WO2023159428A1 - Encoding method, encoder, and storage medium - Google Patents

Abstract

Embodiments of the present application provide an encoding method, an encoder, and a storage medium. The method comprises: determining an encoding sequence of attribute information of the current point cloud on the basis of geometric information of the current point cloud; determining a first quantized residual value of the current point on the basis of at least one neighbor point before the current point to be encoded in the encoding sequence; determining at least one second quantized residual value of the current point; determining a third quantized residual value according to the first quantized residual value and the at least one second quantized residual value; and encoding the third quantized residual value to obtain a code stream. The encoding method provided by the present application can improve the encoding performance of the encoder.

Description

Encoding method, encoder and storage medium technical field

The embodiments of the present application relate to the technical field of encoding and decoding, and more specifically, relate to an encoding method, an encoder, and a storage medium.

Background technique

Point cloud has begun to be popularized in various fields, for example, virtual/augmented reality, robotics, geographic information system, medical field, etc. With the continuous improvement of the benchmark and speed of scanning equipment, a large number of point clouds on the surface of objects can be accurately obtained, often corresponding to hundreds of thousands of points in one scene. Such a large number of points also brings challenges to computer storage and transmission. Therefore, point compression has become a hot issue.

For point cloud compression, the encoder needs to compress its position information and attribute information; specifically, the encoder first performs octree encoding on the position information of the point cloud; at the same time, the encoder encodes the position information according to the octree The position information of the current point selects the neighbor points used to predict the attribute value of the current point from the encoded points, and predicts the current point with reference to the selected neighbor points to obtain the attribute prediction value of the current point, and then based on the current point The predicted value of the attribute of the current point and the original value of the attribute of the current point are calculated to obtain the residual value of the current point, and then the encoder quantizes the residual value of the current point to obtain the quantized residual value, and finally the encoder converts the quantized residual value of the current point The value is transmitted to the decoding end in the form of a code stream; the decoding end can obtain the quantized residual value of the current point by receiving and analyzing the code stream, and obtain the residual value of the current point through steps such as inverse transformation and inverse quantization of the quantized residual value , the decoding end uses the same process to predict the attribute prediction value, which is superimposed with the residual value obtained by parsing the code stream to obtain the attribute reconstruction value of the current point.

Usually, after the encoder predicts the current point with reference to the selected neighbor points to obtain the attribute prediction value of the current point, it needs to use the attribute reconstruction value of the selected neighbor points to perform differential prediction on the attribute of the current point to obtain the current point Then the encoder quantizes and encodes the residual value of the current point. However, in this encoding process, since the encoder uses the same quantization parameter for all points in the same point cloud sequence for quantization encoding, it will result in low encoding efficiency or low reconstruction quality for some points, thereby reducing the The encoding performance of the encoder.

Contents of the invention

Embodiments of the present application provide an encoding method, an encoder, and a storage medium, which can improve the encoding performance of the encoder.

In a first aspect, the present application provides an encoding method, including:

Based on the geometric information of the current point cloud, determine the encoding sequence of the attribute information of the current point cloud;

Determining a first quantized residual value of the current point based on at least one neighbor point before the current point to be encoded in the encoding order;

determining at least one second quantized residual value for the current point;

determining a third quantized residual value based on the first quantized residual value and the at least one second quantized residual value;

Encoding the third quantized residual value to obtain a code stream.

In a second aspect, the present application provides an encoder, including:

Identify units for:

Based on the geometric information of the current point cloud, determine the encoding sequence of the attribute information of the current point cloud;

Determining a first quantized residual value of the current point based on at least one neighbor point before the current point to be encoded in the encoding order;

determining at least one second quantized residual value for the current point;

determining a third quantized residual value based on the first quantized residual value and the at least one second quantized residual value;

A coding unit, configured to code the third quantized residual value to obtain a code stream.

In a third aspect, the present application provides an encoding device, including:

a processor adapted to implement computer instructions; and,

A computer-readable storage medium, the computer-readable storage medium stores computer instructions, and the computer instructions are adapted to be loaded by a processor and execute the encoding method in the above first aspect or its various implementations.

In an implementation manner, there are one or more processors, and one or more memories.

In an implementation manner, the computer-readable storage medium may be integrated with the processor, or the computer-readable storage medium may be provided separately from the processor.

In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, the computer-readable storage medium stores computer instructions, and when the computer instructions are read and executed by a processor of a computer device, the computer device executes the above-mentioned first step. An encoding method in an aspect or implementations thereof.

In the fifth aspect, the embodiment of the present application provides a code stream, the code stream is as the code stream generated by the method described in the first aspect or its various implementations.

Based on the above technical solution, when the present application determines the quantized residual value of the final encoded code stream for the current point, the first quantized residual value determined based on at least one neighbor point before the current point to be encoded in the encoding order is considered On this basis, at least one second quantized residual value is also introduced, because when the current point adopts different quantized residual values, it indicates that the quantization scale of the current point is different; therefore, the encoder first uses the first quantized residual value and the The third quantized residual value is determined by at least one second quantized residual value, and then the third quantized residual value is encoded, which can not only prevent the encoder from using the same quantized scale for all points in the current point cloud to perform quantized encoding , it is also beneficial to select an appropriate quantization scale for the points in the current point cloud, which can avoid the low coding efficiency or low reconstruction quality of some points, and improve the coding performance of the encoder.

Description of drawings

Fig. 1 is an example of a point cloud image provided by an embodiment of the present application.

FIG. 2 is a partially enlarged view of the point cloud image shown in FIG. 1 .

Fig. 3 is an example of point cloud images with six viewing angles provided by the embodiment of the present application.

Fig. 4 is a schematic block diagram of a coding framework provided by an embodiment of the present application.

Fig. 5 is an example of a bounding box provided by an embodiment of the present application.

Fig. 6 is an example of performing octree division on a bounding box provided by an embodiment of the present application.

Figures 7 to 9 show the arrangement order of Morton codes in two-dimensional space.

Fig. 10 shows the arrangement order of Morton codes in three-dimensional space.

Fig. 11 is a schematic block diagram of an LOD layer provided by an embodiment of the present application.

Fig. 12 is a schematic block diagram of a decoding framework provided by an embodiment of the present application.

Fig. 13 is another schematic block diagram of a coding framework provided by an embodiment of the present application.

Fig. 14 is another schematic block diagram of a decoding framework provided by an embodiment of the present application.

Fig. 15 is a schematic flowchart of an encoding method provided by an embodiment of the present application.

Fig. 16 is a schematic structural diagram of an attribute code stream provided by an embodiment of the present application.

Fig. 17 is a schematic structural diagram of a payload in an attribute code stream provided by an embodiment of the present application.

Fig. 18 is another schematic flowchart of the encoding method provided by the embodiment of the present application.

Fig. 19 is a schematic flowchart of a decoding method provided by an embodiment of the present application.

Fig. 20 is a schematic flowchart of an encoder provided by an embodiment of the present application.

Fig. 21 is another schematic flowchart of the encoder provided by the embodiment of the present application.

Detailed ways

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

A point cloud is a set of discrete point sets randomly distributed in space that express the spatial structure and surface properties of a 3D object or 3D scene. Figure 1 and Figure 2 show the 3D point cloud image and local enlarged view respectively, and it can be seen that the point cloud surface is composed of densely distributed points.

The two-dimensional image has information expression at each pixel point, so there is no need to additionally record its position information; however, the distribution of points in the point cloud in the three-dimensional space is random and irregular, so it is necessary to record the location of each point in the space In order to fully express a point cloud. Similar to a two-dimensional image, each point in the point cloud has corresponding attribute information, usually an RGB color value, and the color value reflects the color of the object; for the point cloud, the attribute information corresponding to each point is in addition to the color , it can also be a reflectance value, which reflects the surface material of the object. Each point in the point cloud can include geometric information and attribute information, wherein the geometric information of each point in the point cloud refers to the Cartesian three-dimensional coordinate data of the point, and the attribute information of each point in the point cloud can include but not limited to At least one of the following: color information, material information, and laser reflection intensity information. The color information can be information on any color space. For example, the color information may be Red Green Blue (RGB) information. For another example, the color information may also be brightness and chrominance (YCbCr, YUV) information. Among them, Y represents brightness (Luma), Cb (U) represents a blue chroma component, and Cr (V) represents a red chroma component. Each point in the point cloud has the same amount of attribute information. For example, each point in the point cloud has two attribute information, color information and laser reflection intensity. For another example, each point in the point cloud has three attribute information: color information, material information and laser reflection intensity information.

The point cloud image can have multiple viewing angles, for example, the point cloud image shown in Figure 3 can have six viewing angles, the data storage format corresponding to the point cloud image consists of a file header information part and a data part, the header information It includes the data format, data representation type, total point cloud points, and the content represented by the point cloud.

Point cloud can flexibly and conveniently express the spatial structure and surface properties of three-dimensional objects or scenes, and because point cloud is obtained by directly sampling real objects, it can provide a strong sense of reality under the premise of ensuring accuracy, so it is widely used. Including virtual reality games, computer-aided design, geographic information systems, automatic navigation systems, digital cultural heritage, free viewpoint broadcasting, 3D immersive telepresence, 3D reconstruction of biological tissues and organs, etc.

Exemplarily, point clouds can be divided into two categories based on application scenarios, namely, machine-perceived point clouds and human-eye-perceived point clouds. The application scenarios of machine perception point cloud include but are not limited to: autonomous navigation system, real-time inspection system, geographic information system, visual sorting robot, emergency rescue robot and other point cloud application scenarios. The application scenarios of point cloud perceived by the human eye include but are not limited to: digital cultural heritage, free viewpoint broadcasting, 3D immersive communication, 3D immersive interaction and other point cloud application scenarios. Correspondingly, the point cloud can be divided into dense point cloud and sparse point cloud based on the acquisition method of the point cloud; the point cloud can also be divided into static point cloud and dynamic point cloud based on the acquisition method of the point cloud, more specifically, it can be It is divided into three types of point clouds, namely, the first static point cloud, the second type of dynamic point cloud and the third type of dynamically acquired point cloud. For the first static point cloud, the object is stationary, and the device for obtaining the point cloud is also stationary; for the second type of dynamic point cloud, the object is moving, but the device for obtaining the point cloud is stationary; for the third type of dynamic Obtaining point cloud, the equipment for obtaining point cloud is in motion.

