruction value of the target neighboring point is the brightness reconstruction value of the target neighboring point, the attribute reference value of the attribute to be decoded at the current point is the reflectivity reconstruction value of the target neighboring point, and the attribute prediction value of the attribute to be decoded at the current point is the reflectivity prediction value.

For example, the brightness prediction value of the current point can be determined according to formula (11) and formula (12):

In formula (11) and formula (12), Coeff _ref represents the reflectivity prediction value of the current point (the attribute prediction value of the attribute to be decoded), Coeff _luma represents the brightness reconstruction value of the current point (the first attribute reconstruction value of the current point), _si represents the second coefficient of the target neighbor point (the i-th neighbor point of the current point), Represents the reflectivity reconstruction value of the target neighbor point (the attribute reference value of the attribute to be decoded), Represents the brightness reconstruction value of the target neighboring point (the first attribute reconstruction value of the target neighboring point).

(2) When the attribute reconstruction order of the current point is that the second attribute precedes the first attribute, the attribute to be decoded is the first attribute, the first attribute reconstruction value of the current point is the reconstruction value of the second attribute of the current point, the first attribute reconstruction value of the target neighboring point is the reconstruction value of the second attribute of the target neighboring point, and the attribute reference value of the attribute to be decoded is the reconstruction value of the first attribute of the target neighboring point.

In an embodiment of the present application, when the attribute reconstruction order of the current point is that reflection precedes brightness, the attribute to be decoded of the current node is brightness, the first attribute reconstruction value of the current point is the reflectivity reconstruction value of the current point, the first attribute reconstruction value of the target neighboring point is the reflectivity reconstruction value of the target neighboring point, the attribute reference value of the attribute to be decoded at the current point is the brightness reconstruction value of the target neighboring point, and the attribute prediction value of the attribute to be decoded at the current point is the brightness prediction value.

For example, the brightness prediction value of the current point can be determined according to formula (13) and formula (14):

In formula (13) and formula (14), Coeff _ref represents the reflectivity reconstruction value of the current point (the first attribute reconstruction value of the current point), Coeff _luma represents the brightness prediction value of the current point (the attribute reference value of the attribute to be decoded), and _si represents the second coefficient of the target neighbor point (the i-th neighbor point of the current point). Represents the reflectivity reconstruction value of the target neighboring point (the first attribute reconstruction value of the target neighboring point), Represents the brightness reconstruction value of the target neighbor point (the attribute reference value of the attribute to be decoded).

It can be understood that the second coefficient takes into account the ratio between the first attribute reconstruction value of the current point and the first attribute reconstruction value of the target neighboring point. This relationship can help consider the correlation between the attributes of the current point and the attributes of the target neighboring point, thereby more accurately predicting the attribute value of the current point. Combining the second coefficient and the attribute reference value, a more accurate attribute prediction value of the attribute to be decoded can be obtained. The consideration of the second coefficient makes the prediction process more targeted, can better reflect the attribute relationship between the current point and the target neighboring point, and improve the accuracy of the prediction. Accurate attribute prediction values can reduce unnecessary data transmission and save transmission resources. By considering the second coefficient, the correlation between attributes can be more effectively utilized, redundant information in the transmission process can be reduced, and the efficiency of data transmission can be improved.

3. The correlation coefficient in case 2 represents the correlation between the attribute reconstruction values of the target neighboring points and the attribute reconstruction values of the non-target neighboring points of the current point.

In some embodiments of the present application, the correlation coefficient includes a third coefficient; and the implementation of determining the correlation coefficient of the target neighbor point based on one or more attribute reconstruction values of the target neighbor point in S104 may include:

The third coefficient is determined according to the first attribute reconstructed value of the non-target neighbor point and the first attribute reconstructed value of the target neighbor point.

In the embodiment of the present application, in case 3, the attribute corresponding to the first attribute reconstruction value of the target neighbor point is the same as the attribute corresponding to the attribute reconstruction value of the non-target neighbor point.

In an embodiment of the present application, the non-target neighbor point is any neighbor point among the M neighbor points of the current point except the target neighbor point. For example, the non-target neighbor point can be the neighbor point closest to the current point among the M neighbor points of the current point except the target neighbor point, or the non-target neighbor point can be the neighbor point closest to the target neighbor point among the M neighbor points of the current point except the target neighbor point. The present application does not impose any restrictions on this.

In some embodiments of the present application, the third coefficient includes a ratio of the first attribute reconstructed value of the target neighboring point to the first attribute reconstructed value of the non-target neighboring point.

In the embodiment of the present application, the attribute corresponding to the first attribute reconstruction value is related to the attribute coding order of the current point.

In the embodiment of the present application, taking the first attribute as brightness (luma) and the second attribute as reflectivity (ref) as an example, the third coefficient includes the following two cases:

(1) When the attribute reconstruction order of the current point is that the first attribute precedes the second attribute, the attribute to be decoded is the second attribute, the first attribute reconstruction value of the non-target neighbor point is the reconstruction value of the first attribute of the non-target neighbor point, the first attribute reconstruction value of the target neighbor point is the reconstruction value of the first attribute of the target neighbor point, and the attribute reference value of the attribute to be decoded is the reconstruction value of the second attribute of the target neighbor point.

In an embodiment of the present application, when the attribute reconstruction order of the current point is brightness before reflectivity, the attribute to be decoded of the current node is reflectivity, the first attribute reconstruction value of the non-target neighbor point is the brightness reconstruction value of the current point, the first attribute reconstruction value of the target neighbor point is the brightness reconstruction value of the target neighbor point, the attribute reference value of the attribute to be decoded at the current point is the reflectivity reconstruction value of the target neighbor point, and the attribute prediction value of the attribute to be decoded at the current point is the reflectivity prediction value.

For example, the brightness prediction value of the current point can be determined according to formula (15) and formula (16):

In formula (15) and formula (16), Coeff _ref represents the reflectivity prediction value of the current point (the attribute prediction value of the attribute to be decoded), _{coeff_lumap} represents the brightness reconstruction value of the non-target neighbor point (the pth neighbor point of the current point) (the first attribute reconstruction value of the non-target neighbor point), _si represents the third coefficient of the target neighbor point (the i-th neighbor point of the current point), Represents the reflectivity reconstruction value of the target neighbor point (the attribute reference value of the attribute to be decoded), Represents the brightness reconstruction value of the target neighbor point (the first attribute of the target neighbor point Rebuild value).

(2) When the attribute reconstruction order of the current point is that the second attribute precedes the first attribute, the attribute to be decoded is the first attribute, the first attribute reconstruction value of the non-target neighbor point is the reconstruction value of the second attribute of the non-target neighbor point, the first attribute reconstruction value of the target neighbor point is the reconstruction value of the second attribute of the target neighbor point, and the attribute reference value of the attribute to be decoded is the reconstruction value of the first attribute of the target neighbor point.

In an embodiment of the present application, when the attribute reconstruction order of the current point is that reflection precedes brightness, the attribute to be decoded of the current node is brightness, the first attribute reconstruction value of the current point is the reflectivity reconstruction value of the current point, the first attribute reconstruction value of the target neighboring point is the reflectivity reconstruction value of the target neighboring point, the attribute reference value of the attribute to be decoded at the current point is the brightness reconstruction value of the target neighboring point, and the attribute prediction value of the attribute to be decoded at the current point is the brightness prediction value.

For example, the brightness prediction value of the current point can be determined according to formula (17) and formula (18):

In formula (17) and formula (18), represents the reflectivity reconstruction value of the non-target neighboring point (the pth neighboring point of the current point) (the first attribute reconstruction value of the non-target neighboring point), Coeff _luma represents the brightness prediction value of the current point (the attribute reference value of the attribute to be decoded), _si represents the third coefficient of the target neighboring point (the i-th neighboring point of the current point), Represents the reflectivity reconstruction value of the target neighboring point (the first attribute reconstruction value of the target neighboring point), Represents the brightness reconstruction value of the target neighboring point (the first attribute reconstruction value of the target neighboring point).

It is understandable that the third coefficient takes into account the ratio relationship between the first attribute reconstruction value of the target neighbor point and the first attribute reconstruction value of the non-target neighbor point. This ratio relationship can help evaluate the attribute correlation between the target neighbor point and the non-target neighbor point, so as to better understand the characteristics and laws of the data. Combining the third coefficient and the attribute reference value, a more accurate attribute prediction value of the attribute to be decoded can be obtained. The consideration of the third coefficient makes the prediction process more comprehensive and comprehensive, which can better reflect the attribute relationship between the current point and the target neighbor point and the non-target neighbor point, and improve the accuracy of the prediction. Accurate attribute prediction values can reduce unnecessary data transmission and save transmission resources. By considering the third coefficient, the attribute correlation between the target neighbor point and the non-target neighbor point can be more effectively utilized, the redundant information in the transmission process can be reduced, and the efficiency of data transmission can be improved.

In some embodiments of the present application, the decoding method further includes:

Parsing the code stream to determine the second syntax element information;

If the value of the second syntax element information is the first value, then determining the attribute reconstruction order of the current point is that the first attribute precedes the second attribute; or

If the value of the second syntax element information is the second value, it is determined that the attribute reconstruction order of the current point is that the second attribute precedes the first attribute.

In the embodiment of the present application, the second syntax element information is used to indicate the attribute reconstruction order of the current point.

Exemplarily, the second syntax element information may be a flag bit in the attribute parameter APS, and the second syntax element information may be expressed as muti_crosstype_pre, which is used to indicate the attribute coding order of the current point.

It should be noted that in the embodiment of the present application, the first value and the second value are different, and the first value and the second value can be in parameter form or in digital form. Specifically, the second syntax identification information can be a parameter written in the profile or a flag value, which is not specifically limited here.

Exemplarily, for the first value and the second value, the first value can be set to 1 and the second value can be set to 0; or, the first value can be set to 0 and the second value can be set to 1; or, the first value can be set to true and the second value can be set to false; or, the first value can be set to false and the second value can be set to true; but this is not specifically limited here.

In an embodiment of the present application, taking the flag written into the code stream as an example, assuming that the first value is set to 1 (true) and the second value is set to 0 (false), at this time if the value of the second syntax identification information is 1 (true), then it can be determined that the attribute reconstruction order of the current point is that the first attribute (brightness) precedes the second attribute (reflectivity), and it is necessary to use the correlation coefficient of the target neighbor node and the brightness reconstruction value of the current point to predict the reflectivity prediction value of the current point; if the value of the second syntax identification information is 0 (false), then it can be determined that the attribute reconstruction order of the current point is that the second attribute (reflectivity) precedes the first attribute (brightness), and it is necessary to use the correlation coefficient of the target neighbor node and the reflectivity reconstruction value of the current point to predict the brightness prediction value of the current point.

In some embodiments of the present application, the candidate prediction mode further includes the first prediction mode, and the decoding method further includes S107:

S107 : Determine, according to the value of the first syntax element information, the best prediction mode at the current point from the candidate prediction modes as the first prediction mode.

In the embodiment of the present application, the first prediction mode is a non-cross-attribute prediction mode.

In this embodiment of the present application, the first prediction mode is associated with the reconstructed attribute values of the M neighboring points of the current point. For example, the first prediction mode may represent a weighted average of the reconstructed attribute values of the M neighboring points of the current node. The reconstructed attributes of the M neighboring points are identical to the attribute to be decoded of the current point.

In the embodiment of the present application, candidate prediction modes can be divided into two cases:

Case 1: The candidate prediction modes include: one or more cross-attribute prediction modes.

In an embodiment of the present application, the candidate prediction modes are shown in Table 2, and one or more cross-attribute prediction modes may include: prediction mode 1, prediction mode 2, and prediction mode 3. Among them, when the target cross-attribute prediction mode is prediction mode 1, it means that the first neighboring point of the current point is used to perform cross-attribute guided prediction to derive the attribute prediction value of the attribute to be decoded of the current point. When the target cross-attribute prediction mode is prediction mode 2, it means that the second neighboring point of the current point is used to perform cross-attribute guided prediction to derive the attribute prediction value of the attribute to be decoded of the current point. When the target cross-attribute prediction mode is prediction mode 3, it means that the third neighboring point of the current point is used to perform cross-attribute guided prediction to derive the attribute prediction value of the attribute to be decoded of the current point.

Case 2: the candidate prediction modes include: one or more cross-attribute prediction modes and one or more non-cross-attribute prediction modes.

In an embodiment of the present application, Table 3 is a schematic table 2 of a candidate prediction mode provided in an embodiment of the present application. As shown in Table 3, the candidate prediction modes include: 3 cross-attribute prediction modes and 1 non-cross-attribute prediction mode (prediction mode 0). When the target cross-attribute prediction mode is prediction mode 0, it means that the attribute reconstruction values of the 3 neighboring points of the current point are used to derive the attribute prediction value of the attribute to be decoded of the current point. Exemplarily, the weighted average value of the brightness reconstruction values of the 3 neighboring points of the current point is used as the brightness prediction value of the current point, or the weighted average value of the reflectivity reconstruction values of the 3 neighboring points of the current point is used as the reflectivity prediction value of the current point.

Table 3

In some embodiments of the present application, as shown in FIG10 , the decoding method further includes S201 to S203:

S201, determining whether the current point meets a preset condition based on the attribute reconstruction values of the reconstructed attributes of the M neighboring points of the current point;

S202: When the reconstructed attribute values of the respective reconstructed attributes of the M neighboring points of the current point meet a preset condition, perform decoding of the code stream to determine the first syntax element information of the current point.