Exemplarily, the way of collecting the point cloud includes but not limited to: computer generation, 3D laser scanning, 3D photogrammetry and the like. Computers can generate point clouds of virtual three-dimensional objects and scenes; 3D laser scanning can obtain point clouds of static real-world three-dimensional objects or scenes, and can obtain millions of point clouds per second; 3D photogrammetry can obtain dynamic real-world three-dimensional objects or scenes The point cloud of tens of millions of points can be obtained per second. Specifically, the point cloud of the surface of the object can be collected through acquisition equipment such as photoelectric radar, lidar, laser scanner, and multi-view camera. The point cloud obtained according to the laser measurement principle may include the three-dimensional coordinate information of the point and the laser reflection intensity (reflectance) of the point. The point cloud obtained according to the principle of photogrammetry may include the three-dimensional coordinate information of the point and the color information of the point. The point cloud is obtained by combining the principles of laser measurement and photogrammetry, which may include the three-dimensional coordinate information of the point, the laser reflection intensity (reflectance) of the point and the color information of the point. These technologies reduce the cost and time period of point cloud data acquisition, and improve the accuracy of the data. For example, in the medical field, point clouds of biological tissues and organs can be obtained from magnetic resonance imaging (MRI), computed tomography (CT), and electromagnetic positioning information. These technologies reduce the acquisition cost and time period of point cloud, and improve the accuracy of data. The transformation of the point cloud data acquisition method has made it possible to acquire a large amount of point cloud data. With the growth of application requirements, the processing of massive 3D point cloud data encounters the bottleneck of storage space and transmission bandwidth limitations.

Taking a point cloud video with a frame rate of 30fps (frame per second) as an example, the number of points in each frame of point cloud is 700,000, and each point in each frame of point cloud has coordinate information xyz (float) and color information RGB (uchar), the data volume of the 10s-length point cloud video is about 0.7 million (million)×(4Byte×3+1Byte×3)×30fps×10s=3.15GB, and the YUV sampling format is 4:2:0 , 1280×720 two-dimensional video with a frame rate of 24fps, the data volume of 10s is about 1280×720×12bit×24frames×10s≈0.33GB, and the data volume of 10s two-view 3D video is about 0.33×2=0.66GB . It can be seen that the data volume of point cloud video far exceeds the data volume of 2D video and 3D video of the same duration. 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 to promote the development of the point cloud industry.

Point cloud compression generally uses point cloud geometric information and attribute information to compress separately. Attribute compression of point cloud; at the decoding end, the geometric information of the point cloud is first decoded in the geometry decoder, and then the decoded geometric information is input into the attribute decoder as additional information to assist the attribute compression of the point cloud. The entire codec consists of preprocessing/postprocessing, geometric encoding/decoding, and attribute encoding/decoding.

Exemplarily, point clouds can be encoded and decoded by various types of encoding frameworks and decoding frameworks, respectively. As an example, the codec framework may be the Geometry Point Cloud Compression (G-PCC) codec framework or Video Point Cloud Compression (Video Point Cloud Compression) provided by the Moving Picture Experts Group (MPEG). , V-PCC) codec framework, or the AVS-PCC codec framework or Point Cloud Compression Reference Platform (PCRM) framework provided by the Audio Video Standard (AVS) task force. The G-PCC codec framework can be used to compress the first static point cloud and the third type of dynamically acquired point cloud, and the V-PCC codec framework can be used to compress the second type of dynamic point cloud. The G-PCC codec framework is also called point cloud codec TMC13, and the V-PCC codec framework is also called point cloud codec TMC2. Both G-PCC and AVS-PCC are aimed at static sparse point clouds, and their coding frameworks are roughly the same.

The codec framework applicable to the embodiment of the present application will be described below by taking the G-PCC framework as an example.

Fig. 4 is a schematic block diagram of a coding framework provided by an embodiment of the present application.

As shown in FIG. 4 , the encoding framework 100 can acquire the position information and attribute information of the point cloud from the acquisition device. The encoding of point cloud includes location encoding and attribute encoding. In one embodiment, the position encoding process includes: performing preprocessing on the original point cloud such as coordinate transformation, quantization and removing duplicate points, etc.; building an octree and encoding to form a geometric code stream.

As shown in Figure 4, the position encoding process of the encoder can be realized by the following units:

Coordinate transformation (Tanmsform coordinates) unit 101, quantization and removal of repeated points (Quantize and remove points) unit 102, octree analysis (Analyze octree) unit 103, geometric reconstruction (Reconstruct geometry) unit 104 and first arithmetic coding (Arithmetic encode) unit 105.

The coordinate transformation unit 101 can be used to transform the world coordinates of points in the point cloud into relative coordinates. For example, subtracting the minimum values of the xyz coordinate axes from the geometric coordinates of the point is equivalent to a DC operation to transform the coordinates of the points in the point cloud from world coordinates to relative coordinates. Quantization and removal of duplicate points unit 102 can reduce the number of coordinates by quantization; original different points may be given the same coordinates after quantization, based on this, repeated points can be deleted through de-duplication operations; for example, with the same quantization position and Multiple clouds with different attribute information can be merged into one cloud through attribute transformation. In some embodiments of the present application, the Quantize and Remove Duplicate Points unit 102 is an optional unit module. The octree analysis unit 103 may use an octree encoding method to encode the position information of the quantized points. For example, the point cloud is regularized in the form of an octree, so that the position of the point can correspond to the position of the octree one by one, by counting the position of the point in the octree, and marking it (flag) Recorded as 1 for geometric encoding. The first arithmetic coding unit 105 can perform arithmetic coding on the position information output by the octree analysis unit 103 using an entropy coding method, that is, the position information output by the octree analysis unit 103 is generated using an arithmetic coding method to generate a geometric code stream; the geometric code stream is also Can be called geometry bit stream (geometry bit stream).

The regularization processing method of the point cloud is described below.

Due to the irregular distribution of point clouds in space, it brings challenges to the encoding process. Therefore, the recursive octree structure is used to express the points in the point cloud as the center of the cube in a regular way. For example, as shown in Figure 5, the entire point cloud can be placed in a cube bounding box. At this time, the coordinates of the points in the point cloud can be expressed as (x ^k , y ^k , z ^k ), k=0,...,K -1, where K is the total number of points in the point cloud, then the boundary values of the point cloud in the x-axis, y-axis and z-axis directions are:

x ^min =min(x ⁰ ,x ¹ ,…,x ^K-1 );

y ^min =min(y ⁰ ,y ¹ ,...,y ^K-1 );

z ^min =min(z ⁰ ,z ¹ ,...,z ^K-1 );

x ^max = max(x ⁰ ,x ¹ ,...,x ^K-1 );

y ^max = max(y ⁰ ,y ¹ ,...,y ^K-1 );

z ^max = max(z ⁰ , z ¹ , . . . , z ^K−1 ).

Furthermore, the origin (x ^origin , y ^origin , z ^origin ) of the bounding box can be calculated as follows:

x ^origin = int(floor(x ^min ));

y ^origin = int(floor(y ^min ));

z ^origin = int(floor(z ^min )).

Among them, floor() represents the calculation of rounding down or rounding down. int() means rounding operation.

Based on this, the encoder can calculate the size of the bounding box in the x-axis, y-axis, and z-axis directions based on the calculation formula of the boundary value and the origin as follows:

BoudingBoxSize_x=int(x ^max -x ^origin )+1;

BoudingBoxSize_y=int(y ^max -y ^origin )+1;

BoudingBoxSize_z=int(z ^max -z ^origin )+1.

As shown in Figure 6, after the encoder obtains the size of the bounding box in the x-axis, y-axis, and z-axis directions, it first divides the bounding box into an octree, and obtains eight sub-blocks each time. Empty blocks (blocks containing points) are divided into octrees again, so recursively divided up to a certain depth, and the non-empty sub-blocks of the final size are called voxels, and each voxel contains one or more points , normalize the geometric positions of these points to the center point of the voxel, and the attribute value of the center point is the average value of the attribute values of all points in the voxel. Regularizing the point cloud into blocks in space is beneficial to describe the positional relationship between the points in the point cloud and the previous points, and then helps to design a specific encoding order. Based on this encoder, each voxel can be encoded based on the determined encoding order ( voxel), which encodes the point (or "node") represented by each voxel.

After the geometric encoding is completed, the encoder reconstructs the geometric information, and uses the reconstructed geometric information to encode the attribute information. The attribute coding process includes: by given the reconstruction information of the position information of the input point cloud and the real value of the attribute information, select one of the three prediction modes for point cloud prediction, quantify the predicted results, and perform arithmetic coding to form property stream.

As shown in Figure 4, the attribute encoding process of the encoder can be realized by the following units:

Color space transformation (Transform colors) unit 110, attribute transformation (Transfer attributes) unit 111, region adaptive layered transformation (Region Adaptive Hierarchical Transform, RAHT) unit 112, prediction change (predicting transform) unit 113 and lifting transform (lifting transform) ) unit 114, a quantization (Quantize) unit 115, and a second arithmetic coding unit 116.

The color space transformation unit 110 can be used to transform the RGB color space of points in the point cloud into YCbCr format or other formats. The attribute transformation unit 111 can be used to transform attribute information of points in the point cloud to minimize attribute distortion. For example, in the case of geometric lossy encoding, since the geometric information changes after geometric encoding, the attribute transformation unit 111 is required to reassign attribute values for each point after geometric encoding, so that the reconstruction point cloud and the original point cloud Attribute errors are minimal. For example, the attribute information may be color information of dots. The attribute transformation unit 111 can be used to obtain the original attribute value of the point. After the original attribute value of the point is transformed by the attribute transformation unit 111, any prediction unit can be selected to predict the point in the point cloud. The unit for predicting points in the point cloud may include: at least one of RAHT 112, predicting transform unit 113, and lifting transform unit 114. In other words, any one of the RAHT 112, the predicting transform unit 113 and the lifting transform unit 114 can be used to predict the attribute information of the points in the point cloud to obtain the predicted attribute values of the points, and then can The residual value of the attribute information of the point is obtained based on the attribute prediction value of the point. For example, the residual value of the attribute information of a point may be the original value of the attribute of the point minus the predicted value of the attribute of the point. The quantization unit 115 may be used to quantize residual values of attribute information of points. For example, if the quantization unit 115 is connected to the predictive transformation unit 113 , the quantization unit 115 may be used to quantize the residual value of the point attribute information output by the predictive transformation unit 113 . For example, the residual value of the attribute information of the points output by the predictive transformation unit 113 is quantized using the quantization step size, so as to improve system performance. The second arithmetic coding unit 116 may use zero run length coding to perform entropy coding on the residual value of the attribute information of the point, so as to obtain an attribute code stream. The attribute code stream may be bit stream information.

The predictive transformation unit 113 can be used to obtain the original order of the point cloud and divide the point cloud into detail layers (level of detail, LOD) based on the original order of the point cloud. After the predictive transformation unit 113 obtains the LOD of the point cloud, it can The attribute information of the points in the LOD is predicted in sequence, and then the residual value of the point attribute information is calculated, so that the subsequent units can perform subsequent quantization and encoding processing based on the residual value of the point attribute information. For each point in the LOD, based on the search results of neighbor points on the LOD where the current point is located, find 3 neighbor points before the current point, and then use the attributes of at least one of the 3 neighbor points to reconstruct the value of the current point Perform prediction to obtain the predicted value of the attribute of the current point; based on this, the residual value of the attribute information of the current point can be obtained based on the predicted value of the attribute of the current point and the original value of the attribute of the current point.

The original order of the point cloud acquired by the predictive transformation unit 113 may be an arrangement sequence obtained by performing Morton reordering on the current point cloud by the predictive transformation unit 113 . The encoder can obtain the original order of the current point cloud by reordering the current point cloud. After the encoder obtains the original order of the current point cloud, it can divide the points in the point cloud according to the original order of the current point cloud. Get the LOD of the current point cloud, and then predict the attribute information of the points in the point cloud based on the LOD.

Figures 7 to 9 show the arrangement order of Morton codes in two-dimensional space.

As shown in FIG. 7 , the encoder can adopt a "z"-shaped Morton sequence in the two-dimensional space formed by 2*2 blocks. As shown in Figure 8, the encoder can adopt a "z"-shaped Morton arrangement sequence in the two-dimensional space formed by four 2*2 blocks, where each 2*2 block can also be formed in the two-dimensional space By adopting the "z"-shaped Morton arrangement order, the Morton arrangement order adopted by the encoder in the two-dimensional space formed by 4*4 blocks can be finally obtained. As shown in Figure 9, the encoder can adopt a "z"-shaped Morton arrangement sequence in the two-dimensional space formed by four 4*4 blocks, wherein, each two-dimensional space formed by four 2*2 blocks and each In the two-dimensional space formed by 2*2 blocks, a "z"-shaped Morton arrangement order can also be used, and finally the Morton arrangement order adopted by the encoder in the two-dimensional space formed by 8*8 blocks can be obtained.

Fig. 10 shows the arrangement order of Morton codes in three-dimensional space.

As shown in Figure 10, the Morton arrangement order is not only applicable to two-dimensional space, but also can be extended to three-dimensional space. For example, Figure 10 shows 16 points, each "z" inside, each "z" The Morton arrangement sequence between "z" and "z" is first coded along the x-axis, then along the y-axis, and finally along the z-axis.

The generation process of LOD includes: according to the location information of the points in the point cloud, the Euclidean distance between the points is obtained; according to the Euclidean distance, the points are divided into different LOD layers. In one embodiment, after sorting the Euclidean distances, the Euclidean distances in different ranges can be divided into different LOD layers. For example, a point can be randomly selected as the first LOD layer. Then calculate the Euclidean distance between the remaining points and this point, and classify the points whose Euclidean distance meets the first threshold requirement as the second LOD layer. Obtain the centroid of the point in the second LOD layer, calculate the Euclidean distance between points other than the first and second LOD layers and the centroid, and classify the points whose Euclidean distance meets the second threshold as the third LOD layer. By analogy, all points are classified into the LOD layer. By adjusting the threshold of the Euclidean distance, the number of points of each layer of LOD can be increased. It should be understood that other manners may also be used for dividing the LOD layer, which is not limited in this application. It should be noted that the point cloud can be directly divided into one or more LOD layers, or the point cloud can be divided into multiple point cloud slices first, and then each point cloud slice can be divided into one or more LOD layers. LOD layers. For example, the point cloud can be divided into multiple point cloud cutouts, and the number of points in each point cloud cutout can be between 550,000 and 1.1 million. Each point cloud slice can be regarded as a separate point cloud. Each point cloud slice can be divided into multiple LOD layers, and each LOD layer includes multiple points. In one embodiment, the LOD layer can be divided according to the Euclidean distance between points.

Fig. 11 is a schematic block diagram of an LOD layer provided by an embodiment of the present application.

As shown in Figure 11, it is assumed that the point cloud includes multiple points arranged in original order, namely P0, P1, P2, P3, P4, P5, P6, P7, P8, and P9, and the assumption can be based on point and point The Euclidean distance between can divide the point cloud into three LOD layers, namely LOD0, LOD1 and LOD2. Wherein, LOD0 may include P0, P5, P4, and P2, LOD2 may include P1, P6, and P3, and LOD3 may include P9, P8, and P7. At this time, LOD0, LOD1 and LOD2 can be used to form the LOD-based order (LOD-based order) of the point cloud, namely P0, P5, P4, P2, P1, P6, P3, P9, P8 and P7. The sequence based on LOD can be used as the encoding sequence of the point cloud.

Exemplarily, when the encoder predicts the current point in the point cloud, based on the neighbor point search results on the LOD where the current point is located, multiple predictor variable candidates are created, that is, the value of the index of the prediction mode (predMode) can be 0~3. For example, when using the prediction method to encode the attribute information of the current point, the encoder first finds three neighbor points before the current point based on the neighbor point search results on the LOD where the current point is located, and the prediction mode with index 0 refers to Based on the distance between the 3 neighbor points and the current point, the weighted average of the reconstructed attribute values of the 3 neighbor points is determined as the attribute prediction value of the current point; the prediction mode with an index of 1 refers to the nearest neighbor point among the 3 neighbor points The attribute reconstruction value of the current point is used as the attribute prediction value of the current point; the prediction mode with index 2 refers to the attribute reconstruction value of the next nearest neighbor point as the attribute prediction value of the current point; The attribute reconstruction value of the neighbor point other than the nearest neighbor point and the second nearest neighbor point is used as the attribute prediction value of the current point; after obtaining the candidates of the attribute prediction value of the current point based on the above-mentioned various prediction modes, the encoder can use the rate distortion The optimization (Rate distortion optimization, RDO) technique selects the best attribute prediction value, and then arithmetically encodes the selected attribute prediction value.

Further, if the index of the prediction mode at the current point is 0, the code stream does not need to encode the index of the prediction mode; if the index of the prediction mode selected by RDO is 1, 2 or 3, the code stream needs Encoding the index of the selected prediction mode means that the index of the selected prediction mode needs to be encoded into the attribute code stream.

Table 1

Predictor index Predicted value 0 average 1 P4(1 ^st nearest point) 2 P5( ^2nd nearest point) 3 P0(3 ^rd nearest point)

As shown in Table 1, when using the prediction method to encode the attribute information of the current point P2, the prediction mode with an index of 0 refers to reconstructing the attribute values of the neighbor points P0, P5 and P4 based on the distances of the neighbor points P0, P5 and P4 The weighted average value of is determined as the attribute prediction value of the current point P2; the prediction mode with index 1 refers to the attribute reconstruction value of the nearest neighbor point P4 as the attribute prediction value of the current point P2; the prediction mode with index 2 refers to the next neighbor The attribute reconstruction value of point P5 is used as the attribute prediction value of the current point P2; the prediction mode with index 3 means that the attribute reconstruction value of the next neighbor point P0 is used as the attribute prediction value of the current point P2.

The RDO technology is described as an example below.

The encoder first calculates the maximum difference maxDiff of the attributes of at least one neighbor point of the current point, and compares maxDiff with the set threshold. If it is less than the set threshold, the prediction mode of the weighted average of the attribute values of the neighbor points is used; otherwise, the The point uses the RDO technique to select the optimal forecasting mode. Specifically, the encoder calculates the maximum attribute difference maxDiff of at least one neighbor point of the current point, for example, first calculates the maximum difference of at least one neighbor point of the current point on the R component, that is, max(R1,R2,R3)-min(R1 , R2, R3); similarly, the encoder calculates the maximum difference between at least one neighbor point of the current point on the G and B components, that is, max(G1,G2,G3)-min(G1,G2,G3) and max( B1,B2,B3)-min(B1,B2,B3), and then select the maximum difference value in the R, G, and B components as maxDiff, that is, maxDiff=max(max(R1,R2,R3)-min(R1, R2,R3),max(G1,G2,G3)-min(G1,G2,G3),max(B1,B2,B3)-min(B1,B2,B3)); the encoder will get the maxDiff and set Compared with the specified threshold, if it is less than the set threshold, the prediction mode of the current point is set to 0, that is, predMode=0; if it is greater than or equal to the set threshold, the encoder can use RDO technology to determine the current point. predictive mode. For RDO technology, the encoder can calculate the corresponding rate-distortion cost for each prediction mode of the current point, and then select the prediction mode with the smallest rate-distortion cost, that is, the optimal prediction mode as the attribute prediction mode of the current point.

Exemplarily, the rate-distortion cost of the prediction mode whose index is 1, 2 or 3 can be calculated by the following formula:

J _{indx_i} = D _{indx_i} + λ×R _{indx_i} ;

Among them, _{Jindx_i} represents the rate-distortion cost when the current point adopts the prediction mode with index i, and D is the sum of the three components of attrResidualQuant, that is, D=attrResidualQuant[0]+attrResidualQuant[1]+attrResidualQuant[2]. λ is determined according to the quantization parameter of the current point, and R _{indx_i} represents the number of bits required in the code stream for the quantized residual value obtained when the current point adopts the prediction mode with index i.

Exemplarily, after the encoder determines the prediction mode used by the current point, it can determine the attribute prediction value attrPred of the current point based on the determined prediction mode, and then use the original attribute value attrValue of the current point to subtract the attribute prediction value attrPred of the current point And quantize the result to obtain the quantized residual value attrResidualQuant of the current point. For example, the encoder can determine the quantization residual value of the current point by the following formula:

attrResidualQuant=(attrValue-attrPred)/Qstep;

Among them, attrResidualQuant indicates the quantization residual value of the current point, attrPred indicates the attribute prediction value of the current point, attrValue indicates the original value of the attribute of the current point, and Qstep indicates the quantization step size. Among them, Qstep is calculated by the quantization parameter (Quantization Parameter, Qp).