S203 : When the reconstructed attribute values of the respective reconstructed attributes of the M neighboring points of the current point do not meet a preset condition, determine that the current point adopts the second prediction mode.

In the embodiment of the present application, the preset condition is specified by both the encoder and the decoder, and both the encoder and the decoder need to execute the judgment step S201.

In the embodiment of the present application, the reconstructed attribute is the same as the attribute to be decoded.

In some embodiments of the present application, M is a positive integer greater than or equal to 2; the preset condition includes: the maximum attribute difference between the reconstructed attributes of the M neighboring points is greater than or equal to a preset threshold.

In the embodiments of the present application, the preset threshold is a value specified by both the decoder and the encoder, or the preset threshold is set by the encoder, the encoder writes the preset threshold into the bitstream, and the decoder obtains the preset threshold after decoding the bitstream. This application does not impose any restrictions on the method for setting the preset threshold.

In an embodiment of the present application, the preset threshold is an adaptive threshold, for example, it can be adaptively adjusted according to relevant information of the current point cloud (size, attribute complexity, number of points, etc.), and the preset threshold can be expressed as adaptive_threshold.

Exemplarily, taking the reconstructed attribute and the attribute to be decoded as brightness as an example, it is assumed that the neighboring points of the current point include: neighboring point 1, neighboring point 2, and neighboring point 3. The attribute reconstruction values of the reconstructed attributes of the three neighboring points of the current point include: brightness reconstruction luma1 of neighboring point 1, brightness reconstruction luma2 of neighboring point 2, and brightness reconstruction luma3 of neighboring point 3. The attribute reconstruction differences between the three neighboring points of the current point include: |luma1-luma2|, |luma1-luma3|, |luma2-luma3|. If |luma1-luma3|>|luma1-luma2|>|luma2-luma3|, then |luma1-luma3| is used as the maximum attribute difference. If the maximum attribute difference is greater than the preset threshold, the decoder executes the step of decoding the code stream in S101 to determine the first syntax element information of the current point. If the maximum attribute difference is less than the preset threshold, it is determined that the current point adopts the second prediction mode.

In an embodiment of the present application, the second prediction mode is the same as or different from the first prediction mode. When the second prediction mode is the same as the first prediction mode, the second prediction mode predicts the attribute prediction value of the attribute to be decoded at the current point using the attribute reconstruction values of the reconstructed attributes of the M neighboring points of the current point. Exemplarily, the second prediction mode predicts the attribute prediction value of the attribute to be decoded at the current point using the weighted average reconstruction value of the attribute reconstruction values of the reconstructed attributes of the M neighboring points of the current point.

It should be noted that S202 and S203 are parallel implementation steps, and either S202 or S203 can be executed, that is, the decoder can execute S202 or S203, and this application does not limit this.

It can be understood that, on the one hand, by judging whether the attribute reconstruction values of the M neighboring points of the current point meet the preset conditions, data decision optimization can be performed. If the conditions are met, the decoding code stream process is executed, which can more accurately determine the first syntax element information of the current point and improve the accuracy and efficiency of decoding; if the conditions are not met, the second prediction mode is selected, making the data processing more intelligent and more adaptable. Strong. On the other hand, judging based on preset conditions can avoid decoding data that does not meet the conditions, saving computing resources and time during the decoding process, helping to improve resource utilization efficiency and making the decoding process more efficient. Data processing and decision-making based on preset conditions can ensure data quality and integrity. Decoding only data that meets the conditions or selecting the second prediction mode helps avoid unnecessary data processing errors or misjudgments, and improves the accuracy and reliability of data processing. Furthermore, the application of preset conditions can make system operation more intelligent and efficient. Flexible selection of decoding or prediction modes based on specific conditions helps improve system efficiency and meet different data processing requirements.

It can be understood that, based on the attribute reconstruction values of the reconstructed attributes of the M neighboring points of the current point, the preset conditions are judged and the decoding code stream is executed or the second prediction mode is selected. This can produce beneficial effects in terms of data decision optimization, resource utilization efficiency improvement, data transmission cost reduction, data quality assurance and system operation efficiency improvement, which is of great significance to the efficiency and accuracy in the data encoding and decoding and transmission process.

In some embodiments of the present application, the decoding method further includes:

When it is determined that the current point adopts the second prediction mode, determining the spatial geometric weights of the M neighboring points according to their respective spatial positions and the spatial position of the current point;

The attribute prediction value of the attribute to be decoded is determined according to the attribute reconstruction value of each of the reconstructed attributes of the M neighboring points and the spatial geometric weights of each of the M neighboring points.

In the embodiment of the present application, the process of determining the attribute prediction value of the attribute to be decoded using the second prediction mode can be expressed by formula (19) and formula (20):

In formula (19) and formula (20), represents the attribute prediction value of the attribute to be decoded at the current point i, j represents the index of M neighboring points, represents the attribute reconstruction value of the reconstructed attribute of the neighbor point i among the M neighbor points, represents the spatial geometric weight of the neighbor point j to the current point i among the M neighbor points, x _i , y _ij and z _ij represent the geometric position coordinates of the neighbor point j, and x _i , y _i and z _i represent the geometric position coordinates (spatial position) of the current point i.

It can be understood that by considering the spatial positions of the M nearest neighbors and the current point to determine the spatial geometric weight, we can more fully utilize spatial information, help reflect the spatial correlation between data, and improve the accuracy and reliability of data prediction. Combining spatial geometric weights with attribute reconstruction values can better assess the correlation between the attributes of the M nearest neighbors and the current point. This optimization can make the prediction process more comprehensive and holistic, improving the accuracy and stability of predictions.

In some embodiments of the present application, the decoding method further includes:

Decoding the code stream to determine third syntax element information;

In a case where the third syntax element information indicates that the current point allows the use of the cross-attribute prediction mode, determining that the candidate prediction modes include one or more cross-attribute prediction modes; or

In a case where the third syntax element information indicates that the current point does not allow the cross-attribute prediction mode to be adopted, determining that the candidate prediction modes include one or more non-cross-attribute prediction modes;

According to the value of the first syntax element information, a target prediction mode of the current point is determined from one or more non-cross-attribute prediction modes.

In the embodiment of the present application, the third syntax element information is used to indicate whether the cross-attribute prediction mode is allowed at the current point.

In the embodiment of the present application, the candidate prediction mode is related to whether the current point indicated by the third syntax element allows the use of the cross-attribute prediction mode, specifically including the following two cases:

Case 1: The third syntax element information indicates that the cross-attribute prediction mode is allowed at the current point.

In an embodiment of the present application, if the current point allows the use of a cross-attribute prediction mode, the candidate prediction modes include at least: one or more cross-attribute prediction modes. Taking the candidate prediction modes shown in Table 3 as an example, the candidate prediction modes of the current point include: prediction mode 0 (first prediction mode), prediction mode 1, prediction mode 2, and prediction mode 3. Among them, prediction mode 0 is a non-cross-attribute prediction mode, and prediction mode 1, prediction mode 2, and prediction mode 3 are cross-attribute prediction modes. The first syntax element information is used to indicate the target prediction mode adopted by the current point, wherein the target prediction mode can be a target cross-attribute prediction mode (any one of prediction modes 1 to 3) or a non-cross-attribute prediction mode (prediction mode 0).

Exemplarily, taking the attribute to be decoded of the current point as brightness as an example, if the value of the first syntax element information is 0, the target prediction mode of the current point is determined to be 0. At this time, the decoder uses the weighted average brightness reconstruction value of the brightness reconstruction values of the three neighboring points of the current point as the brightness prediction value of the current point. If the value of the first syntax element information is 1, the target prediction mode of the current point is determined to be prediction mode 1. At this time, the decoder uses the reflectivity reconstruction value of the current point and the correlation coefficient of the first neighboring point of the current point to determine the brightness prediction value of the current point. If the value of the first syntax element information is 2, the target prediction mode of the current point is determined to be prediction mode 2. At this time, the decoder uses the reflectivity reconstruction value of the current point and the correlation coefficient of the second neighboring point of the current point to determine the brightness prediction value of the current point. If the value of the first syntax element information is 3, the target prediction mode of the current point is determined to be prediction mode 3. At this time, the decoder uses the reflectivity reconstruction value of the current point The correlation coefficient with the third neighboring point of the current point is used to determine the brightness prediction value of the current point.

Case 2: The third syntax element information indicates that the cross-attribute prediction mode is not allowed at the current point.

In this embodiment of the present application, if the current point allows the use of a cross-attribute prediction mode, the candidate prediction modes include at least one or more non-cross-attribute prediction modes. Taking the candidate prediction modes shown in 1 as an example, the candidate prediction modes for the current point include: prediction mode 0, prediction mode 1, prediction mode 2, and prediction mode 3. Among them, prediction modes 0 to 3 are all non-cross-attribute prediction modes.

Exemplarily, taking the attribute to be decoded of the current point as brightness as an example, if the value of the first syntax element information is 0, the target prediction mode of the current point is determined to be 0. At this time, the decoder uses the weighted average brightness reconstruction value of the brightness reconstruction values of the three neighboring points of the current point as the brightness prediction value of the current point. If the value of the first syntax element information is 1, the target prediction mode of the current point is determined to be prediction mode 1. At this time, the decoder uses the brightness reconstruction value of the first neighboring point of the current point to determine the brightness prediction value of the current point. If the value of the first syntax element information is 2, the target prediction mode of the current point is determined to be prediction mode 2. At this time, the decoder uses the brightness reconstruction value of the second neighboring point of the current point to determine the brightness prediction value of the current point. If the value of the first syntax element information is 3, the target prediction mode of the current point is determined to be prediction mode 3. At this time, the decoder uses the brightness reconstruction value of the third neighboring point of the current point to determine the brightness prediction value of the current point.

It can be understood that, on the one hand, according to the value of the first syntax element information, when the cross-attribute prediction mode is allowed, it is determined that the candidate prediction mode includes one or more cross-attribute prediction modes; or, when the cross-attribute prediction mode is not allowed, it is determined that the candidate prediction mode includes one or more non-cross-attribute prediction modes, so that the attribute prediction requirements of the current point can be matched more accurately, and the accuracy and reliability of the prediction can be improved. On the other hand, determining whether the cross-attribute prediction mode or the non-cross-attribute prediction mode is allowed at the current point according to the third syntax element information helps to improve the efficiency of data prediction. Using a suitable prediction mode can make data prediction faster and reduce computing and time costs. Reasonable selection of a prediction mode can reduce unnecessary data transmission and save transmission resources. By matching the target prediction mode, redundant information in the prediction process can be reduced and data transmission efficiency can be optimized. Appropriate prediction mode selection can avoid errors or misjudgments in the prediction process and ensure the accuracy and reliability of data processing.

In some embodiments of the present application, the decoding method further includes:

If the value of the third syntax element information is the third value, it is determined that the cross-attribute prediction mode is allowed at the current point; or

If the value of the third syntax element information is the fourth value, it is determined that the cross-attribute prediction mode is not allowed at the current point.

In the embodiment of the present application, the third syntax element information is used to indicate whether the cross-attribute prediction mode is allowed at the current point.

Exemplarily, the third syntax element information may be a flag in the attribute parameter APS, and the third syntax element information may be expressed as crosstype_enable_flag, which is used to indicate whether the cross-attribute prediction mode is allowed at the current point.

It should be noted that in the embodiment of the present application, the third value is different from the fourth value, and the third value and the fourth value can be in parameter form or in numerical form. Specifically, the third syntax identification information can be a parameter written in the profile or a flag value, which is not specifically limited here.

Exemplarily, for the third value and the fourth value, the third value can be set to 1 and the fourth value can be set to 0; or, the third value can be set to 0 and the fourth value can be set to 1; or, the third value can be set to true and the fourth value can be set to false; or, the third value can be set to false and the fourth value can be set to true; but this is not specifically limited here.

In an embodiment of the present application, taking the flag written into the bitstream as an example, assuming that the third value is set to 1 (true) and the fourth value is set to 0 (false), if the value of the third syntax identification information is 0 (false), then it can be determined that the cross-attribute prediction mode is not allowed at the current point; if the value of the third syntax identification information is 1 (true), then it can be determined that the cross-attribute prediction mode is allowed at the current point.

On the second aspect, an embodiment of the present application provides a coding method. On the one hand, by determining the attribute prediction value of the attribute to be coded of the current point through the correlation coefficient of the candidate cross-attribute prediction mode and the candidate neighboring points, the attribute value of the current point can be predicted more accurately, avoiding the transmission of redundant information. This can reduce the amount of data to be transmitted during coding, thereby reducing the waste of code rate. On the one hand, in the process of predicting the attribute reconstruction value of the attribute to be coded of the current point, it is determined based on the correlation coefficient of the candidate neighboring points of the current point and the attribute reference value of the attribute to be coded of the current point, which can improve the utilization of the code rate, avoid the waste of code rate, reduce the redundancy and cost during data transmission, and thus improve the efficiency and performance of coding.

The embodiments of the present application will be described in detail below with reference to the accompanying drawings.

In one embodiment of the present application, FIG11 is a flow chart of an encoding method provided by the embodiment of the present application. As shown in FIG11 , the method may include S301 to S307:

S301: Determine a candidate cross-attribute prediction mode for the current point from candidate prediction modes.