Exemplarily, the attribute reconstruction value of the current point can be used as a neighbor candidate of the subsequent point, and the attribute information of the subsequent point is predicted by using the reconstruction value of the current point. The encoder may reconstruct a value based on the property of the current point determined by the first quantization residual value through the following formula:

Recon=attrResidualQuant×Qstep+attrPred;

Among them, Recon represents the attribute reconstruction value of the current point determined based on the quantization residual value of the current point, attrResidualQuant represents the quantization residual value of the current point, Qstep represents the quantization step size, and attrPred represents the attribute prediction value of the current point. Among them, Qstep is calculated by the quantization parameter (Quantization Parameter, Qp).

It should be noted that, in this application, the attribute predicted value (predicted value) of the current point may also be referred to as the predicted value of the attribute information or the color predicted value (predictedColor). The original attribute value of the current point may also be referred to as the real value or the original color value of the attribute information of the current point. The residual value of the current point may also be referred to as the difference between the original value of the attribute of the current point and the predicted value of the attribute of the current point, or may also be referred to as the color residual value (residualColor) of the current point. The reconstructed value of the attribute of the current point (reconstructed value) may also be referred to as the reconstructed value of the attribute of the current point or the reconstructed color value (reconstructedColor).

Fig. 12 is a schematic block diagram of a decoding framework 200 provided by an embodiment of the present application.

The decoding framework 200 can obtain the code stream of the point cloud from the encoding device, and obtain the position information and attribute information of the points in the point cloud by parsing the code. The decoding of point cloud includes position decoding and attribute decoding. The process of position decoding includes: performing arithmetic decoding on the geometric code stream; merging after constructing the octree, and reconstructing the position information of the point to obtain the reconstruction information of the position information of the point; Transform to get the position information of the point. The location information of a point may also be referred to as geometric information of a point. The attribute decoding process includes: obtaining the residual value of the attribute information of the point cloud by parsing the attribute code stream; dequantizing the residual value of the attribute information of the point to obtain the residual value of the attribute information of the dequantized point value; based on the reconstruction information of the position information of the point obtained in the position decoding process, one of the three prediction modes is selected for point cloud prediction, and the attribute reconstruction value of the point is obtained; the color space inverse transformation is performed on the attribute reconstruction value of the point to obtain Get the decoded point cloud.

As shown in Figure 12, position decoding can be realized by the following units: first arithmetic decoding unit 201, octree analysis (synthesize octree) unit 202, geometric reconstruction (Reconstruct geometry) unit 203 and coordinate inverse change (inverse transform coordinates) unit 204. Attribute encoding can be realized by the following units: second arithmetic decoding unit 210, inverse quantize unit 211, RAHT unit 212, predicting transform unit 213, lifting transform unit 214, and color space inverse transform (inverse transform colors) Unit 215.

It should be noted that decompression is an inverse process of compression. Similarly, the functions of each unit in the decoding framework 200 may refer to the functions of corresponding units in the encoding framework 100 . For example, the decoding framework 200 can divide the point cloud into multiple LODs according to the Euclidean distance between points in the point cloud; then, sequentially decode the attribute information of the points in the LOD; Quantity (zero_cnt), to decode the residual with a zero-based quantity; then, the decoding framework 200 can perform dequantization based on the decoded residual value, and add the dequantized residual value to the predicted value of the current point Get the reconstruction value of the point cloud until all the point clouds are decoded. The current point will be used as the nearest neighbor of the point in the subsequent LOD, and the attribute information of the subsequent point will be predicted by using the reconstruction value of the current point.

The codec framework applicable to the embodiment of the present application will be described below by taking the PCRM framework as an example.

Fig. 13 is a schematic block diagram of an encoding framework provided by an embodiment of the present application.

As shown in Figure 13, in the encoding framework, the geometric information of point cloud and the attribute information corresponding to each point are encoded separately.

In the geometric coding part of the encoding end, the original geometric information is preprocessed, that is, the coordinate transformation of the geometric information is carried out through coordinate translation, so that all point clouds are contained in a bounding box. Then, the geometric information is converted from floating-point numbers to integers through coordinate quantization, which is convenient for subsequent regularization processing. Due to quantization and rounding, the geometric information of some points is the same. At this time, it is necessary to decide whether to remove duplicate points. Quantization and removal of duplicate points belong to Preprocessing process; then, geometrically encode the regularized geometric information, that is, divide the bounding box in the order of breadth-first traversal (such as octree/quadtree/binary tree), and place the code for each node to encode. As an example, in the octree-based geometric code framework, the bounding box is divided sequentially to obtain sub-cubes, and the sub-cubes that are not empty (including points in the point cloud) continue to be divided until the leaf nodes obtained by the division are When the unit cube of 1x1x1 is divided, the number of points contained in the leaf nodes is encoded, and finally the encoding of the geometric octree is completed to generate a binary code stream. In the geometric decoding process based on the octree, the decoding end first obtains the placeholder code of each node through continuous parsing in the order of breadth-first traversal, and divides the nodes in turn until the 1x1x1 unit cube is divided. Then analyze to get the number of points contained in each leaf node, and finally recover or reconstruct the geometric information of the point cloud.

It should be understood that the method for regularizing the point cloud can refer to the descriptions of FIG. 5 and FIG. 6 , and will not be repeated here to avoid repetition.

After the geometric encoding is completed, the encoder reconstructs the geometric information, and uses the reconstructed geometric information to encode the attribute information.

In the attribute encoding part, firstly, the attribute encoding mainly encodes color information and/or reflectance information. First, the encoder judges whether to perform color space conversion. If the processed attribute information is color information, the original color needs to be transformed into a YUV color space that is more in line with the visual characteristics of the human eye; then, in the geometry In the case of lossy encoding, since the geometric information changes after geometric encoding, it is necessary to reassign attribute values for each point after geometric encoding, so that the attribute error between the reconstructed point cloud and the original point cloud is minimized. This process is called attribute Interpolation or attribute recoloring; then, attribute encoding is performed on the preprocessed attribute information, and attribute information encoding is divided into attribute prediction and attribute transformation. Among them, for the attribute prediction in attribute information coding, the encoder can obtain the attribute prediction value of the current point through prediction, and after obtaining the attribute prediction value of the current point, it can obtain the current point’s attribute prediction value based on the current point’s attribute prediction value. The residual value, for example, the residual value is the difference between the original value of the attribute at the current point and the predicted value of the attribute. For attribute transformation in attribute information coding, the encoder first performs wavelet transform on the original attribute value of the current point to obtain the transformation coefficient, and then quantizes the obtained transformation coefficient to obtain the quantized transformation coefficient; further, the encoder performs inverse quantization , Inverse wavelet transform to obtain the attribute reconstruction value of the current point; then the encoder calculates the difference between the original value of the attribute of the current point and the reconstruction value of the attribute of the current point to obtain the residual value of the current point. After the encoder obtains the residual value of the current point, it can quantize the residual value to obtain the quantized residual value of the current point, and input the quantized residual value into the attribute entropy encoder to form the attribute code stream.

Exemplarily, for attribute prediction in attribute information encoding, the encoder can reorder the current point cloud and perform attribute prediction. Optionally, the reordering methods include Morton reordering and Hilbert reordering. The encoder can obtain the encoding order of the current point cloud by reordering the current point cloud. After the encoder obtains the encoding order of the current point cloud, it can predict the attribute information of the points in the point cloud according to the encoding order of the current point cloud.

It should be understood that for the relevant content of the Morton code reordering, refer to the relevant content involved in the above-mentioned FIG. 7 to FIG. 10 , and to avoid repetition, details are not repeated here.

For the attribute prediction of the current point in the current point cloud, if the geometric information of the current point is the same as that of the encoded point before the encoding sequence, it is a duplicate point, and the attribute reconstruction value of the duplicate point is used as the attribute prediction value of the current point. Optionally, if the geometric information of the current point is not the same as that of the encoded points before the encoding order, that is, it is not a repeated point, the encoder can select the first m points as the neighbor candidate points of the current point according to the encoding order, and then calculate The Manhattan distance between m points and the geometric information of the current point, and determine the nearest n points as the neighbor points of the current point, and then the encoder can use the reciprocal of the distance as the weight to calculate the weighted average of the attributes of the n neighbors, As the predicted value of the attribute at the current point.

Exemplarily, the encoder can obtain the attribute prediction value of the current point in the following ways:

attrPred=(1/W ₁ ×ref ₁ +1/W ₂ ×ref ₂ +1/W ₃ ×ref ₃ )/(1/W ₁ +1/W ₂ +1/W ₃ ).

Among them, W ₁ , W ₂ , and W ₃ represent the geometric distances between neighbor point 1, neighbor point 2, neighbor point 3 and the current point, respectively, and ref ₁ , ref ₂ , and ref ₃ represent neighbor point 1, neighbor point 2, and neighbor point respectively. A property reconstruction value of 3.

Exemplarily, the encoder can determine the quantization residual value of the current point through the following formula:

attrResidualQuant=(attrValue-attrPred)/Qstep;

Among them, attrResidualQuant indicates the quantization residual value of the current point, attrPred indicates the attribute prediction value of the current point, attrValue indicates the original value of the attribute of the current point, and Qstep indicates the quantization step size. Among them, Qstep is calculated by the quantization parameter (Quantization Parameter, Qp).

Exemplarily, the attribute reconstruction value of the current point can be used as a neighbor candidate of the subsequent point, and the attribute information of the subsequent point is predicted by using the reconstruction value of the current point. The encoder may reconstruct a value based on the property of the current point determined by the first quantization residual value through the following formula:

Recon=attrResidualQuant×Qstep+attrPred;

Among them, Recon represents the attribute reconstruction value of the current point determined based on the quantization residual value of the current point, attrResidualQuant represents the quantization residual value of the current point, Qstep represents the quantization step size, and attrPred represents the attribute prediction value of the current point. Among them, Qstep is calculated by the quantization parameter (Quantization Parameter, Qp).

It should be noted that, in this application, the attribute predicted value (predicted value) of the current point may also be referred to as the predicted value of the attribute information or the color predicted value (predictedColor). The original attribute value of the current point may also be referred to as the real value or the original color value of the attribute information of the current point. The residual value of the current point may also be referred to as the difference between the original value of the attribute of the current point and the predicted value of the attribute of the current point, or may also be referred to as the color residual value (residualColor) of the current point. The reconstructed value of the attribute of the current point (reconstructed value) may also be referred to as the reconstructed value of the attribute of the current point or the reconstructed color value (reconstructedColor).

Fig. 14 is a schematic block diagram of a decoding framework provided by an embodiment of the present application.