It should be noted that the encoding method of the embodiment of the present application is applied to an encoder. In addition, the encoding method can specifically refer to a method for cross-attribute prediction of lidar point clouds. Among them, in the attribute prediction mode, this is mainly an improvement of a PT encoding method for single-neighbor cross-attribute prediction, so as to avoid the problem of a large amount of redundancy between the two attribute information when one attribute information has been encoded and another attribute information is encoded in the related art.

In the embodiment of the present application, the current point is also called the current node, the current point to be encoded, the point to be encoded, the current point to be detected, the node to be encoded, etc., and the embodiment of the present application does not impose any limitations on this.

In the embodiment of the present application, the candidate cross-attribute prediction mode is one or more candidate cross-attribute prediction modes in the candidate prediction mode. Here, the candidate cross-attribute prediction mode is also referred to as a cross-attribute prediction mode.

Exemplarily, the candidate prediction mode includes three candidate cross-attribute prediction modes: prediction mode 1, prediction mode 2, and prediction mode 3. The candidate cross-attribute prediction mode refers to any one of prediction mode 1, prediction mode 2, and prediction mode 3.

S302: Determine one or more attribute reconstruction values of candidate neighboring points of the current point according to the candidate cross-attribute prediction mode.

In the embodiment of the present application, for each candidate cross-attribute prediction mode of the candidate prediction modes, it is necessary to determine one or more attribute reconstruction values of candidate neighboring points of the current point. The candidate neighboring points correspond to the candidate cross-attribute prediction modes.

Exemplarily, according to prediction mode 1, one or more attribute reconstruction values of the first candidate neighbor point of the current point are determined; according to prediction mode 2, one or more attribute reconstruction values of the second candidate neighbor point of the current point are determined; according to prediction mode 3, one or more attribute reconstruction values of the third candidate neighbor point of the current point are determined.

It should be noted that the description of S302 can refer to the description of S103 in the previous text, and will not be repeated here.

S303: Determine the correlation coefficient of the candidate neighboring points based on one or more attribute reconstruction values of the candidate neighboring points.

In the embodiment of the present application, the correlation coefficients of candidate neighbor points have the following three cases:

Case 1: The correlation coefficient of the candidate neighbor points represents the correlation between multiple attribute reconstruction values of the candidate neighbor points.

For example, the multiple attribute reconstruction values of the candidate neighboring points may include: a brightness reconstruction value and a reflectivity reconstruction value. In this case, the correlation coefficient of the candidate neighboring points may represent the correlation between the brightness reconstruction value and the reflectivity reconstruction value of the candidate neighboring points.

Case 2: The correlation coefficient of the candidate neighboring points represents the correlation between the attribute reconstruction value of the candidate neighboring points and the attribute reconstruction value of the current point.

For example, when the attribute reconstruction value of the candidate neighbor point is a brightness reconstruction value, the correlation coefficient of the candidate neighbor point can represent the correlation between the brightness reconstruction value of the candidate neighbor point and the brightness reconstruction value of the current point. Alternatively, when the attribute reconstruction value of the candidate neighbor point is a reflectivity reconstruction value, the correlation coefficient of the candidate neighbor point can represent the correlation between the reflectivity reconstruction value of the candidate neighbor point and the reflectivity reconstruction value of the current point.

Case 3: The correlation coefficient of the candidate neighbor points represents the correlation between the attribute reconstruction values of the candidate neighbor points and the attribute reconstruction values of the non-candidate neighbor points of the current point.

For example, the non-candidate neighbor point is any neighbor point other than the candidate neighbor point among the M neighbor points of the current point. The non-candidate neighbor point can be the neighbor point closest to the current point other than the candidate neighbor point among the M neighbor points of the current point.

For example, the correlation coefficient of the candidate neighbor point may represent the correlation between the brightness reconstructed value of the candidate neighbor point and the brightness reconstructed value of the non-candidate neighbor point. Alternatively, the correlation coefficient of the candidate neighbor point may represent the correlation between the brightness reconstructed value of the candidate neighbor point and the reflectivity reconstructed value of the non-candidate neighbor point. Alternatively, the correlation coefficient of the candidate neighbor point may represent the correlation between the reflectivity reconstructed value of the candidate neighbor point and the reflectivity reconstructed value of the non-candidate neighbor point.

S304: Determine the attribute prediction value of the attribute to be encoded at the current point according to the attribute reference value and the correlation coefficient of the attribute to be encoded at the current point.

In the embodiment of the present application, the attributes to be encoded may include: brightness and reflectivity.

In some embodiments of the present application, the attribute reference value of the attribute to be encoded at the current point is multiplied by the correlation coefficient to determine the attribute prediction value of the attribute to be encoded.

S305 : Make a coding decision based on the attribute prediction value of the to-be-coded attribute of the current point corresponding to one or more candidate cross-attribute prediction modes, and determine the best prediction mode of the current point as the target cross-attribute prediction mode.

In an embodiment of the present application, a rate-distortion optimization (RDO) is performed on the attribute prediction value of the attribute to be encoded of the current point corresponding to one or more candidate cross-attribute prediction modes to obtain one or more rate-distortion cost values; and the candidate cross-attribute prediction mode corresponding to the minimum cost value among the one or more rate-distortion cost values is used as the optimal prediction mode for the current point.

S306: Determine the value of the first syntax element information at the current point according to the optimal prediction mode.

In the embodiment of the present application, the first syntax element information is used to indicate the best prediction mode for the current point, wherein the best prediction mode for the current point can be a cross-attribute prediction mode or a non-cross-attribute prediction mode.

In the embodiment of the present application, the value of the first syntax element information can be in parameter form or in digital form. Specifically, the first syntax element information can be a parameter written in the profile or a flag value, which is not specifically limited here.

Exemplarily, if it is determined that the best prediction mode of the current point is prediction mode 0, the value of the first syntax element information is set to 0; if it is determined that the best prediction mode of the current point is prediction mode 1, the value of the first syntax element information is set to 1.

S307: Encode the first syntax element information, and write the obtained coded bits into a bitstream.

An embodiment of the present application provides a coding method, which includes: determining a candidate cross-attribute prediction mode for a current point from candidate prediction modes; determining one or more attribute reconstruction values of candidate neighboring points of the current point based on the candidate cross-attribute prediction mode; determining the correlation coefficient of the candidate neighboring points based on one or more attribute reconstruction values of the candidate neighboring points; determining the attribute prediction value of the attribute to be encoded at the current point based on the attribute reference value and the correlation coefficient of the attribute to be encoded at the current point; making a coding decision based on the attribute prediction value of the attribute to be encoded at the current point corresponding to one or more candidate cross-attribute prediction modes, and determining the best prediction mode for the current point as the target cross-attribute prediction mode; and determining the best prediction mode for the current point as the target cross-attribute prediction mode based on the best The optimal prediction mode is used to determine the value of the first syntax element information at the current point; the first syntax element information is encoded, and the resulting coded bits are written into the bitstream. The attribute prediction value of the attribute to be coded is calculated based on the correlation information of the neighboring points of the current point and the known attribute values, and can be used as the predicted value of the attribute at the current point. The correlation coefficient can help measure the degree of correlation between the attribute to be coded and the known attributes. If the correlation coefficient is high, it means that there is a strong linear relationship between the two, and the predicted value can more accurately reflect the actual value of the attribute to be coded. By using the correlation coefficient for prediction, unnecessary data transmission and storage can be avoided. If the correlation between the attribute to be coded and the known attributes is low, the predicted value will be closer to the actual value of the attribute to be coded, reducing the transmission and storage of redundant data. This can improve the utilization of the bit rate, avoid bit rate waste, and thus improve coding performance.

The following describes how to perform cross-attribute prediction on the attribute prediction value of the attribute to be decoded at the current point in the three cases of correlation coefficients of the target neighbor points mentioned above. The details are divided into the following three points.

1. For case 1, the correlation coefficient represents the correlation between multiple attribute reconstruction values of candidate neighbor points.

In some embodiments of the present application, the correlation coefficient includes a first coefficient; and the implementation of determining the correlation coefficient of the candidate neighboring point based on one or more attribute reconstruction values of the candidate neighboring point in S303 may include:

A first coefficient is determined according to the first attribute reconstruction value of the candidate neighbor point and the second attribute reconstruction value of the candidate neighbor point.

In an embodiment of the present application, in situation 1, the attribute corresponding to the first attribute reconstruction value of the candidate neighbor point is different from the attribute corresponding to the second attribute reconstruction value of the candidate neighbor point, and the attribute corresponding to the first attribute reconstruction value is correlated with the attribute corresponding to the second attribute reconstruction value.

It should be noted that the attribute corresponding to the first attribute reconstruction value and the attribute corresponding to the second attribute reconstruction value are different and correlated, meaning that the first attribute reconstruction value and the second attribute reconstruction value are reconstruction values of two different attributes (the first attribute and the second attribute), but the first attribute and the second attribute are correlated. For example, the first attribute can be brightness and the second attribute can be reflectivity.

Here, the correlation between brightness and reflectivity is explained. In terms of the optical properties of an object, brightness generally refers to the intensity or brightness of light perceived by the human eye. Reflectivity, on the other hand, characterizes the degree of light reflection from an object's surface, that is, the relative intensity of light reflected from the surface. Generally speaking, higher reflectivity results in higher brightness. According to optical theory, an object's brightness and reflectivity properties are strongly correlated. Statistics show that the reflectivity and brightness information of most points in the Am-fused point cloud also have a strong correlation, especially for neighboring points, where the correlation is essentially the same. Therefore, using the encoded attribute information of the current point to predict the unencoded attribute value can reduce residual errors and remove redundancy. For example, as shown in Figure 9, the brightness information (luma) of the current point cloud has been encoded. The current point (the point to be predicted) is P0, and its three neighboring points are P1, P2, and P3. If the candidate neighbor point of the current point is P1, the relationship between the brightness reconstruction value of P1 (luma=40) and the reflectivity reconstruction value of P1 (ref=40) is used to establish a linear model (first coefficient) as the correlation between the brightness information and reflectivity information of the current point P0, so as to use the brightness reconstruction value of P0 and the first coefficient of P1 to predict the attribute prediction value of the attribute to be decoded (reflectivity) of the current point, or use the reflectivity reconstruction value of P0 and the first coefficient of P1 to predict the attribute prediction value of the attribute to be decoded (brightness rate) of the current point.

It can be understood that the first coefficient can reflect the degree of correlation between the different attributes of the candidate neighbor points. If the first coefficient is close to 1, it indicates a strong positive correlation between the first attribute and the second attribute; if it is close to 0, it indicates that there is almost no correlation between the two. This helps to assess the correlation between attributes and better understand the characteristics and patterns of the data.

In some embodiments of the present application, the first coefficient includes a ratio of a first attribute reconstruction value of the candidate neighbor point to a second attribute reconstruction value of the candidate neighbor point.

In the embodiment of the present application, the attribute corresponding to the first attribute reconstruction value and the attribute corresponding to the second attribute reconstruction are related to the attribute coding order of the current node.

In the embodiment of the present application, taking the first attribute as brightness (luma) and the second attribute as reflectivity (ref) as an example, the first coefficient includes the following two cases:

(1) When the attribute reconstruction order of the current point is that the first attribute precedes the second attribute, the attribute to be decoded is the second attribute, the first attribute reconstruction value of the candidate neighbor point is the reconstruction value of the second attribute of the candidate neighbor point, the second attribute reconstruction value of the candidate neighbor point is the reconstruction value of the first attribute of the candidate neighbor point, and the attribute reference value of the attribute to be decoded is the reconstruction value of the first attribute of the current point.

In an embodiment of the present application, when the attribute reconstruction order of the current point is brightness before reflectivity, the attribute to be decoded of the current node is reflectivity, the first attribute reconstruction value of the candidate neighbor point is the reflectivity reconstruction value of the candidate neighbor point, the second attribute reconstruction value of the candidate neighbor point is the brightness reconstruction value of the candidate neighbor point, the attribute reference value of the attribute to be decoded of the current point is the brightness reconstruction value of the current point, and the attribute prediction value of the attribute to be decoded of the current point is the reflectivity prediction value. For example, the reflectivity prediction value of the current point can refer to the description of formulas (7) and (8) above.

(2) When the attribute reconstruction order of the current point is that the second attribute precedes the first attribute, the attribute to be decoded is the first attribute, the first attribute reconstruction value of the candidate neighbor point is the reconstruction value of the first attribute of the candidate neighbor point, the second attribute reconstruction value of the candidate neighbor point is the reconstruction value of the second attribute of the candidate neighbor point, and the attribute reference value of the attribute to be decoded is the reconstruction value of the second attribute of the current point.

In the embodiment of the present application, when the attribute reconstruction order of the current point is that reflection precedes brightness, the attribute to be decoded of the current node is brightness, the first attribute reconstruction value of the candidate neighboring point is the brightness reconstruction value of the candidate neighboring point, the second attribute reconstruction value of the candidate neighboring point is the reflectivity reconstruction value of the candidate neighboring point, the attribute reference value of the attribute to be decoded of the current point is the reflectivity reconstruction value of the current point, and the attribute to be decoded of the current point is the reflectivity reconstruction value of the current point. The attribute prediction value of the code attribute is a brightness prediction value. For example, the brightness prediction value of the current point can refer to the description of formula (9) and formula (10) in the above text.