As shown in Figure 14, at the decoding end, the geometry and attributes are also decoded separately. In the geometry decoding part, the decoder first performs entropy decoding on the geometry code stream to obtain the geometric information of each point, then constructs the octree structure of the point cloud in the same way as the geometry code, and combines the decoded geometry code stream to obtain The geometric information expressed by the tree structure, on the one hand, coordinate inverse quantization and inverse translation of the geometric information expressed by the octree structure, to obtain the decoded geometric information, on the other hand, it is input into the attribute decoder as additional information. In the attribute decoding part, the Morton order is constructed in the same way as the encoding side, and the attribute code stream is entropy decoded to obtain the quantized residual information; then inverse quantization is performed to obtain the point cloud residual; similarly, according to the In the same manner as attribute encoding, the attribute prediction value of the current point to be decoded is obtained, and then the attribute prediction value is added to the residual value to restore the YUV attribute value of the current point to be decoded; finally, the decoded attribute is obtained through inverse color space transformation information.

It can be seen that, regardless of the G-PCC framework or the AVS-PCC framework, for point cloud compression, the encoder needs to compress its position information and attribute information; specifically, the encoder first performs Octree encoding; at the same time, the encoder selects the neighbor points used to predict the attribute value of the current point from the encoded points according to the position information of the current point encoded by the octree, and refers to the selected neighbor points for the current point Point prediction to obtain the predicted attribute value of the current point, and then calculate the residual value of the current point based on the predicted attribute value of the current point and the original value of the attribute of the current point, and then the encoder quantizes the residual value of the current point and other processes To obtain the quantized residual value, the final encoder transmits the quantized residual value of the current point to the decoder in the form of a code stream; the decoder can obtain the quantized residual value of the current point by receiving and analyzing the code stream, and the quantized residual value The residual value of the current point is obtained through steps such as inverse transformation and inverse quantization, and the decoding end uses the same process to predict the attribute prediction value, which is superimposed with the residual value obtained by parsing the code stream to obtain the attribute reconstruction value of the current point.

Usually, after the encoder predicts the current point with reference to the selected neighbor points to obtain the attribute prediction value of the current point, it needs to use the attribute reconstruction value of the selected neighbor points to perform differential prediction on the attribute of the current point to obtain the current point Then the encoder quantizes and encodes the residual value of the current point. However, in this encoding process, since the encoder uses the same quantization parameter for all points in the same point cloud sequence for quantization encoding, it will result in low encoding efficiency or low reconstruction quality for some points, thereby reducing the The encoding performance of the encoder.

In view of this, the present application provides an encoding method, an encoder and a storage medium, which can improve the encoding performance of the encoder.

FIG. 15 is a schematic flowchart of an encoding-based method 300 provided by an embodiment of the present application. The method 300 can be executed by an encoder or an encoding framework, such as the encoding framework shown in FIG. 4 .

As shown in Figure 15, the encoding method 300 may include:

S310. Based on the geometric information of the current point cloud, determine the encoding sequence of the attribute information of the current point cloud;

S320. Determine a first quantized residual value of the current point based on at least one neighbor point before the current point to be encoded in the encoding order;

S330. Determine at least one second quantization residual value of the current point;

S340. Determine a third quantized residual value according to the first quantized residual value and the at least one second quantized residual value;

S350. Encode the third quantized residual value to obtain a code stream.

Exemplarily, after the encoder determines the first quantized residual value based on at least one neighbor point before the current point in the encoding order, the encoder may calculate the first quantized residual value and the at least Selecting a quantized residual value from one quantized residual value as the third quantized residual value, and encoding the third quantized residual value to obtain an attribute code stream. Optionally, the third quantized residual value may be the first quantized residual value, or one of the at least one second quantized residual value, and this application does not make any Specific limits.

In the embodiment of the present application, when determining the quantized residual value of the final coded code stream for the current point, the present application considers the first quantized residual value determined based on at least one neighbor point before the current point to be coded in the coding order On the basis of , at least one second quantized residual value is also introduced, because when the current point adopts different quantized residual values, it means that the quantization scale of the current point is different; therefore, the encoder first uses the first quantized residual value and The at least one second quantization residual value selects a third quantization residual value, and then encodes the third quantization residual value, which not only prevents the encoder from using the same quantization scale for all points in the current point cloud for quantization Encoding is also conducive to selecting an appropriate quantization scale for the points in the current point cloud, thereby avoiding low encoding efficiency or low reconstruction quality of some points, and improving the encoding performance of the encoder.

The attribute code stream obtained by encoding the third quantized residual value by the encoder will be described below.

Fig. 16 is a schematic structural diagram of an attribute code stream provided by an embodiment of the present application.

As shown in Figure 16, because some point cloud data is too large, before encoding the point cloud, the point cloud can be divided into multiple point cloud slices (slices), which is equivalent to the encoder performing Before encoding, the large-scale point cloud is converted into many small-scale point clouds, and the point cloud slices are serially encoded, that is, the encoder encodes all the points in the first point cloud slice The second point cloud slice, and so on, until all point cloud slices are encoded. The encoder can obtain the attribute code stream by encoding all point cloud segments included in the current point cloud, and the attribute code stream can include attribute parameter set (Attribute parameter set, APS) and attribute header (Attribute header, ABH) information and Payload (Payload), APS contains the basic information and configuration files of the current point cloud, such as the number of layers of the level of detail (LOD), and the ABH information contains the division parameters of the point cloud slices. Each coded point cloud slice, The encoder first encodes the ABH information of the point cloud slice, and then encodes the load of the point cloud slice, where the load of the point cloud slice encodes the quantized residual value of the point (that is, the third quantized residual value of the current point) get the information.

Fig. 17 is a schematic structural diagram of a payload in an attribute code stream provided by an embodiment of the present application.

As shown in Figure 17, the encoder can use a run-length coding scheme to encode the quantized residual value of the point, that is, the run length (run Length) parameter indicates the number of continuously distributed quantized residual values of the current point cloud that are zero , when a non-zero quantized residual value is encountered, the length of the run will be encoded first, and then the quantized residual value (Res) will be encoded, and finally the attribute code stream of the current point cloud will be obtained.

It should be noted that before encoding the quantized residual value, the prediction mode PredMode used by the current point may be encoded. When predicting the attribute, the maximum attribute difference maxDiff of at least one neighbor point of the current point is greater than or equal to the set When a certain threshold (for example, the error maxDiff between the attribute of the nearest neighbor point and the attribute of the farthest neighbor point among the three neighbor points is greater than the set threshold value), the encoder needs to use the rate-distortion algorithm to select the prediction mode PredMode of the current point (also is the prediction mode of index 1 to 3 in Table 1), at this time the encoder can also encode the prediction mode used at the current point, that is, the encoder can encode the The prediction mode to use for the current point to encode. Of course, when the maximum attribute difference maxDiff of at least one neighbor point of the current point is less than the set threshold, the encoder uses the prediction mode with index 0 to predict the attribute information of the current point. At this time, the encoder does not need to use the current point The predicted mode is written into the code stream.

In some embodiments, the S330 may include:

Based on the first quantized residual value, the at least one second quantized residual value is determined.

Exemplarily, the first quantized residual value is directly proportional to the at least one second quantized residual value.

Exemplarily, each second quantization residual value of the at least one second quantization residual is smaller than the first quantization residual value.

Exemplarily, part of the at least one second quantized residual value is smaller than the first quantized residual value, and another part of the at least one second quantized residual value is second quantized The residual value is greater than the first quantized residual value.

It should be understood that, the present application does not limit the specific implementation manner of determining the at least one second quantization residual value based on the first quantization residual value. For example, the at least one second quantized residual value may be determined based on the first quantized residual value based on a certain functional relationship or mapping relationship.

In some embodiments, the S330 may include:

An integer smaller than the first quantized residual value is determined as the at least one second quantized residual value.

Exemplarily, when the value of the first quantized residual value is N, the number of the at least one second quantized residual value is N-1, and the value of the at least one second quantized residual value is The values are: 0,...,N-1. Wherein, N is a positive integer.

In some embodiments, the S330 may include:

A default residual value, is determined as the at least one second quantized residual value.

Exemplarily, the default residual value may also be referred to as a default residual value.

Exemplarily, the default residual value may be stipulated by an agreement. Wherein, the protocol may be various coding standards or decoding standard protocols.

Exemplarily, it is assumed that the exact residual value is set to 0.

In some embodiments, the S340 may include:

calculating a rate-distortion cost for the first quantized residual value and a rate-distortion cost for the second quantized residual value;

A quantized residual value having the smallest rate-distortion cost among the rate-distortion cost of the first quantized residual value and the rate-distortion cost of the second quantized residual value is determined as the third quantized residual value.

Exemplarily, the encoder calculates the rate-distortion cost of the first quantization residual value and the rate-distortion cost of each second quantization residual value, and combines the first quantization residual value and the at least one second quantization residual value A quantized residual value with the smallest rate-distortion cost among the quantized residual values is determined as the third quantized residual value. For example, when the rate-distortion cost of the first quantization residual value is smaller than the rate-distortion cost of each second quantization residual value in the at least one second quantization residual value, the first quantization residual value Determined as the third quantized residual value, when the second quantized residual value with the smallest rate-distortion cost in the at least one second quantized residual value is smaller than the first quantized residual value, the rate-distortion cost is minimized The second quantized residual value of is determined as the first quantized residual value.

In some embodiments, the rate-distortion cost of the first quantized residual value is calculated in the same manner as the rate-distortion cost of the second quantized residual value.

Of course, in other alternative embodiments, the calculation method of the rate-distortion cost of the first quantization residual value and the calculation mode of the rate-distortion cost of the second quantization residual value may be different, and this application does not make any Specific limits.

In some embodiments, the rate-distortion cost of the first quantization residual value or the second quantization residual value is calculated according to the following formula:

J ₁ =D ₁ +λ×R ₁ ;

Wherein, when J ₁ represents the rate-distortion cost of the first quantized residual value, D ₁ represents the original attribute value of the current point and the reconstructed attribute value of the current point determined based on the first quantized residual value The error between , λ is determined according to the quantization parameter of the current point, R ₁ represents the number of bits required by the first quantization residual value in the code stream; or, J ₁ represents the second quantization residual value When the rate-distortion cost of the difference, D ₁ represents the error between the original attribute value of the current point and the attribute reconstruction value of the current point determined based on the second quantized residual value, and λ is based on the current point The quantization parameter is determined, and R ₁ represents the number of bits required by the second quantization residual value in the code stream.

Exemplarily, D can be obtained by the following formula:

D ₁ = |attrValue-Recon1|;

Wherein, attrValue represents the original value of the attribute of the current point, and Recon1 represents the reconstructed value of the attribute of the current point determined based on the first quantized residual value.

Exemplarily, λ may be a parameter obtained through a large number of tests. Optionally, the test range of λ is about 0.0-4.0, that is to say, the value range of λ may be 0.0-4.0. Of course, the test range of λ may also be other numerical ranges, which is not specifically limited in this application. For example, λ may be a parameter obtained by testing when the quantization step size or quantization parameter of the current point is fixed, for example, the quantization step size of the current point is 0.1. In other words, λ and the quantization parameter or quantization step size also form a certain functional relationship, such as λ=QP×0.1. In other words, different QPs can correspond to different values of λ. For example, point cloud standard test environment C1 (test conditions with near-destructive geometric properties and non-destructive properties) corresponds to 6 QPs (48, 40, 32, 24, 16, 8), At this time, each of the six QPs may correspond to a value of λ. Another example is that the point cloud standard test environment CY (test conditions with geometric lossy properties and near lossless properties) corresponds to 5 QPs (10, 16, 22, 28, 34). At this time, each QP in the 5 QPs can correspond to a Value of λ. Certainly, the value of the above QP is only an example of the present application, and should not be construed as a limitation to the present application.

In this embodiment, when calculating the rate-distortion cost of the first quantized residual value, D is designed to represent the original value of the attribute at the current point and the value determined based on the first quantized residual value. The error between the attribute reconstruction values of the current point, so that J can take into account the reconstruction quality of the attribute reconstruction value of the current point determined based on the first quantized residual value, and R is designed to represent the first The number of bits required by the quantization residual value in the code stream, so that J can take into account the code rate at the current point, that is, make J can take into account the quantization scale corresponding to the first quantization residual value Coding efficiency, that is to say, through the design of D and R, this application can make the rate-distortion cost of the first quantization residual value take into account the reconstruction at the quantization scale corresponding to the first quantization residual value Quality and coding efficiency, thus ensuring coding performance. In addition, by introducing λ, it is equivalent to the case that all points in the current point cloud adopt the same quantization step size, combined with the design of D and R, not only can make the first quantization residual value and Under different quantization scales, the second quantized residual value can also make the rate-distortion cost of the first quantized residual value and the rate-distortion cost of the second quantized residual value take into account the current point The reconstruction quality and coding efficiency of the current point make the final quantized residual value selected at the current point the third quantized residual value with the best quantization scale, which avoids the low coding efficiency or low reconstruction quality of the current point, and can improve the coding Encoding performance of the device.

Exemplarily, the attribute reconstruction value of the current point can be used as a neighbor candidate of the subsequent point, and the attribute information of the subsequent point is predicted by using the reconstruction value of the current point.

Exemplarily, the encoder may use the following formula to reconstruct the attribute value of the current point determined based on the first quantized residual value:

Recon1=attrResidualQuant1×Qstep+attrPred;

Wherein, Recon1 represents the attribute reconstruction value of the current point determined based on the first quantized residual value, attrResidualQuant1 represents the first quantized residual value, Qstep represents the quantization step size, and attrPred represents the predicted attribute value of the current point. Among them, Qstep is calculated by the quantization parameter (Quantization Parameter, Qp).

In some embodiments, λ is determined according to the quantization parameter of the current point and at least one of the following:

The sequence type of the current point cloud and the current component of the current point.

Exemplarily, for different sequence types, the corresponding value of λ may be different. Optionally, λ can be a parameter obtained through a large number of tests, for example, λ can be a parameter obtained by testing under the sequence type of the current point cloud; in other words, point clouds of different sequence types can be used, through testing or The training method obtains its corresponding value of λ.

Exemplarily, for different components of the current point, corresponding values of λ may be different. Optionally, λ may be a parameter obtained through a large number of tests, for example, λ may be a parameter obtained by testing under the current component of the current point. In other words, different components of points in the point cloud can be used to obtain the corresponding value of λ through testing or training. Optionally, the current component may be a component to be encoded at the current point. For example, the value of λ corresponding to the V component, the U component, and the Y component may be the same or different. For another example, for example, the value of λ corresponding to the R component, the G component, and the B component may be the same or different. Optionally, the encoding scheme provided in this application may only be applicable to some components of the current point, or may be applicable to all components of the current point, which is not specifically limited in this application.

The scheme of determining the first quantized residual value of the current point in combination with the G-PCC framework will be described below.

In some embodiments, the S320 may include:

Based on at least one neighbor point before the current point to be encoded in the encoding order, determine the prediction mode adopted by the current point;

Based on the prediction mode adopted by the current point, determine the first quantization residual value of the current point.

Exemplarily, the encoder first determines the prediction mode adopted by the current point, and then predicts the attribute value of the current point based on the prediction mode adopted by the current point to obtain the predicted value of the attribute of the current point, and based on the predicted value of the attribute of the current point and the attribute Original value, get the first quantized residual value of the current point.

In some embodiments, the attribute difference of the at least one neighbor point is determined based on the difference in each component of the at least one neighbor point; if the attribute difference of the at least one neighbor point is smaller than a first threshold, the first The prediction mode is determined as the prediction mode adopted by the current point; the first prediction mode refers to a prediction mode for predicting the attribute value of the current point based on the weighted average value of the at least one neighbor point.

Exemplarily, when predicting the current point in the point cloud, the encoder creates multiple prediction mode candidates based on the search results of neighbor points on the LOD where the current point is located. For example, when using the prediction method to encode the attribute information of the current point, the encoder first finds 3 neighbor points before the current point based on the neighbor point search results on the LOD where the current point is located, and creates 4 prediction mode candidates, That is, the prediction mode (predMode) whose index value is 0 to 3. Among them, the prediction mode with an index of 0 refers to determining the weighted average of the reconstructed attribute values of the three neighbor points as the attribute prediction value of the current point based on the distance between the three neighbor points and the current point; the prediction mode with an index of 1 refers to The attribute reconstruction value of the nearest neighbor point among the three neighbor points is used as the attribute prediction value of the current point; the prediction mode with index 2 refers to the attribute reconstruction value of the next nearest neighbor point as the attribute prediction value of the current point; the prediction mode with index 3 The mode refers to the attribute reconstruction value of the neighbor points except the nearest neighbor point and the next nearest neighbor point among the three neighbor points as the attribute prediction value of the current point. After the encoder obtains each prediction mode, it can obtain the candidate of the attribute prediction value of the current point based on various prediction modes, and use rate distortion optimization (Rate distortion optimization, RDO) technology to select the best attribute prediction value, and then the selected Arithmetic coding of attribute predictions. For example, when the encoder determines the prediction mode used by the current point, it can first calculate the maximum difference maxDiff of its attributes for at least one neighbor point of the current point, and then compare maxDiff with the set threshold, if it is less than the set threshold (ie The first threshold) uses the prediction mode whose index is 0 (that is, the first prediction mode).

In some embodiments, the attribute difference of the at least one neighbor point is determined by taking the maximum difference among the differences of the at least one neighbor point on each component.

Exemplarily, when the encoder calculates the maximum difference maxDiff of the attributes of at least one neighbor point of the current point, it may first calculate the maximum difference of at least one neighbor point of the current point on each component; for example, at least one neighbor point of the current point is in The maximum difference on the R component is max(R1,R2,R3)-min(R1,R2,R3), and the maximum difference on the G and B components is max(G1,G2,G3)-min(G1,G2) ,G3) and max(B1,B2,B3)-min(B1,B2,B3); Then, select the maximum difference in the R, G, and B components as maxDiff, that is, maxDiff=max(max(R1,R2, R3)-min(R1,R2,R3),max(G1,G2,G3)-min(G1,G2,G3),max(B1,B2,B3)-min(B1,B2,B3)); encoding After obtaining maxDiff, the device can compare maxDiff with a set threshold (ie, the first threshold), and if it is less than the set threshold, the index of the prediction mode at the current point is 0, that is, predMode=0.

In some embodiments, the method 300 may also include:

If the attribute difference of the at least one neighbor point is greater than or equal to the first threshold, determine the prediction mode with the smallest rate-distortion cost in the at least one second prediction mode as the prediction mode adopted by the current point; the at least A second prediction mode has a one-to-one correspondence with the at least one neighbor point, and the second prediction mode refers to using the attribute reconstruction value of the neighbor point corresponding to the second prediction mode among the at least one neighbor point as the current The prediction mode of the predicted value of the attribute of the point;

Write the prediction mode adopted by the current point into the code stream.

Exemplarily, the encoder first calculates the maximum difference maxDiff of its attributes for at least one neighbor point of the current point, compares maxDiff with the set threshold, and if it is less than the set threshold, uses the prediction mode of the weighted average of the attribute values of the neighbor points ; Otherwise, use the RDO technique to select the optimal prediction mode for the current point. Specifically, when the encoder calculates the maximum difference maxDiff of the attributes of at least one neighbor point of the current point, it can first calculate the maximum difference of at least one neighbor point of the current point on each component; for example, at least one neighbor point of the current point is in R The maximum difference on the component is max(R1, R2, R3)-min(R1, R2, R3), and the maximum difference on the G and B components of at least one neighbor point of the current point is max(G1, G2, G3) -min(G1,G2,G3) and max(B1,B2,B3)-min(B1,B2,B3); then, the encoder can choose the maximum difference in R, G, B components as maxDiff, ie maxDiff =max(max(R1,R2,R3)-min(R1,R2,R3),max(G1,G2,G3)-min(G1,G2,G3),max(B1,B2,B3)-min( B1, B2, B3)); after the encoder obtains maxDiff, it can compare maxDiff with the set threshold (ie, the first threshold), and if it is less than the set threshold, the index of the prediction mode at the current point is 0, that is predMode=0; if it is greater than or equal to the set threshold, the encoder can use the RDO technology to determine the index of the prediction mode used by the current point for the current point. For RDO technology, the encoder can calculate the corresponding rate-distortion cost for each prediction mode of the current point, and then select the prediction mode with the smallest rate-distortion cost, that is, the optimal prediction mode as the attribute prediction mode of the current point.

In some embodiments, the method 300 may also include:

Determine the quantized residual value of the current point on each component based on the attribute reconstruction value on each component of the neighbor point corresponding to the second prediction mode;

Determining the sum of the quantized residual values of the current point on each component as the rate-distortion cost of the second prediction mode.