It can be understood that, on the one hand, the correlation between the first attribute reconstruction value and the second attribute reconstruction value of the candidate neighbor point can be incorporated into the prediction process through the first coefficient. If the first coefficient is large, indicating that there is a strong correlation between the two attributes, then when predicting the attribute to be decoded at the current point, the relationship between the first attribute and the second attribute can be more accurately utilized to improve the accuracy of the prediction. On the other hand, combining the first coefficient and the attribute reference value can obtain a more accurate attribute prediction value of the attribute to be decoded. This prediction method based on correlation information can avoid unnecessary errors and improve the accuracy of the decoding process. On the other hand, the attribute prediction value calculation process based on the first coefficient and the attribute reference value is relatively simple and accurate, does not require excessive computing resources, can reduce redundant information in the data transmission process, and improve the efficiency of data transmission. Especially in the case of limited bandwidth or high transmission costs, the effective use of correlation information can save transmission data resources.

2. For case 2, the correlation coefficient represents the correlation between the attribute reconstruction value of the candidate neighbor point and the attribute reconstruction value of the current point.

In some embodiments of the present application, the correlation coefficient includes a second coefficient; and the implementation of determining the correlation coefficient of the candidate neighbor point based on one or more attribute reconstruction values of the candidate neighbor point in S303 may include:

The second coefficient is determined according to the first attribute reconstruction value of the current point and the first attribute reconstruction value of the candidate neighboring point.

In the embodiment of the present application, in case 2, the attribute corresponding to the first attribute reconstruction value of the candidate neighbor point is the same as the attribute corresponding to the attribute reconstruction value of the current point.

In some embodiments of the present application, the second coefficient includes a ratio of a first attribute reconstruction value of the current point to a first attribute reconstruction value of a candidate neighboring point.

In the embodiment of the present application, the attribute corresponding to the first attribute reconstruction value is related to the attribute coding order of the current point.

In the embodiment of the present application, taking the first attribute as brightness (luma) and the second attribute as reflectivity (ref) as an example, the second coefficient includes the following two cases:

(1) When the attribute reconstruction order of the current point is that the first attribute precedes the second attribute, the attribute to be decoded is the second attribute, the first attribute reconstruction value of the current point is the reconstruction value of the first attribute of the current point, the first attribute reconstruction value of the candidate neighboring point is the reconstruction value of the first attribute of the candidate neighboring point, and the attribute reference value of the attribute to be decoded is the reconstruction value of the second attribute of the candidate neighboring point.

In an embodiment of the present application, when the attribute reconstruction order of the current point is brightness before reflectivity, the attribute to be decoded of the current node is reflectivity, the first attribute reconstruction value of the current point is the brightness reconstruction value of the current point, the first attribute reconstruction value of the candidate neighboring point is the brightness reconstruction value of the candidate neighboring point, the attribute reference value of the attribute to be decoded of the current point is the reflectivity reconstruction value of the candidate neighboring point, and the attribute prediction value of the attribute to be decoded of the current point is the reflectivity prediction value. For example, the brightness prediction value of the current point can refer to the description of formula (11) and formula (12) in the previous text.

(2) When the attribute reconstruction order of the current point is that the second attribute precedes the first attribute, the attribute to be decoded is the first attribute, the first attribute reconstruction value of the current point is the reconstruction value of the second attribute of the current point, the first attribute reconstruction value of the candidate neighboring point is the reconstruction value of the second attribute of the candidate neighboring point, and the attribute reference value of the attribute to be decoded is the reconstruction value of the first attribute of the candidate neighboring point.

In an embodiment of the present application, when the attribute reconstruction order of the current point is reflectance before brightness, the attribute to be decoded of the current node is brightness, the first attribute reconstruction value of the current point is the reflectivity reconstruction value of the current point, the first attribute reconstruction value of the candidate neighboring point is the reflectivity reconstruction value of the candidate neighboring point, the attribute reference value of the attribute to be decoded of the current point is the brightness reconstruction value of the candidate neighboring point, and the attribute prediction value of the attribute to be decoded of the current point is the brightness prediction value. For example, the brightness prediction value of the current point can refer to the description of formula (13) and formula (14) above.

It can be understood that the second coefficient takes into account the ratio between the first attribute reconstruction value of the current point and the first attribute reconstruction value of the candidate neighboring point. This relationship can help consider the correlation between the attributes of the current point and the attributes of the candidate neighboring points, thereby more accurately predicting the attribute value of the current point. Combining the second coefficient and the attribute reference value, a more accurate attribute prediction value of the attribute to be decoded can be obtained. Consideration of the second coefficient makes the prediction process more targeted, can better reflect the attribute relationship between the current point and the candidate neighboring points, and improve the accuracy of the prediction. Accurate attribute prediction values can reduce unnecessary data transmission and save transmission resources. By considering the second coefficient, the correlation between attributes can be more effectively utilized, redundant information in the transmission process can be reduced, and the efficiency of data transmission can be improved.

3. The correlation coefficient in case 2 represents the correlation between the attribute reconstruction values of the candidate neighbor points and the attribute reconstruction values of the non-candidate neighbor points of the current point.

In some embodiments of the present application, the correlation coefficient includes a third coefficient; and the implementation of determining the correlation coefficient of the candidate neighbor point based on one or more attribute reconstruction values of the candidate neighbor point in S303 may include:

The third coefficient is determined according to the first attribute reconstruction value of the non-candidate neighbor point and the first attribute reconstruction value of the candidate neighbor point.

In the embodiment of the present application, in case 3, the attribute corresponding to the first attribute reconstruction value of the candidate neighbor point is the same as the attribute corresponding to the attribute reconstruction value of the non-candidate neighbor point.

In the embodiment of the present application, a non-candidate neighbor point is any neighbor point among the M neighbor points of the current point except the candidate neighbor point. For example, the non-candidate neighbor point can be the neighbor point closest to the current point among the M neighbor points of the current point excluding the candidate neighbor point, or the non-candidate neighbor point can be the neighbor point closest to the candidate neighbor point among the M neighbor points of the current point excluding the candidate neighbor point. This application does not impose any restrictions on this.

In some embodiments of the present application, the third coefficient includes a ratio of the first attribute reconstructed value of the candidate neighbor point to the first attribute reconstructed value of the non-candidate neighbor point.

In the embodiment of the present application, the attribute corresponding to the first attribute reconstruction value is related to the attribute coding order of the current point.

In the embodiment of the present application, taking the first attribute as brightness (luma) and the second attribute as reflectivity (ref) as an example, the third coefficient includes the following two cases:

(1) When the attribute reconstruction order of the current point is that the first attribute precedes the second attribute, the attribute to be decoded is the second attribute, the first attribute reconstruction value of the non-candidate neighbor point is the reconstruction value of the first attribute of the non-candidate neighbor point, the first attribute reconstruction value of the candidate neighbor point is the reconstruction value of the first attribute of the candidate neighbor point, and the attribute reference value of the attribute to be decoded is the reconstruction value of the second attribute of the candidate neighbor point.

In an embodiment of the present application, when the attribute reconstruction order of the current point is brightness before reflectivity, the attribute to be decoded of the current node is reflectivity, the first attribute reconstruction value of the non-candidate neighboring point is the brightness reconstruction value of the current point, the first attribute reconstruction value of the candidate neighboring point is the brightness reconstruction value of the candidate neighboring point, the attribute reference value of the attribute to be decoded of the current point is the reflectivity reconstruction value of the candidate neighboring point, and the attribute prediction value of the attribute to be decoded of the current point is the reflectivity prediction value. For example, the brightness prediction value of the current point can refer to the description of formula (15) and formula (16) in the previous text.

(2) When the attribute reconstruction order of the current point is that the second attribute precedes the first attribute, the attribute to be decoded is the first attribute, the first attribute reconstruction value of the non-candidate neighbor point is the reconstruction value of the second attribute of the non-candidate neighbor point, the first attribute reconstruction value of the candidate neighbor point is the reconstruction value of the second attribute of the candidate neighbor point, and the attribute reference value of the attribute to be decoded is the reconstruction value of the first attribute of the candidate neighbor point.

In an embodiment of the present application, when the attribute reconstruction order of the current point is reflectance before brightness, the attribute to be decoded of the current node is brightness, the first attribute reconstruction value of the current point is the reflectivity reconstruction value of the current point, the first attribute reconstruction value of the candidate neighboring point is the reflectivity reconstruction value of the candidate neighboring point, the attribute reference value of the attribute to be decoded of the current point is the brightness reconstruction value of the candidate neighboring point, and the attribute prediction value of the attribute to be decoded of the current point is the brightness prediction value. For example, the brightness prediction value of the current point can refer to the description of formulas (17) and (18) above.

It can be understood that the third coefficient takes into account the ratio relationship between the first attribute reconstruction value of the candidate neighbor point and the first attribute reconstruction value of the non-candidate neighbor point. This ratio relationship can help evaluate the attribute correlation between the candidate neighbor point and the non-candidate neighbor point, so as to better understand the characteristics and laws of the data. Combining the third coefficient and the attribute reference value, a more accurate attribute prediction value of the attribute to be decoded can be obtained. The consideration of the third coefficient makes the prediction process more comprehensive and comprehensive, which can better reflect the attribute relationship between the current point and the candidate neighbor point and the non-candidate neighbor point, and improve the accuracy of the prediction. Accurate attribute prediction values can reduce unnecessary data transmission and save transmission resources. By considering the third coefficient, the attribute correlation between the candidate neighbor point and the non-candidate neighbor point can be more effectively utilized, the redundant information in the transmission process can be reduced, and the efficiency of data transmission can be improved.

In some embodiments of the present application, the encoding method further includes:

determining second syntax element information;

If the value of the second syntax element information is the first value, then determining the attribute reconstruction order of the current point is that the first attribute precedes the second attribute; or

If the value of the second syntax element information is the second value, determining that the attribute reconstruction order of the current point is that the second attribute precedes the first attribute;

The method also includes:

The second syntax identification information is coded, and the obtained coded bits are written into the bitstream.

In the embodiment of the present application, the second syntax element information is used to indicate the attribute reconstruction order of the current point.

Exemplarily, the second syntax element information may be a flag bit in the attribute parameter APS, and the second syntax element information may be expressed as muti_crosstype_pre, which is used to indicate the attribute coding order of the current point.

It should be noted that in the embodiment of the present application, the first value and the second value are different, and the first value and the second value can be in parameter form or in digital form. Specifically, the second syntax identification information can be a parameter written in the profile or a flag value, which is not specifically limited here.

In some embodiments of the present application, the candidate prediction mode further includes the first prediction mode, and the encoding method further includes S401 to S403:

S401, determining a first prediction mode for a current point from candidate prediction modes;

S402: Determine an attribute prediction value of the attribute to be encoded at the current point according to the first prediction mode;

S403 : Make a coding decision based on the attribute prediction values of the to-be-coded attribute of the current point corresponding to each of the one or more candidate cross-attribute prediction modes and the first prediction mode, and determine that the best prediction mode for the current point is the first prediction mode.

In the embodiment of the present application, the first prediction mode is a non-cross-attribute prediction mode.

In the embodiment of the present application, the first prediction mode is related to the reconstructed attribute values of the M neighboring points of the current point. For example, the first prediction mode can represent the weighted average of the attribute reconstruction values of the reconstructed attributes of the M neighboring points of the current node. The reconstructed attributes are the same as the attributes to be encoded of the current point.

In the embodiment of the present application, candidate prediction modes can be divided into two cases:

Case 1: The candidate prediction modes include: one or more candidate cross-attribute prediction modes.

Exemplarily, one or more candidate cross-attribute prediction modes may include: prediction mode 1, prediction mode 2, and prediction mode 3. Prediction mode 1 indicates that the first candidate neighboring point of the current point is used to perform cross-attribute guided prediction to derive the attribute prediction value of the attribute to be encoded at the current point. Prediction mode 2 indicates that the second candidate neighboring point of the current point is used to perform cross-attribute guided prediction to derive the attribute prediction value of the attribute to be encoded at the current point. Prediction mode 3 indicates that the third candidate neighboring point of the current point is used to perform cross-attribute guided prediction to derive the attribute prediction value of the attribute to be encoded at the current point. In this case, the encoder will make encoding decisions on the attribute prediction values of the attribute to be encoded of the current point corresponding to prediction mode 1, prediction mode 2, and prediction mode 3, respectively, to determine the optimal prediction mode for the current point.

Case 2: the candidate prediction modes include: one or more candidate cross-attribute prediction modes and one or more non-cross-attribute prediction modes.

Exemplarily, the candidate prediction modes include; 3 candidate cross-attribute prediction modes (prediction mode 1, prediction mode 2 and prediction mode 3) and 1 non-cross-attribute prediction mode (prediction mode 0). Prediction mode 0 indicates the use of the attribute reconstruction values of the 3 candidate neighboring points of the current point to derive the attribute prediction value of the attribute to be encoded of the current point. Exemplarily, prediction mode 0 indicates the use of the weighted average of the brightness reconstruction values of the 3 candidate neighboring points of the current point as the brightness prediction value of the current point, or the use of the weighted average of the reflectivity reconstruction values of the 3 neighboring points of the current point as the reflectivity prediction value of the current point. In this case, the encoder will make encoding decisions on the attribute prediction values of the attribute to be encoded of the current point corresponding to prediction mode 0, prediction mode 1, prediction mode 2 and prediction mode 3, respectively, to determine the best prediction mode for the current point.