Exemplarily, the rate-distortion cost of the prediction mode whose index is 1, 2 or 3 can be calculated by the following formula:

J _{indx_i} = D _{indx_i} + λ×R _{indx_i} ;

Among them, _{Jindx_i} represents the rate-distortion cost when the current point adopts the prediction mode with index i, and D is the sum of the three components of attrResidualQuant, that is, D=attrResidualQuant[0]+attrResidualQuant[1]+attrResidualQuant[2]. λ is determined according to the quantization parameter of the current point, and R _{indx_i} represents the number of bits required in the code stream for the quantized residual value obtained when the current point adopts the prediction mode with index i.

In some embodiments, the S330 may include:

Determine the predicted attribute value of the current point based on the prediction mode adopted by the current point;

calculating the difference between the original value of the attribute of the current point and the predicted value of the attribute of the current point;

Determining the ratio of the difference to the quantization step used by the current point as the first quantization residual value; the quantization step used by the current point is determined according to the quantization parameter of the current point.

Exemplarily, the encoding end may first use the prediction mode adopted by the at least one neighbor point on the first component, determine the attribute prediction value of the current point on the first component, and then calculate the current point on the first component. The difference between the original value of the attribute on the first component and the predicted value of the attribute of the current point on the first component, and finally the original value of the attribute on the first component of the current point and the predicted value of the attribute of the current point on the first component The ratio of the difference between the attribute prediction values of the current point on the first component to the quantization step size adopted by the current point is determined as the first quantization residual value. Optionally, the first component may be a V component, a U component or a Y component, or may be an R component, a G component or a B component. Optionally, the quantization step size adopted by the current point may be the quantization step size adopted by the current point cloud, that is, all points in the current point cloud adopt the same quantization step size.

Exemplarily, after the encoder determines the prediction mode used by the current point, it can determine the attribute prediction value attrPred of the current point based on the determined prediction mode, and then use the original attribute value attrValue of the current point to subtract the attribute prediction value attrPred of the current point And the result is quantized to obtain the first quantized residual value attrResidualQuant1 of the current point. For example, after the encoder obtains the attribute prediction value of the current point, the first quantized residual value may be determined by the following formula:

attrResidualQuant1 = (attrValue-attrPred)/Qstep;

Wherein, attrResidualQuant represents the first quantization residual value, attrPred represents the attribute prediction value of the current point, attrValue represents the original value of the attribute of the current point, and Qstep represents the quantization step size. Among them, Qstep is calculated by the quantization parameter (Quantization Parameter, Qp).

In some embodiments, the encoding order comprises a layer of detail LOD order.

Exemplarily, the encoder can obtain the original order of the current point cloud by reordering the current point cloud. After the encoder obtains the original order of the current point cloud, it can divide the point cloud by LOD according to the original order of the current point cloud. Then, the attribute information of the points in the point cloud is predicted based on the LOD.

The results obtained by testing the scheme provided by the present application on the G-PCC reference software TMC13V14.0 are described below in conjunction with Table 2.

Among them, Table 2 is in the case of lossless geometry (lossless geometry) compression and near-loss attributes (near-loss attributes) compression and the value of λ is 1.2, the BD of each component of each point cloud sequence of Cat1A type -rate; where Cat1-A represents the point cloud of the point including the color information of the point, and the end-to-end representative rate distortion (End-to-End Bit distortion, End-to-End BD-rate) is a measure of algorithm performance or encoding performance The indicator of the coding algorithm provided by this application is compared with the original coding algorithm in terms of code rate and PSNR. The overall negative value indicates that the performance is better. Hausdorff BD-rate is also a unique evaluation under the condition of nearly lossless properties. The standard of performance, which represents the maximum difference between attribute values, and also when the overall value is negative, it means that the performance of the new algorithm is better. L, Cb and Cr (also known as Y, U, V) represent the performance of the three components of brightness and chromaticity of the point cloud color.

Table 2

As shown in Table 2, in the case of lossless geometric compression and near-lossless attribute compression and the value of λ is 1.2, the End-to-End BD-rate of sequence 2 has performance improvement in each component. The Hausdorff BD-rate of 1 has a performance improvement in each component, especially the Cb component can be improved by 46.5%. All have improved performance, that is, the solution provided by the present application can improve encoding performance.

The solution for determining the first quantized residual value of the current point in combination with the AVS-PCC framework will be described below.

In some embodiments, the S120 may include:

Determining the weighted average of the at least one neighbor point as the attribute prediction value of the current point;

calculating the difference between the original value of the attribute of the current point and the predicted value of the attribute of the current point;

Determining the ratio of the difference to the quantization step used by the current point as the first quantization residual value; the quantization step used by the current point is determined according to the quantization parameter of the current point.

Exemplarily, the encoding end may first determine the weighted average of the at least one neighbor point on the first component as the attribute prediction value of the current point on the first component, and then calculate the current point on the first component. The difference between the original value of the attribute on the first component and the predicted value of the attribute of the current point on the first component, and finally the original value of the attribute on the first component of the current point and The ratio of the difference between the attribute prediction values of the current point on the first component to the quantization step size adopted by the current point is determined as the first quantization residual value. Optionally, the first component may be a V component, a U component or a Y component, or may be an R component, a G component or a B component. Optionally, the quantization step size adopted by the current point may be the quantization step size adopted by the current point cloud, that is, all points in the current point cloud adopt the same quantization step size.

For the attribute prediction of the current point in the current point cloud, if the geometric information of the current point is the same as that of the encoded point before the encoding sequence, it is a duplicate point, and the attribute reconstruction value of the duplicate point is used as the attribute prediction value of the current point. Optionally, if the geometric information of the current point is not the same as that of the encoded points before the encoding order, that is, it is not a repeated point, the encoder can select the first m points as the neighbor candidate points of the current point according to the encoding order, and then calculate The Manhattan distance between m points and the geometric information of the current point, and determine the nearest n points as the neighbor points of the current point, and then the encoder can use the reciprocal of the distance as the weight to calculate the weighted average of the attributes of the n neighbors, As the predicted value of the attribute at the current point.

Exemplarily, the encoder can obtain the attribute prediction value of the current point in the following ways:

attrPred=(1/W ₁ ×ref ₁ +1/W ₂ ×ref ₂ +1/W ₃ ×ref ₃ )/(1/W ₁ +1/W ₂ +1/W ₃ ).

Among them, W ₁ , W ₂ , and W ₃ represent the geometric distances between neighbor point 1, neighbor point 2, neighbor point 3 and the current point, respectively, and ref ₁ , ref ₂ , and ref ₃ represent neighbor point 1, neighbor point 2, and neighbor point respectively. A property reconstruction value of 3.

Exemplarily, after the encoder obtains the attribute prediction value of the current point, the first quantized residual value may be determined by the following formula:

attrResidualQuant1 = (attrValue-attrPred)/Qstep;

Wherein, attrResidualQuant represents the first quantization residual value, attrPred represents the attribute prediction value of the current point, attrValue represents the original value of the attribute of the current point, and Qstep represents the quantization step size. Among them, Qstep is calculated by the quantization parameter (Quantization Parameter, Qp).

In some embodiments, the encoding order comprises a Morton's order or a Hilbert order.

Exemplarily, for attribute prediction in attribute information encoding, the encoder can reorder the current point cloud and perform attribute prediction. Optionally, the reordering methods include Morton reordering and Hilbert reordering. The encoder can obtain the encoding order of the current point cloud by reordering the current point cloud. After the encoder obtains the encoding order of the current point cloud, it can predict the attribute information of the points in the point cloud according to the encoding order of the current point cloud.

In the following, in conjunction with Table 3 to Table 6, the results obtained by testing the solution provided by this application on the latest point cloud compression platform PCRMv4.0 of AVS are described.

Among them, Table 3 is in the case of limiting lossy geometry (limit-lossy geometry) compression and lossy attributes (lossy attributes) compression and the value of λ is 0.5, the BD of each component of Cat1A to Cat1A+Cat2 -rate; where Cat1A represents the point cloud of points that only include the reflectance information of the point, Cat1B represents the point cloud of points that only include the color information of the point, and Cat1C represents the point cloud of points that include both the color information and reflectance information of the point , Cat2 represents the point cloud of the point including the reflectance information of the point and other attribute information, and Cat3 represents the point cloud of the point including the color information of the point and other attribute information. Table 4 is the BD-rate of the color information from Cat1A to Cat1A+Cat2 in the case of lossless geometry (lossless geometry) compression and lossy attribute compression and the value of λ is 0.5. Table 5 shows the BD-rate of each component of Cat1A to Cat1A+Cat2 in the case of lossless geometry compression and lossy attribute compression and the value of λ is 0.8. Table 6 shows the BD-rate of each component of Cat1A to Cat1A+Cat2 under the condition of limiting lossy geometry compression and lossy attribute compression and the value of λ is 0.8. The overall mean (overall) represents the combined performance of all sequences. Representative rate distortion (Bit distortion, BD-rate) is an indicator to measure algorithm performance or coding performance, which indicates the transformation of the coding algorithm provided by this application in terms of code rate and PSNR compared to the original coding algorithm, and the overall negative value indicates performance get better. L, Cb, and Cr (also known as Y, U, V) represent the performance of the three components of the brightness and chromaticity of the point cloud color, and reflectance represents the performance of the reflectance attribute.

table 3

As shown in Table 3, in the case of limiting lossy geometry compression and lossy attribute compression and the value of λ is 0.5, the total average value of BD-rate on the L component can be increased by 7.6%, and the Cb The component can be improved by 1.7%, the Cr component can be improved by 4.4%, and the reflectance component can be improved by 2.5%, that is, the solution provided by the present application can improve the coding performance.

Table 4

As shown in Table 4, in the case of lossless geometric compression and lossy attribute compression and the value of λ is 0.5, the total average value of BD-rate can be increased by 7.2% on the L component, and on the Cb component can be improved by 6.5%, and the Cr component can be improved by 6.6%, that is, the solution provided by the present application can improve the encoding performance.

table 5

As shown in Table 5, in the case of limiting lossy geometry compression and lossy attribute compression and the value of λ is 0.8, the total average value of BD-rate on the L component can be increased by 7.6%, and the Cb The component can be improved by 2.0%, and the Cr component can be improved by 4.5%, that is, the solution provided by the present application can improve the coding performance.

Table 6

As shown in Table 6, in the case of lossless geometric compression and lossy attribute compression and the value of λ is 0.5, the total average value of BD-rate can be increased by 8.3% on the L component, and on the Cb component can be improved by 6.9%, and the Cr component can be improved by 6.9%, that is, the solution provided by the present application can improve the coding performance.

The scheme of the present application is described below in conjunction with specific embodiments.