In some embodiments of the present application, the encoding method further includes S501 to S503:

S501, determining whether the current point meets a preset condition based on the attribute reconstruction values of the reconstructed attributes of the M neighboring points of the current point;

S502: When the attribute reconstruction values of the reconstructed attributes of the M neighboring points of the current point meet the preset conditions, determine the candidate cross-attribute prediction mode of the current point from the candidate prediction modes, or

S503: When the reconstructed attribute values of the respective reconstructed attributes of the M neighboring points of the current point do not meet a preset condition, determine that the current point adopts the second prediction mode.

In some embodiments of the present application, M is a positive integer greater than or equal to 2; the preset conditions include: the maximum attribute difference of the attribute reconstruction values of the reconstructed attributes between the M neighboring points is greater than or equal to a preset threshold; the reconstructed attribute is the same as the attribute to be encoded.

In some embodiments of the present application, the encoding method further includes:

When it is determined that the current point adopts the second prediction mode, determining the spatial geometric weights of the M neighboring points according to their respective spatial positions and the spatial position of the current point;

The attribute prediction value of the attribute to be encoded is determined according to the attribute reconstruction value of the reconstructed attribute of each of the M neighboring points and the spatial geometric weight of each of the M neighboring points.

It's sensible to understand that, on the one hand, by determining whether the attribute reconstructed values of the current point's M neighboring points meet preset conditions, data decision optimization can be performed. If the conditions are met, the bitstream encoding process is executed, more accurately determining the first syntax element information of the current point, improving encoding accuracy and efficiency. If the conditions are not met, the second prediction mode is selected, making data processing more intelligent and adaptable. Furthermore, making decisions based on preset conditions avoids encoding data that doesn't meet the conditions, saving computing resources and time during the encoding process, helping to improve resource utilization and making the encoding process more efficient. Data processing and decision-making based on preset conditions ensures data quality and integrity. Encoding only data that meets the conditions or selecting the second prediction mode helps avoid unnecessary data processing errors or misjudgments, improving data processing accuracy and reliability. Furthermore, the application of preset conditions can make system operation more intelligent and efficient. Flexible selection of encoding or prediction modes based on specific conditions helps improve system efficiency and meet diverse data processing requirements.

It should be noted that the explanation of S501 to S503 can refer to the description of S201 to S203 in the previous text, and will not be repeated here.

In some embodiments of the present application, the encoding method further includes:

determining third syntax element information;

In a case where the third syntax element information indicates that the current point allows the use of the cross-attribute prediction mode, determining that the candidate prediction modes include one or more cross-attribute prediction modes; or

In a case where the third syntax element information indicates that the current point prohibits the cross-attribute prediction mode, determining that the candidate prediction modes include one or more non-cross-attribute prediction modes;

Determining one or more attribute prediction values of the current point according to one or more non-cross-attribute prediction modes;

Determine the best prediction mode for the current point based on the encoding decision of one or more attribute prediction values of the current point;

Determining a value of the first syntax element information according to the optimal prediction mode;

Performing encoding processing on the first syntax element information, and writing the obtained encoding bits into a bitstream;

The encoding method also includes:

The third syntax identification information is coded, and the obtained coded bits are written into the bitstream.

In the embodiment of the present application, the third syntax element information is used to indicate whether the cross-attribute prediction mode is allowed at the current point.

In the embodiment of the present application, the candidate prediction mode is related to whether the current point indicated by the third syntax element allows the use of the cross-attribute prediction mode, specifically including the following two cases:

Case 1: The third syntax element information indicates that the cross-attribute prediction mode is allowed at the current point.

Exemplarily, if the current point allows the use of a cross-attribute prediction mode, the candidate prediction modes include at least: one or more candidate cross-attribute prediction modes. The candidate prediction modes of the current point include: prediction mode 0 (first prediction mode), prediction mode 1, prediction mode 2, and prediction mode 3. Among them, prediction mode 0 is a non-cross-attribute prediction mode, and prediction mode 1, prediction mode 2, and prediction mode 3 are cross-attribute prediction modes. The first syntax element information is used to indicate the best prediction mode adopted by the current point, wherein the best prediction mode can be a target cross-attribute prediction mode (any one of prediction modes 1 to 3) or a non-cross-attribute prediction mode (prediction mode 0).

Case 2: The third syntax element information indicates that the cross-attribute prediction mode is not allowed at the current point.

In this embodiment of the present application, if the current point allows the use of a cross-attribute prediction mode, the candidate prediction modes include at least one or more non-cross-attribute prediction modes. The candidate prediction modes for the current point include: prediction mode 0, prediction mode 1, prediction mode 2, and prediction mode 3. Prediction modes 0 to 3 are all non-cross-attribute prediction modes.

Exemplarily, taking the attribute to be encoded of the current point as brightness as an example, the candidate prediction modes of the current point include: prediction mode 0, prediction mode 1, prediction mode 2 and prediction mode 3. Prediction mode 0 indicates that the weighted average brightness reconstruction value of the brightness reconstruction values of the three neighboring points of the current point is used as the brightness prediction value of the current point. Prediction mode 1 indicates that the brightness prediction value of the current point is determined by using the brightness reconstruction value of the first neighboring point of the current point. Prediction mode 2 indicates that the brightness prediction value of the current point is determined by using the brightness reconstruction value of the second neighboring point of the current point. Prediction mode 3 indicates that the brightness prediction value of the current point is determined by using the brightness reconstruction value of the third neighboring point of the current point. Further, the encoder makes an encoding decision based on the brightness prediction value of the current point corresponding to prediction mode 0, prediction mode 1, prediction mode 2 and prediction mode 3 to determine the best prediction mode for the current point.

In some embodiments of the present application, the encoding method further includes:

If the cross-attribute prediction mode is allowed at the current point, determining the value of the third syntax element information to be a third value; or

If the cross-attribute prediction mode is not allowed at the current point, the value of the third syntax element information is determined to be the fourth value.

In the embodiment of the present application, the third syntax element information is used to indicate whether the cross-attribute prediction mode is allowed at the current point.

Exemplarily, the third syntax element information may be a flag in the attribute parameter APS, and the third syntax element information may be expressed as crosstype_enable_flag, which is used to indicate whether the cross-attribute prediction mode is allowed at the current point.

It should be noted that in the embodiment of the present application, the third value is different from the fourth value, and the third value and the fourth value can be in parameter form or in numerical form. Specifically, the third syntax identification information can be a parameter written in the profile or a flag value, which is not specifically limited here.

Exemplarily, for the third value and the fourth value, the third value can be set to 1 and the fourth value can be set to 0; or, the third value can be set to 0 and the fourth value can be set to 1; or, the third value can be set to true and the fourth value can be set to false; or, the third value can be set to false and the fourth value can be set to true; but this is not specifically limited here.

In an embodiment of the present application, taking the flag written into the bitstream as an example, assuming that the third value is set to 1 (true) and the fourth value is set to 0 (false), if the value of the third syntax identification information is 0 (false), then it can be determined that the cross-attribute prediction mode is not allowed at the current point; if the value of the third syntax identification information is 1 (true), then it can be determined that the cross-attribute prediction mode is allowed at the current point.

The encoding and decoding method provided by this application is explained below in a specific embodiment.

The encoding and decoding method provided in the embodiment of the present application is also called an improved PT encoding method for single-neighbor cross-attribute prediction. As shown in FIG12 , the process of determining the optimal prediction mode includes S21 to S29:

S21. Calculate the maximum attribute difference of the three candidate neighbors.

In some embodiments, after the LoD is constructed, the three nearest neighboring points of the current point to be encoded are first found from the encoded data points according to the LoD generation order. The attribute reconstructed values of these three nearest neighboring points (3 nearest neighboring points) are used as candidate prediction values for the current point to be encoded.

S22: Whether the maximum attribute difference is greater than a preset threshold.

In some embodiments, the maximum attribute difference max_difference of the three candidate neighbors is calculated. If the maximum attribute difference is greater than a preset threshold (adaptive threshold adaptive_threshold), step 23 is executed. If the maximum attribute difference is less than or equal to the preset threshold (adaptive threshold adaptive_threshold), step S24 is executed.

S23. Calculate the correlation coefficient s of the two attributes of the three nearest neighbor points respectively.

S24, optimal prediction mode = 0.

In some embodiments, if the value of max_difference is less than the adaptive threshold adaptive_threshold, the three neighboring points are considered to have attribute values close to the predicted point, and thus mode 0 weighted prediction is adopted.

S25. Recalculate the attribute prediction values of patterns 1 to 3 using the correlation coefficient.

S26. Calculate the scores of prediction modes 0 to 3 using RDO.

In some embodiments, if the value of max_difference is greater than the adaptive threshold adaptive_threshold, the optimal prediction value is selected according to Rate-Distortion Optimal (RDO). The scores of modes 0 to 3 are calculated, and the minimum cost score is found as the optimal prediction mode.

S27. Find the minimum score.

In some embodiments, the mode corresponding to the minimum cost score among the scores of modes 0 to 3 is taken as the best prediction mode.

S28. Set the optimal prediction mode.

S29, optimal prediction mode = 0 to 3.

In an embodiment of the present application, in the current Predicting Transform (PT) coding scheme, after the LoD is constructed, the three nearest neighboring points of the current point to be encoded are first found from the encoded data points according to the generation order of the LoD. The maximum attribute difference max_difference of the three candidate neighbors is then calculated. If the value of max_difference is less than the adaptive threshold, the three neighboring points are considered to be close to the attribute values of the predicted point, and thus mode 0 weighted prediction is adopted. If the value of max_difference is greater than the adaptive threshold, the optimal prediction value is selected from four prediction modes according to RDO: mode 0 is average prediction, and modes 1, 2, and 3 respectively reconstruct the attribute values of the three nearest neighboring points as the attribute prediction value of the current point to be encoded. When one attribute information of the multi-attribute point cloud has been encoded, the prediction modes 1, 2, and 3 are modified to use the correlation between the two attributes of each nearest neighbor point as the correlation between the two attributes of the current point (equivalent to the correlation coefficient), so that the first attribute value encoded at the current point (equivalent to the attribute reference value of the attribute to be decoded at the current point) can be used to predict the second attribute value to be encoded at the current point (equivalent to the attribute prediction value of the attribute to be decoded at the current point).

In the current PT encoding scheme, as shown in Table 1, prediction modes 1 to 3 directly use the attribute reconstruction values of the three nearest neighbors of the current point as the attribute prediction value of the current point to be encoded. Since the brightness information and reflectivity information of the point cloud are correlated, especially between neighboring points, the correlation between the two attributes is roughly the same. If the brightness information of the point cloud has already been encoded, cross-attribute prediction is used to remove redundancy. The reflectivity value of the current point to be predicted can be expressed as: Where Coeff _ref and Coeff _luma represent the reflectivity value and brightness value of the current point respectively, and s is the correlation coefficient. This application replaces prediction modes 1 to 3 with the correlation coefficients of the brightness reconstruction values and reflectivity reconstruction values of the three nearest neighboring points of the current point, and then uses these three correlation coefficients as the attribute correlation coefficients of the current point, thereby achieving the purpose of single neighbor guidance cross-attribute prediction. Therefore, the improved prediction mode modification of attribute coding is shown in Table 3. The calculation of the correlation coefficient of the brightness and reflectivity of the neighboring nodes can be expressed as: in and represent the reconstructed reflectance value and brightness value of the ith neighbor, respectively, and _si represents the correlation coefficient of the brightness and reflectance attributes of the ith neighbor.

In the embodiment of the present application, the operation flow at the encoding end is shown in FIG13. Taking brightness (equivalent to the first attribute) as an example to predict reflectivity (equivalent to the second attribute), the brightness information of the original point cloud is encoded according to the original mode. After the brightness attribute encoding is completed, for the reflectivity, first, according to the generation order of LoD, first find the three nearest neighboring points (3 nearest neighboring points) of the current point to be encoded (current point) from the encoded data points (the nearest neighboring points of the current point). Then calculate the maximum attribute distance (maximum attribute difference) of the three nearest neighboring points. If the maximum attribute distance is less than the adaptive threshold (preset threshold), select the average prediction of prediction mode 0 (first prediction mode); if the maximum attribute distance is greater than the adaptive threshold, calculate the correlation coefficient s (first coefficient) of the brightness and attribute information of the three nearest neighboring points respectively. Then use the brightness information of the current point to be encoded (brightness reconstruction value) and the calculated correlation coefficient s to calculate the predicted value of the reflectivity (the predicted value of the reflectivity of the current point). Use RDO technology to select the best predicted value, quantize the prediction residual (the predicted residual of the reflectivity of the current point), entropy code, and form an attribute code stream.

In the embodiment of the present application, the original code stream structure is not changed, and only the flag crosstype_enable_flag (equivalent to the third syntax element information) is added to the attribute parameter APS and encoded. Its value is 1 to enable cross-attribute prediction, and its value is 0 to disable cross-attribute prediction.

In the embodiment of the present application, the operation process at the decoding end is shown in Figure 14. Taking brightness (equivalent to the first attribute) to predict reflectivity (equivalent to the second attribute) as an example, the brightness information of the original point cloud is decoded according to the original mode. After the brightness attribute decoding and reconstruction are completed, for reflectivity, the decoding end reads the attribute code stream, first solves the attribute quantization residual through entropy decoding, and dequantizes the decoded quantization residual to obtain the attribute residual (reflectivity prediction residual). Then, according to the generated LoD order, the three nearest neighbors (3 neighboring points) of the current point are found from the decoded and reconstructed data points (the neighboring points of the current point). The maximum attribute distance (maximum attribute difference) between the three nearest neighbor points is then calculated. If the maximum attribute distance is less than an adaptive threshold (preset threshold), the prediction mode is inferred to be the average prediction of 0 (the first prediction mode). If the maximum attribute distance is greater than the adaptive threshold, the optimal prediction mode i (the first syntax element information) is decoded from the bitstream. If the value of i is 1 to 3, the correlation coefficient (the first coefficient) between the brightness and reflectivity of the i-th neighbor (the target neighbor point) is calculated. The reflectivity prediction value (the current point's reflectivity prediction value) is then calculated using the current point's brightness information (the current point's brightness reconstruction value) and the correlation coefficient s (the first coefficient). After obtaining the attribute prediction value (the current point's reflectivity prediction value), it is added to the attribute residual obtained by inverse quantization (the current point's reflectivity prediction residual) to obtain the true reconstructed attribute value (the current point's reflectivity reconstruction value). The reflectivity information decoding of the current point is completed. This process is repeated through all point sets until the reflectivity information of all points is fully reconstructed.

In the case of this application, the specific implementation of the decoding end program is as follows: if the distance (maximum attribute difference) is greater than the adaptive threshold (predicted value), (a threshold is set), the optimal prediction mode i is decoded from the bitstream. If the value of i is 1 to 3, the correlation coefficient between the brightness and reflectivity of the i-th neighbor is calculated. The predicted reflectivity is then calculated using the brightness information of the current point and the correlation coefficient s (the first coefficient) of the target neighbor. This calculation process is implemented in the decoder function predictReflectance, where s is the correlation coefficient between the brightness and reflectivity of the i-th neighbor (the target neighbor).

In this embodiment, tests were conducted using the G-PCC reference software TMC13 V24.0 under the CTC-C1 and CTC-C2 test conditions, using octree-predicting and bitrates of r01, r02, and r03. Compared to the original PT solution, this application achieved a -2.9% gain in the reflectance component of the Am-fused average dataset under the TMC13 and CTC-C1 test conditions, and a -3.0% gain under the CTC-C2 test conditions. Furthermore, this application did not change the runtime complexity of G-PCC. The C1 condition is lossless geometry, lossy attribute coding, while the C2 condition is lossy geometry, lossy attribute coding. End-to-End BD-AttrRate represents the BD-Rate of the end-to-end attribute value for the attribute bitstream. BD-Rate reflects the difference in PSNR curves between two scenarios (with and without filtering). A decrease in BD-Rate indicates improved performance with a reduced bitrate while maintaining the same PSNR; conversely, performance decreases. The greater the decrease in BD-Rate, the better the compression. The Am-fused average dataset represents the fused point cloud dataset, and Overall average is the average of all sequence test results.

In the embodiment of the present application, the same correlation coefficient prediction method can be imitated on the LT lifting transformation (an improvement based on the PT) to perform neighbor-guided cross-attribute prediction redundancy removal.

The preferred embodiments of the present application are described in detail above in conjunction with the accompanying drawings. However, the present application is not limited to the specific details in the above embodiments. Within the technical concept of the present application, the technical solution of the present application can be subjected to a variety of simple modifications, and these simple modifications all fall within the scope of protection of the present application. For example, the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present application will no longer describe the various possible combinations separately. For another example, the various different embodiments of the present application can also be arbitrarily combined, as long as they do not violate the idea of the present application, they should also be regarded as the contents disclosed in the present application. For another example, under the premise of no conflict, the various embodiments and/or the technical features in each embodiment described in the present application can be arbitrarily combined with the prior art, and the technical solution obtained after the combination should also fall within the scope of protection of the present application.

It should be understood that in the various method embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

In one embodiment of the present application, based on the same inventive concept as the aforementioned embodiment, a code stream is provided, wherein the code stream is generated by bit encoding based on information to be encoded; wherein the information to be encoded includes at least one of the following:

First syntax element information, second syntax element information, and third syntax element information; the first syntax element information is used to indicate the prediction mode adopted by the current point, the second syntax element information is used to indicate the attribute reconstruction order of the current point, and the third syntax element information is used to indicate whether the current point allows the use of a cross-attribute prediction mode.

In yet another embodiment of the present application, based on the same inventive concept as the aforementioned embodiment, referring to FIG15 , a schematic diagram of the structure of a decoder provided in an embodiment of the present application is shown. As shown in FIG15 , the decoder 1000 includes a decoding part 1001 and a first determining part 1002, wherein:

The decoding part 1001 is configured to decode the code stream and determine the first syntax element information of the current point;

The first determining part 1002 is configured to determine, from the candidate prediction modes, the best prediction mode of the current point as the target cross-attribute prediction mode according to the value of the first grammatical element information; determine one or more attribute reconstruction values of the target neighboring points of the current point according to the target cross-attribute prediction mode; determine the target neighboring points according to the one or more attribute reconstruction values of the target neighboring points. determining the attribute prediction value of the attribute to be decoded at the current point according to the attribute reference value of the attribute to be decoded at the current point and the correlation coefficient.

In some embodiments, the correlation coefficient represents the correlation between multiple attribute reconstruction values of the target neighboring points.

In some embodiments, the correlation coefficient includes a first coefficient; the first determining portion 1002 is further configured to determine the first coefficient based on a first attribute reconstruction value of the target neighbor point and a second attribute reconstruction value of the target neighbor point.

In some embodiments, the first coefficient includes a ratio of a first attribute reconstructed value of the target neighbor point to a second attribute reconstructed value of the target neighbor point.

In some embodiments, when the attribute reconstruction order of the current point is that the first attribute precedes the second attribute, the attribute to be decoded is the second attribute, the first attribute reconstruction value of the target neighbor point is the reconstruction value of the second attribute of the target neighbor point, the second attribute reconstruction value of the target neighbor point is the reconstruction value of the first attribute of the target neighbor point, and the attribute reference value of the attribute to be decoded is the reconstruction value of the first attribute of the current point; or, when the attribute reconstruction order of the current point is that the second attribute precedes the first attribute, the attribute to be decoded is the first attribute, the first attribute reconstruction value of the target neighbor point is the reconstruction value of the first attribute of the target neighbor point, the second attribute reconstruction value of the target neighbor point is the reconstruction value of the second attribute of the target neighbor point, and the attribute reference value of the attribute to be decoded is the reconstruction value of the second attribute of the current point.

In some embodiments, the correlation coefficient represents the correlation between the attribute reconstruction value of the target neighbor point and the attribute reconstruction value of the current point.

In some embodiments, the correlation coefficient includes a second coefficient; the first determining part 1002 is further configured to determine the second coefficient based on the first attribute reconstruction value of the current point and the first attribute reconstruction value of the target neighboring point.

In some embodiments, the second coefficient includes a ratio of the first attribute reconstruction value of the current point to the first attribute reconstruction value of the target neighboring point.

In some embodiments, when the attribute reconstruction order of the current point is that the first attribute precedes the second attribute, the attribute to be decoded is the second attribute, the first attribute reconstruction value of the current point is the reconstruction value of the first attribute of the current point, the first attribute reconstruction value of the target neighboring point is the reconstruction value of the first attribute of the target neighboring point, and the attribute reference value of the attribute to be decoded is the reconstruction value of the second attribute of the target neighboring point; or, when the attribute reconstruction order of the current point is that the second attribute precedes the first attribute, the attribute to be decoded is the first attribute, the first attribute reconstruction value of the current point is the reconstruction value of the second attribute of the current point, the first attribute reconstruction value of the target neighboring point is the reconstruction value of the second attribute of the target neighboring point, and the attribute reference value of the attribute to be decoded is the reconstruction value of the first attribute of the target neighboring point.

In some embodiments, the decoding part 1001 is further configured to parse the code stream to determine the second syntax element information.

In some embodiments, the first determining part 1002 is further configured to, if the value of the second grammatical element information is a first value, determine that the attribute reconstruction order of the current point is that the first attribute precedes the second attribute; or, if the value of the second grammatical element information is a second value, determine that the attribute reconstruction order of the current point is that the second attribute precedes the first attribute.

In some embodiments, the correlation coefficient represents the correlation between the attribute reconstructed value of the target neighbor point and the attribute reconstructed value of the non-target neighbor point of the current point.

In some embodiments, the first determining portion 1002 is further configured to multiply the attribute reference value of the attribute to be decoded at the current point by the correlation coefficient to determine the attribute prediction value of the attribute to be decoded.

In some embodiments, the candidate prediction mode also includes a first prediction mode, and the first determination part 1002 is further configured to determine that the best prediction mode of the current point from the candidate prediction modes is the first prediction mode based on the value of the first syntax element information.

In some embodiments, the first determination part 1002 is further configured to determine whether the current point meets a preset condition based on the attribute reconstruction values of the respective reconstructed attributes of the M neighboring points of the current point; when the attribute reconstruction values of the respective reconstructed attributes of the M neighboring points of the current point meet the preset condition, perform the step of decoding the code stream and determining the first syntax element information of the current point; or, when the attribute reconstruction values of the respective reconstructed attributes of the M neighboring points of the current point do not meet the preset condition, determine that the current point adopts the second prediction mode.

In some embodiments, M is a positive integer greater than or equal to 2; the preset conditions include: the maximum attribute difference between the attribute reconstruction values of the reconstructed attributes of the M neighboring points is greater than or equal to a preset threshold; the reconstructed attribute is the same as the attribute to be decoded.

In some embodiments, the first determination part 1002 is further configured to, when it is determined that the current point adopts the second prediction mode, determine the spatial geometric weights of the M neighboring points according to the spatial positions of the M neighboring points and the spatial position of the current point; and determine the attribute prediction value of the attribute to be decoded according to the attribute reconstruction value of the reconstructed attribute of each of the M neighboring points and the spatial geometric weights of each of the M neighboring points.

In some embodiments, the decoding part 1001 is further configured to decode the code stream and determine third syntax element information.

In some embodiments, the first determining part 1002 is further configured to determine that the candidate prediction mode includes one or more cross-attribute prediction modes when the third syntax element information indicates that the current point allows the cross-attribute prediction mode; or When the third syntax element information indicates that the current point does not allow the cross-attribute prediction mode, it is determined that the candidate prediction modes include one or more non-cross-attribute prediction modes; and according to the value of the first syntax element information, the target prediction mode of the current point is determined from the one or more non-cross-attribute prediction modes.

In some embodiments, if the value of the third syntax element information is a third value, it is determined that the current point allows the use of a cross-attribute prediction mode; or, if the value of the third syntax element information is a fourth value, it is determined that the current point does not allow the use of a cross-attribute prediction mode.

It is understandable that in the embodiments of the present application, a "part" can be a part of a circuit, a part of a processor, a part of a program or software, etc., and of course it can also be a module, or it can be non-modular. Moreover, the various components in this embodiment can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into a single unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional modules.

If the integrated unit is implemented in the form of a software functional module and is not sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this embodiment, or the part that contributes to the existing technology, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the method described in this embodiment. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

Therefore, an embodiment of the present application provides a computer-readable storage medium, which is applied to the decoder 1000. The computer-readable storage medium stores a computer program, and when the computer program is executed by the first processor, it implements the method described in any one of the aforementioned embodiments.

Based on the above-mentioned components of the decoder 1000 and the computer-readable storage medium, refer to Figure 16, which shows a schematic diagram of the specific hardware structure of the decoder 1000 provided in an embodiment of the present application. As shown in Figure 16, the decoder 1000 may include: a first communication interface 1101, a first memory 1102, and a first processor 1103; these components are coupled together via a first bus system 1104. It will be understood that the first bus system 1104 is used to achieve connection and communication between these components. In addition to including a data bus, the first bus system 1104 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, in Figure 16, all various buses are labeled as the first bus system 1104.

in,

The first communication interface 1101 is used to receive and send signals when sending and receiving information with other external network elements;

A first memory 1102 is used to store computer programs that can be run on the first processor 1103;

The first processor 1103 is configured to, when running the computer program, execute:

Decode the code stream and determine the first syntax element information of the current point;

Determining, according to the value of the first syntax element information, the best prediction mode for the current point from the candidate prediction modes as the target cross-attribute prediction mode;

Determining one or more attribute reconstruction values of target neighboring points of the current point according to the target cross-attribute prediction mode;

Determining a correlation coefficient of the target neighboring point based on one or more attribute reconstruction values of the target neighboring point;

Determine an attribute prediction value of the attribute to be decoded at the current point according to the attribute reference value of the attribute to be decoded at the current point and the correlation coefficient.

It is understood that the first memory 1102 in the embodiment of the present application can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM (DRRAM). The first memory 1102 of the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

The first processor 1103 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit in the first processor 1103 or the instructions in the form of software. The above-mentioned first processor 1103 can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The various methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor Etc. The steps of the method disclosed in conjunction with the embodiments of this application can be directly implemented as execution by a hardware decoding processor, or can be implemented using a combination of hardware and software modules within the decoding processor. The software module can be located in a storage medium well-established in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the first memory 1102, and the first processor 1103 reads the information in the first memory 1102 and, in conjunction with its hardware, completes the steps of the above method.

It is understood that the embodiments described in this application can be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit can be implemented in one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described in this application, or a combination thereof. For software implementation, the technology described in this application can be implemented by modules (such as processes, functions, etc.) that perform the functions described in this application. The software code can be stored in a memory and executed by a processor. The memory can be implemented in the processor or outside the processor.

Optionally, as another embodiment, the first processor 1103 is further configured to execute any one of the methods described in the foregoing embodiments when running the computer program.

In another embodiment of the present application, based on the same inventive concept as the above embodiment, see FIG17 , which shows a schematic diagram of the composition structure of an encoder provided by an embodiment of the present application. As shown in FIG17 , the encoder 2000 may include a second determining part 2001 and an encoding part 2002; wherein,

The second determining part 2001 is configured to determine a candidate cross-attribute prediction mode for the current point from the candidate prediction modes; determine one or more attribute reconstruction values of candidate neighboring points of the current point based on the candidate cross-attribute prediction mode; determine the correlation coefficient of the candidate neighboring points based on the one or more attribute reconstruction values of the candidate neighboring points; determine the attribute prediction value of the attribute to be encoded at the current point based on the attribute reference value of the attribute to be encoded at the current point and the correlation coefficient; make a coding decision based on the attribute prediction values of the attribute to be encoded at the current point corresponding to the one or more candidate cross-attribute prediction modes, and determine the best prediction mode for the current point as the target cross-attribute prediction mode; and determine the value of the first syntax element information of the current point based on the best prediction mode;

The encoding part 2002 is configured to perform encoding processing on the first syntax element information and write the obtained encoded bits into a bitstream.

In some embodiments, the correlation coefficient represents the correlation between multiple attribute reconstruction values of the candidate neighboring points.

In some embodiments, the correlation coefficient includes a first coefficient; the second determining part 2001 is further configured to determine the first coefficient according to the first attribute reconstruction value of the candidate neighbor point and the second attribute reconstruction value of the candidate neighbor point.

In some embodiments, the first coefficient includes a ratio of a first attribute reconstruction value of the candidate neighbor point to a second attribute reconstruction value of the candidate neighbor point.

In some embodiments, when the attribute reconstruction order of the current point is that the first attribute precedes the second attribute, the attribute to be encoded is the second attribute, the first attribute reconstruction value of the candidate neighbor point is the reconstruction value of the second attribute of the candidate neighbor point, the second attribute reconstruction value of the candidate neighbor point is the reconstruction value of the first attribute of the candidate neighbor point, and the attribute reference value of the attribute to be encoded is the reconstruction value of the first attribute of the current point; or, when the attribute reconstruction order of the current point is that the second attribute precedes the first attribute, the attribute to be encoded is the first attribute, the first attribute reconstruction value of the candidate neighbor point is the reconstruction value of the first attribute of the candidate neighbor point, the second attribute reconstruction value of the candidate neighbor point is the reconstruction value of the second attribute of the candidate neighbor point, and the attribute reference value of the attribute to be encoded is the reconstruction value of the second attribute of the current point.

In some embodiments, the correlation coefficient represents the correlation between the attribute reconstruction value of the candidate neighbor point and the attribute reconstruction value of the current point.

In some embodiments, the correlation coefficient includes a second coefficient; the second determination part 2001 is further configured to determine the second coefficient according to the first attribute reconstruction value of the current point and the first attribute reconstruction value of the candidate neighboring point.

In some embodiments, the second coefficient includes a ratio of the first attribute reconstruction value of the current point to the first attribute reconstruction value of the candidate neighboring point.

In some embodiments, when the attribute reconstruction order of the current point is that the first attribute precedes the second attribute, the attribute to be encoded is the second attribute, the first attribute reconstruction value of the current point is the reconstruction value of the first attribute of the current point, the first attribute reconstruction value of the candidate neighboring point is the reconstruction value of the first attribute of the candidate neighboring point, and the attribute reference value of the attribute to be encoded is the reconstruction value of the second attribute of the candidate neighboring point; or, when the attribute reconstruction order of the current point is that the second attribute precedes the first attribute, the attribute to be encoded is the first attribute, the first attribute reconstruction value of the current point is the reconstruction value of the second attribute of the current point, the first attribute reconstruction value of the candidate neighboring point is the reconstruction value of the second attribute of the candidate neighboring point, and the attribute reference value of the attribute to be encoded is the reconstruction value of the first attribute of the candidate neighboring point.

In some embodiments, the second determination part 2001 is further configured to determine second grammatical element information; if the value of the second grammatical element information is a first value, the attribute reconstruction order of the current point is determined to be the first attribute before the second attribute; or, if the value of the second grammatical element information is a second value, the attribute reconstruction order of the current point is determined to be the second attribute before the first attribute.

In some embodiments, the encoding part 2002 is further configured to encode the second syntax identification information and write the obtained encoded bits into a bitstream.

In some embodiments, the correlation coefficient represents the correlation between the attribute reconstruction values of the candidate neighboring points and the attribute reconstruction values of the non-candidate neighboring points of the current point.

In some embodiments, the second determining portion 2001 is further configured to multiply the attribute reference value of the attribute to be encoded at the current point by the correlation coefficient to determine the attribute prediction value of the attribute to be encoded.

In some embodiments, the candidate prediction mode also includes a first prediction mode, and the second determination part 2001 is further configured to determine the first prediction mode of the current point from the candidate prediction mode; determine the attribute prediction value of the attribute to be encoded of the current point based on the first prediction mode; make an encoding decision based on the attribute prediction value of the attribute to be encoded of the current point corresponding to each of the one or more candidate cross-attribute prediction modes and the first prediction mode, and determine that the best prediction mode of the current point is the first prediction mode.

In some embodiments, the second determination part 2001 is further configured to determine whether the current point meets a preset condition based on the attribute reconstruction values of the reconstructed attributes of the M neighboring points of the current point; when the attribute reconstruction values of the reconstructed attributes of the M neighboring points of the current point meet the preset condition, perform the step of determining the candidate cross-attribute prediction mode of the current point from the candidate prediction modes, or, when the attribute reconstruction values of the reconstructed attributes of the M neighboring points of the current point do not meet the preset condition, determine that the current point adopts the second prediction mode.

In some embodiments, M is a positive integer greater than or equal to 2; the preset conditions include: the maximum attribute difference of the attribute reconstruction values of the reconstructed attributes between the M neighboring points is greater than or equal to a preset threshold; the reconstructed attribute is the same as the attribute to be encoded.

In some embodiments, the second determination part 2001 is further configured to, when it is determined that the current point adopts the second prediction mode, determine the spatial geometric weights of the M neighboring points according to the spatial positions of the M neighboring points and the spatial position of the current point; and determine the attribute prediction value of the attribute to be encoded according to the attribute reconstruction value of the reconstructed attribute of each of the M neighboring points and the spatial geometric weights of each of the M neighboring points.

In some embodiments, the second determination part 2001 is further configured to determine third syntax element information; when the third syntax element information indicates that the current point allows the use of a cross-attribute prediction mode, determine that the candidate prediction mode includes one or more cross-attribute prediction modes; or, when the third syntax element information indicates that the current point prohibits the use of a cross-attribute prediction mode, determine that the candidate prediction mode includes one or more non-cross-attribute prediction modes; determine one or more attribute prediction values of the current point based on the one or more non-cross-attribute prediction modes; determine the optimal prediction mode adopted by the current point based on the encoding decision of the one or more attribute prediction values of the current point; and determine the value of the first syntax element information based on the optimal prediction mode.

In some embodiments, the encoding part 2002 is further configured to perform encoding processing on the third syntax identification information and write the obtained encoded bits into the bitstream.

It is understood that in this embodiment, a "portion" may be a circuit portion, a processor portion, a program portion, or software portion, and may also be a module or non-modular. Furthermore, the various components in this embodiment may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional modules.

If the integrated unit is implemented as a software functional module and is not sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, this embodiment provides a computer-readable storage medium, which is applied to the encoder 2000 and stores a computer program. When the computer program is executed by the second processor, it implements any of the methods in the aforementioned embodiments.

Based on the composition of the above-mentioned encoder 2000 and the computer-readable storage medium, refer to Figure 18, which shows a specific hardware structure diagram of the encoder 2000 provided in an embodiment of the present application. As shown in Figure 18, the encoder 2000 may include: a second communication interface 2101, a second memory 2102 and a second processor 2103; each component is coupled together through a second bus system 2104. It can be understood that the second bus system 2104 is used to realize the connection and communication between these components. In addition to the data bus, the second bus system 2104 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, various buses are marked as the second bus system 2104 in Figure 18. Among them,

The second communication interface 2101 is used to receive and send signals during the process of sending and receiving information between other external network elements;

The second memory 2102 is used to store computer programs that can be run on the second processor 2103;

The second processor 2103 is configured to, when running the computer program, execute:

Determining a candidate cross-attribute prediction mode for the current point from candidate prediction modes;

Determining one or more attribute reconstruction values of candidate neighboring points of the current point according to the candidate cross-attribute prediction mode;

Determining a correlation coefficient of the candidate neighboring points based on one or more attribute reconstruction values of the candidate neighboring points;

Determining an attribute prediction value of the attribute to be encoded at the current point according to the attribute reference value of the attribute to be encoded at the current point and the correlation coefficient;

Performing a coding decision based on the attribute prediction values of the to-be-coded attribute of the current point corresponding to one or more candidate cross-attribute prediction modes, and determining the best prediction mode of the current point as a target cross-attribute prediction mode;

Determining, according to the optimal prediction mode, a value of the first syntax element information of the current point;

The first syntax element information is coded and the obtained coded bits are written into a bitstream.

Optionally, as another embodiment, the second processor 2103 is further configured to execute any one of the methods described in the foregoing embodiments when running the computer program.

It can be understood that the hardware functions of the second memory 2102 are similar to those of the first memory 1102, and the hardware functions of the second processor 2103 are similar to those of the first processor 1103; they will not be described in detail here.

In yet another embodiment of the present application, referring to FIG19 , a schematic diagram of the structure of a coding and decoding system provided by an embodiment of the present application is shown. As shown in FIG19 , the coding and decoding system 3000 may include a decoder 3001 and an encoder 3002 .

In the embodiment of the present application, the decoder 3001 may be the decoder described in any one of the aforementioned embodiments, and the encoder 3002 may be the encoder described in any one of the aforementioned embodiments.

It should be noted that, in this application, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

The serial numbers of the above embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

The methods disclosed in the several method embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments.

The features disclosed in the several product embodiments provided in this application can be arbitrarily combined without conflict to obtain new product embodiments.

The features disclosed in the several method or device embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments or device embodiments.

The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the embodiments of the present application.

Claims (41)

A decoding method, applied to a decoder, comprising: Decode the code stream and determine the first syntax element information of the current point; Determining, according to the value of the first syntax element information, the best prediction mode for the current point from the candidate prediction modes as the target cross-attribute prediction mode; Determining one or more attribute reconstruction values of target neighboring points of the current point according to the target cross-attribute prediction mode; Determining a correlation coefficient of the target neighboring point based on one or more attribute reconstruction values of the target neighboring point; Determine an attribute prediction value of the attribute to be decoded at the current point according to the attribute reference value of the attribute to be decoded at the current point and the correlation coefficient. The method according to claim 1, wherein the correlation coefficient represents the correlation between multiple attribute reconstruction values of the target neighboring points. The method according to claim 2, wherein the correlation coefficient includes a first coefficient; and determining the correlation coefficient of the target neighbor point based on one or more attribute reconstruction values of the target neighbor point comprises: The first coefficient is determined according to the first attribute reconstruction value of the target neighboring point and the second attribute reconstruction value of the target neighboring point. The method according to claim 3, wherein the first coefficient comprises a ratio of a first attribute reconstructed value of the target neighbor point to a second attribute reconstructed value of the target neighbor point. The method according to claim 4, wherein When the attribute reconstruction order of the current point is that the first attribute precedes the second attribute, the attribute to be decoded is the second attribute, the first attribute reconstruction value of the target neighboring point is the reconstructed value of the second attribute of the target neighboring point, the second attribute reconstruction value of the target neighboring point is the reconstructed value of the first attribute of the target neighboring point, and the attribute reference value of the attribute to be decoded is the reconstructed value of the first attribute of the current point; or When the attribute reconstruction order of the current point is that the second attribute precedes the first attribute, the attribute to be decoded is the first attribute, the first attribute reconstruction value of the target neighboring point is the reconstructed value of the first attribute of the target neighboring point, the second attribute reconstruction value of the target neighboring point is the reconstructed value of the second attribute of the target neighboring point, and the attribute reference value of the attribute to be decoded is the reconstructed value of the second attribute of the current point. The method according to claim 1, wherein the correlation coefficient represents the correlation between the attribute reconstructed value of the target neighbor point and the attribute reconstructed value of the current point. The method according to claim 6, wherein the correlation coefficient includes a second coefficient; and determining the correlation coefficient of the target neighbor point based on one or more attribute reconstruction values of the target neighbor point comprises: A second coefficient is determined according to the first attribute reconstruction value of the current point and the first attribute reconstruction value of the target neighboring point. The method according to claim 7, wherein the second coefficient comprises a ratio of the first attribute reconstructed value of the current point to the first attribute reconstructed value of the target neighboring point. The method according to claim 8, wherein When the attribute reconstruction order of the current point is that the first attribute precedes the second attribute, the attribute to be decoded is the second attribute, the first attribute reconstruction value of the current point is the reconstructed value of the first attribute of the current point, the first attribute reconstruction value of the target neighboring point is the reconstructed value of the first attribute of the target neighboring point, and the attribute reference value of the attribute to be decoded is the reconstructed value of the second attribute of the target neighboring point; or When the attribute reconstruction order of the current point is that the second attribute precedes the first attribute, the attribute to be decoded is the first attribute, the first attribute reconstruction value of the current point is the reconstruction value of the second attribute of the current point, the first attribute reconstruction value of the target neighboring point is the reconstruction value of the second attribute of the target neighboring point, and the attribute reference value of the attribute to be decoded is the reconstruction value of the first attribute of the target neighboring point. The method according to claim 5 or 9, wherein the method further comprises: Parsing the code stream to determine the second syntax element information; If the value of the second syntax element information is the first value, determining that the attribute reconstruction order of the current point is that the first attribute precedes the second attribute; or If the value of the second syntax element information is the second value, it is determined that the attribute reconstruction order of the current point is that the second attribute precedes the first attribute. The method according to any one of claims 1 to 10, wherein the correlation coefficient represents the correlation between the attribute reconstructed value of the target neighbor point and the attribute reconstructed value of the non-target neighbor point of the current point. The method according to any one of claims 1 to 11, wherein determining the attribute prediction value of the attribute to be decoded at the current point based on the attribute reference value of the attribute to be decoded at the current point and the correlation coefficient comprises: The attribute reference value of the attribute to be decoded at the current point is multiplied by the correlation coefficient to determine the attribute prediction value of the attribute to be decoded. The method according to claim 1, wherein the candidate prediction mode further includes a first prediction mode, and the method further includes: According to the value of the first syntax element information, the best prediction mode for the current point is determined from the candidate prediction modes as the first prediction mode. The method according to any one of claims 1 to 13, wherein the method further comprises: Determining whether the current point meets a preset condition based on the attribute reconstruction values of the reconstructed attributes of the M neighboring points of the current point; When the reconstructed attribute values of the respective reconstructed attributes of the M neighboring points of the current point meet a preset condition, performing a step of decoding the code stream to determine the first syntax element information of the current point; or When the attribute reconstruction values of the reconstructed attributes of the M neighboring points of the current point do not meet a preset condition, it is determined that the current point adopts the second prediction mode. The method according to claim 14, wherein M is a positive integer greater than or equal to 2; the preset conditions include: a maximum attribute difference between the attribute reconstruction values of the reconstructed attributes of the M neighboring points is greater than or equal to a preset threshold; and the reconstructed attribute is the same as the attribute to be decoded. The method according to claim 14, wherein the method further comprises: When it is determined that the second prediction mode is adopted for the current point, determining a spatial geometric weight of each of the M neighboring points according to the spatial positions of each of the M neighboring points and the spatial position of the current point; An attribute prediction value of the attribute to be decoded is determined according to the attribute reconstruction value of each of the reconstructed attributes of the M neighboring points and the spatial geometric weight of each of the M neighboring points. The method according to any one of claims 1 to 16, wherein the method further comprises: Decoding the code stream to determine third syntax element information; In a case where the third syntax element information indicates that the current point allows the use of the cross-attribute prediction mode, determining that the candidate prediction modes include one or more cross-attribute prediction modes; or In a case where the third syntax element information indicates that the current point does not allow the cross-attribute prediction mode to be adopted, determining that the candidate prediction modes include one or more non-cross-attribute prediction modes; According to the value of the first syntax element information, the optimal prediction mode for the current point is determined from the one or more non-cross-attribute prediction modes. The method according to claim 17, wherein the method further comprises: If the value of the third syntax element information is the third value, it is determined that the current point allows the cross-attribute prediction mode; or If the value of the third syntax element information is the fourth value, it is determined that the cross-attribute prediction mode is not allowed to be adopted at the current point. A coding method, applied to an encoder, comprising: Determining a candidate cross-attribute prediction mode for the current point from candidate prediction modes; Determining one or more attribute reconstruction values of candidate neighboring points of the current point according to the candidate cross-attribute prediction mode; Determining a correlation coefficient of the candidate neighboring points based on one or more attribute reconstruction values of the candidate neighboring points; Determining an attribute prediction value of the attribute to be encoded at the current point according to the attribute reference value of the attribute to be encoded at the current point and the correlation coefficient; Performing a coding decision based on the attribute prediction values of the to-be-coded attribute of the current point corresponding to one or more candidate cross-attribute prediction modes, and determining the best prediction mode of the current point as a target cross-attribute prediction mode; Determining, according to the optimal prediction mode, a value of the first syntax element information of the current point; The first syntax element information is coded and the obtained coded bits are written into a bitstream. The method according to claim 19, wherein the correlation coefficient represents the correlation between multiple attribute reconstruction values of the candidate neighbor points. The method according to claim 20, wherein the correlation coefficient includes a first coefficient; and determining the correlation coefficient of the candidate neighboring points based on one or more attribute reconstruction values of the candidate neighboring points comprises: The first coefficient is determined according to the first attribute reconstruction value of the candidate neighbor point and the second attribute reconstruction value of the candidate neighbor point. The method according to claim 21, wherein the first coefficient comprises a ratio of a first attribute reconstruction value of the candidate neighbor point to a second attribute reconstruction value of the candidate neighbor point. The method according to claim 22, wherein When the attribute reconstruction order of the current point is that the first attribute precedes the second attribute, the attribute to be encoded is the second attribute, the first attribute reconstruction value of the candidate neighbor point is the reconstructed value of the second attribute of the candidate neighbor point, the second attribute reconstruction value of the candidate neighbor point is the reconstructed value of the first attribute of the candidate neighbor point, and the attribute reference value of the attribute to be encoded is the reconstructed value of the first attribute of the current point; or When the attribute reconstruction order of the current point is that the second attribute precedes the first attribute, the attribute to be encoded is the first attribute, the first attribute reconstruction value of the candidate neighbor point is the reconstructed value of the first attribute of the candidate neighbor point, the second attribute reconstruction value of the candidate neighbor point is the reconstructed value of the second attribute of the candidate neighbor point, and the attribute reference value of the attribute to be encoded is the reconstructed value of the second attribute of the current point. The method according to claim 19, wherein the correlation coefficient represents the correlation between the attribute reconstructed value of the candidate neighboring point and the attribute reconstructed value of the current point. The method according to claim 24, wherein the correlation coefficient includes a second coefficient; and determining the correlation coefficient of the candidate neighboring point based on one or more attribute reconstruction values of the candidate neighboring point comprises: A second coefficient is determined according to the first attribute reconstruction value of the current point and the first attribute reconstruction value of the candidate neighboring point. The method according to claim 25, wherein the second coefficient comprises a ratio of the first attribute reconstruction value of the current point to the first attribute reconstruction value of the candidate neighboring point. The method according to claim 26, wherein When the attribute reconstruction order of the current point is that the first attribute precedes the second attribute, the attribute to be encoded is the second attribute, the first attribute reconstruction value of the current point is the reconstructed value of the first attribute of the current point, the first attribute reconstruction value of the candidate neighboring point is the reconstructed value of the first attribute of the candidate neighboring point, and the attribute reference value of the attribute to be encoded is the reconstructed value of the second attribute of the candidate neighboring point; or When the attribute reconstruction order of the current point is that the second attribute precedes the first attribute, the attribute to be encoded is the first attribute, the first attribute reconstruction value of the current point is the reconstruction value of the second attribute of the current point, the first attribute reconstruction value of the candidate neighboring point is the reconstruction value of the second attribute of the candidate neighboring point, and the attribute reference value of the attribute to be encoded is the reconstruction value of the first attribute of the candidate neighboring point. The method according to claim 23 or 27, wherein the method further comprises: determining second syntax element information; If the value of the second syntax element information is the first value, determining that the attribute reconstruction order of the current point is that the first attribute precedes the second attribute; or If the value of the second syntax element information is the second value, determining that the attribute reconstruction order of the current point is such that the second attribute precedes the first attribute; The method further comprises: The second syntax identification information is coded, and the obtained coded bits are written into a bitstream. The method according to any one of claims 19 to 28, wherein the correlation coefficient represents the correlation between the attribute reconstructed values of the candidate neighboring points and the attribute reconstructed values of the non-candidate neighboring points of the current point. The method according to any one of claims 19 to 29, wherein determining the attribute prediction value of the attribute to be encoded at the current point based on the attribute reference value of the attribute to be encoded at the current point and the correlation coefficient comprises: The attribute reference value of the attribute to be encoded at the current point is multiplied by the correlation coefficient to determine the attribute prediction value of the attribute to be encoded. The method according to claim 1, wherein the candidate prediction mode further includes a first prediction mode, and the method further includes: Determining the first prediction mode of the current point from the candidate prediction modes; determining, according to the first prediction mode, an attribute prediction value of the attribute to be encoded at the current point; An encoding decision is made based on the attribute prediction values of the to-be-encoded attributes of the current point corresponding to each of the one or more candidate cross-attribute prediction modes and the first prediction mode, and the best prediction mode for the current point is determined to be the first prediction mode. The method according to any one of claims 19 to 31, further comprising: Determining whether the current point meets a preset condition based on the attribute reconstruction values of the reconstructed attributes of the M neighboring points of the current point; When the attribute reconstruction values of the reconstructed attributes of the M neighboring points of the current point meet a preset condition, performing a step of determining a candidate cross-attribute prediction mode of the current point from the candidate prediction modes, or When the attribute reconstruction values of the reconstructed attributes of the M neighboring points of the current point do not meet a preset condition, it is determined that the current point adopts the second prediction mode. The method according to claim 32, wherein M is a positive integer greater than or equal to 2; the preset conditions include: the maximum attribute difference of the attribute reconstruction values of the reconstructed attributes between the M neighboring points is greater than or equal to a preset threshold; and the reconstructed attribute is the same as the attribute to be encoded. The method according to claim 32, wherein the method further comprises: When it is determined that the second prediction mode is adopted for the current point, determining a spatial geometric weight of each of the M neighboring points according to the spatial positions of each of the M neighboring points and the spatial position of the current point; An attribute prediction value of the attribute to be encoded is determined according to the attribute reconstruction value of each of the reconstructed attributes of the M neighboring points and the spatial geometric weight of each of the M neighboring points. The method according to any one of claims 19 to 34, further comprising: determining third syntax element information; In a case where the third syntax element information indicates that the current point allows the use of the cross-attribute prediction mode, determining that the candidate prediction modes include one or more cross-attribute prediction modes; or In a case where the third syntax element information indicates that the current point prohibits the cross-attribute prediction mode, determining that the candidate prediction modes include one or more non-cross-attribute prediction modes; determining one or more attribute prediction values of the current point according to the one or more non-cross-attribute prediction modes; Determining an optimal prediction mode for the current point based on one or more attribute prediction value encoding decisions of the current point; Determining a value of the first syntax element information according to the optimal prediction mode; performing encoding processing on the first syntax element information, and writing the obtained encoding bits into a bitstream; The method further comprises: The third syntax identification information is coded, and the obtained coded bits are written into the bitstream. A code stream is generated by bit-coding information to be coded; wherein the information to be coded includes at least one of the following: First syntax element information, second syntax element information, and third syntax element information; the first syntax element information is used to indicate the prediction mode adopted by the current point, the second syntax element information is used to indicate the attribute reconstruction order of the current point, and the third syntax element information is used to indicate whether the current point allows the use of a cross-attribute prediction mode. A decoder comprising a decoding part and a first determining part, wherein: The decoding part is configured to decode the code stream and determine the first syntax element information of the current point; The first determination part is configured to determine, from the candidate prediction modes, the best prediction mode of the current point as the target cross-attribute prediction mode according to the value of the first syntax element information; determine one or more attribute reconstruction values of the target neighboring points of the current point according to the target cross-attribute prediction mode; determine the correlation coefficient of the target neighboring points according to the one or more attribute reconstruction values of the target neighboring points; and determine the attribute prediction value of the attribute to be decoded of the current point according to the attribute reference value of the attribute to be decoded of the current point and the correlation coefficient. An encoder comprising an encoding part and a second determining part, wherein: The second determination part is configured to determine the candidate cross-attribute prediction mode of the current point from the candidate prediction modes; determine one or more attribute reconstruction values of the candidate neighboring points of the current point according to the candidate cross-attribute prediction mode; determine the correlation coefficient of the candidate neighboring points according to the one or more attribute reconstruction values of the candidate neighboring points; determine the attribute prediction value of the attribute to be encoded at the current point according to the attribute reference value of the attribute to be encoded at the current point and the correlation coefficient; make a coding decision based on the attribute prediction value of the attribute to be encoded at the current point corresponding to the one or more candidate cross-attribute prediction modes, and determine the best prediction mode of the current point as the target cross-attribute prediction mode; determine the value of the first syntax element information of the current point according to the best prediction mode; The encoding part is configured to perform encoding processing on the first syntax element information and write the obtained encoded bits into a bitstream. A decoder comprising a first memory and a first processor, wherein: The first memory is configured to store a computer program that can be executed on the first processor; The first processor is configured to perform the method according to any one of claims 1 to 18 when running the computer program. An encoder comprising a second memory and a second processor, wherein: The second memory is configured to store a computer program that can be executed on the second processor; The second processor is configured to perform the method according to any one of claims 19 to 35 when running the computer program. A computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed, the method according to any one of claims 1 to 18 is implemented, or the method according to any one of claims 19 to 35 is implemented.