In this embodiment, the first quantized residual value of the current point is the quantized residual value attrResidualQuant1 determined based on the attribute prediction value obtained by the encoder on the attribute prediction of the current point, and attrResidualQuant2 obtained by setting attrResidualQuant1 to 0 is used as the current point’s At least one second quantized residual value. Based on this, the encoder can determine the final quantized residual value at the current point in attrResidualQuant1 and attrResidualQuant2, that is, the third quantized residual value mentioned above.

Exemplarily, after the encoder obtains the attribute prediction value of the current point through predictive transformation (Predicting Transform), it can determine the attribute reconstruction value Recon1 of the current point based on the attribute prediction value and attrResidualQuant1, and the encoder sets attrResidualQuant1 to 0 and uses it as attrResidualQuant2. Further, the encoder may determine the attribute reconstruction value Recon2 of the current point based on the attribute prediction value and attrResidualQuant2. Then, the encoder can calculate the rate-distortion cost of attrResidualQuant1 and the rate-distortion cost of attrResidualQuant2 based on the attribute reconstruction value Recon1 of the current point and the attribute reconstruction value Recon2 of the current point, and determine attrResidualQuant with the smallest rate-distortion cost as the final quantization of the current point The residual value, and finally encode the final quantized residual value of the current point to realize the encoding of the attribute information of the current point.

FIG. 18 is a schematic flowchart of an encoding method 400 provided by an embodiment of the present application. The method 400 can be executed by an encoder or an encoding framework, such as the encoding framework shown in FIG. 4 .

As shown in Figure 18, the encoding method 400 may include:

S410. The encoder acquires the current point cloud.

S420. The encoder performs attribute prediction on the current point to obtain an attribute prediction value of the current point.

For the G-PCC framework, when the encoder predicts the current point in the point cloud, it creates multiple prediction mode candidates based on the search results of neighbor points on the LOD where the current point is located. For example, when using the prediction method to encode the attribute information of the current point, the encoder first finds 3 neighbor points before the current point based on the neighbor point search results on the LOD where the current point is located, and creates 4 prediction mode candidates, That is, the prediction mode (predMode) whose index value is 0 to 3. Among them, the prediction mode with an index of 0 refers to determining the weighted average of the reconstructed attribute values of the three neighbor points as the attribute prediction value of the current point based on the distance between the three neighbor points and the current point; the prediction mode with an index of 1 refers to The attribute reconstruction value of the nearest neighbor point among the three neighbor points is used as the attribute prediction value of the current point; the prediction mode with index 2 refers to the attribute reconstruction value of the next nearest neighbor point as the attribute prediction value of the current point; the prediction mode with index 3 The mode refers to using the attribute reconstruction value of the neighbor points other than the nearest neighbor point and the next nearest neighbor point among the three neighbor points as the attribute prediction value of the current point; the candidate for the attribute prediction value of the current point is obtained based on the above-mentioned various prediction modes After the item, the encoder can use rate distortion optimization (Rate distortion optimization, RDO) technology to select the best attribute prediction value, and then perform arithmetic coding on the selected attribute prediction value. When the encoder determines the prediction mode used by the current point, it can first calculate the maximum difference maxDiff of its attributes for at least one neighbor point of the current point, compare maxDiff with the set threshold, if it is less than the set threshold (that is, the first A threshold) then use the prediction mode whose index is 0 (that is, the first prediction mode).

Exemplarily, when the encoder calculates the maximum difference maxDiff of the attributes of at least one neighbor point of the current point, it may first calculate the maximum difference of at least one neighbor point of the current point on each component; for example, at least one neighbor point of the current point is in The maximum difference on the R component is max(R1,R2,R3)-min(R1,R2,R3), and the maximum difference on the G and B components is max(G1,G2,G3)-min(G1,G2) ,G3) and max(B1,B2,B3)-min(B1,B2,B3); Then, select the maximum difference in the R, G, and B components as maxDiff, that is, maxDiff=max(max(R1,R2, R3)-min(R1,R2,R3),max(G1,G2,G3)-min(G1,G2,G3),max(B1,B2,B3)-min(B1,B2,B3)); encoding After obtaining maxDiff, the device can compare maxDiff with a set threshold (ie, the first threshold), and if it is less than the set threshold, the index of the prediction mode at the current point is 0, that is, predMode=0.

Exemplarily, when the encoder determines the prediction mode used by the current point, it can first calculate the maximum difference maxDiff of its attributes for at least one neighbor point of the current point, compare maxDiff with the set threshold, and if it is greater than or equal to the set threshold, the encoder can use RDO technology to determine the index of the prediction mode used by the current point for the current point. For RDO technology, the encoder can calculate the corresponding rate-distortion cost for each prediction mode of the current point, and then select the prediction mode with the smallest rate-distortion cost, that is, the optimal prediction mode as the attribute prediction mode of the current point.

For the AVS-PCC framework, the encoder can reorder the current point cloud and perform attribute prediction. Optionally, the reordering methods include Morton reordering and Hilbert reordering. The encoder can obtain the encoding order of the current point cloud by reordering the current point cloud. After the encoder obtains the encoding order of the current point cloud, it can predict the attribute information of the points in the point cloud according to the encoding order of the current point cloud. Optionally, if the geometric information of the current point is the same as that of the encoded point before the encoding sequence, that is, it is a repeated point, then the attribute reconstruction value of the repeated point is used as the attribute prediction value of the current point. Optionally, if the geometric information of the current point is not the same as that of the encoded points before the encoding order, that is, it is not a repeated point, the encoder can select the first m points as the neighbor candidate points of the current point according to the encoding order, and then calculate The Manhattan distance between m points and the geometric information of the current point, and determine the nearest n points as the neighbor points of the current point, and then the encoder can use the reciprocal of the distance as the weight to calculate the weighted average of the attributes of the n neighbors, As the predicted value of the attribute at the current point.

Exemplarily, the encoder can obtain the attribute prediction value of the current point in the following ways:

attrPred=(1/W ₁ ×ref ₁ +1/W ₂ ×ref ₂ +1/W ₃ ×ref ₃ )/(1/W ₁ +1/W ₂ +1/W ₃ ).

Among them, W ₁ , W ₂ , and W ₃ represent the geometric distances between neighbor point 1, neighbor point 2, neighbor point 3 and the current point, respectively, and ref ₁ , ref ₂ , and ref ₃ represent neighbor point 1, neighbor point 2, and neighbor point respectively. A property reconstruction value of 3.

S430. The encoder determines a residual value of the current point based on the original value of the attribute and the predicted value of the attribute of the current point.

S440. The encoder quantizes the residual value of the current point to obtain the quantized residual value attrResidualQuant1 of the current point.

Exemplarily, after the encoder acquires the predicted attribute value of the current point, attrResidualQuant1 can be determined by the following formula:

attrResidualQuant1 = (attrValue-attrPred)/Qstep;

Among them, attrPred represents the attribute prediction value of the current point, attrValue represents the original value of the attribute of the current point, and Qstep represents the quantization step size. Among them, Qstep is calculated by the quantization parameter (Quantization Parameter, Qp).

S441. The encoder determines the attribute reconstruction value Recon1 of the current point based on attrResidualQuant1 and the attribute prediction value of the current point.

Exemplarily, the encoder can reconstruct the value Recon1 based on the attribute of the current point determined by attrResidualQuant1 through the following formula:

Recon1=attrResidualQuant1×Qstep+attrPred;

Wherein, Recon1 indicates the attribute reconstruction value of the current point determined based on attrResidualQuant1, Qstep indicates the quantization step size, and attrPred indicates the attribute prediction value of the current point. Among them, Qstep is calculated by the quantization parameter (Quantization Parameter, Qp).

S442. The encoder determines the rate-distortion cost J ₁ of attrResidualQuant1 based on Recon1.

Exemplarily, the encoder calculates the rate-distortion cost of attrResidualQuant1 according to the following formula:

J ₁ =D ₁ +λ×R ₁ ;

Among them, J ₁ represents the rate-distortion cost of attrResidualQuant1, D ₁ represents the error between the original attribute value attrValue of the current point and Recon1, λ is determined according to the quantization parameter of the current point, and R ₁ represents attrResidualQuant1 in the code stream The number of bits required in .

Exemplarily, D can be obtained by the following formula:

D ₁ = |attrValue-Recon1|;

Wherein, attrValue represents the original value of the attribute of the current point, and Recon1 represents the reconstruction value of the attribute of the current point determined based on attrResidualQuant1.

S450. The encoder determines the quantization residual value attrResidualQuant2 of the current point based on attrResidualQuant1.

Exemplarily, the encoder sets attrResidualQuant1 to 0 and uses it as attrResidualQuant2. That is, attrResidualQuant2=0.

S451. The encoder determines the attribute reconstruction value Recon2 of the current point based on attrResidualQuant2 and the attribute prediction value of the current point.

Exemplarily, the encoder can reconstruct the value Recon2 based on the attribute of the current point determined by attrResidualQuant2 through the following formula:

Recon2=attrResidualQuant2×Qstep+attrPred;

Wherein, since attrResidualQuant2=0, the attribute reconstruction value of the current point obtained based on attrResidualQuant2 is equal to the attribute prediction value of the current point, that is, Recon2=attrPred.

S452. The encoder determines the rate-distortion cost J ₂ of attrResidualQuant2 based on the Recon2.

Exemplarily, the encoder calculates the rate-distortion cost of the quantized residual value attrResidualQuant1 according to the following formula:

J ₂ =D ₂ +λ×R ₂ ;

Among them, J ₂ represents the rate-distortion cost of attrResidualQuant2, D ₂ represents the error between the original attribute value attrValue of the current point and Recon2, λ ₂ is determined according to the quantization parameter of the current point, and R ₁ represents attrResidualQuant2 in the code The desired number of bits in the stream.

Exemplarily, _D2 can be obtained by the following formula:

D ₂ =|attrValue-Recon2|;

Wherein, attrValue represents the original value of the attribute of the current point, and Recon2 represents the reconstruction value of the attribute of the current point determined based on attrResidualQuant2. That is, when attrResidualQuant2=0, Recon2=attrPred.

S460, the encoder judges that J ₁ >J ₂ ?

S461. If J ₁ >J ₂ , the encoder encodes attrResidualQuant2.

For example, if J ₁ >J ₂ , it means that the encoding cost of attrResidualQuant1 is relatively high. At this time, the encoding scheme of attrResidualQuant2 can be selected, that is, after attrResidualQuant1 is set to 0, it will be used as the final quantized residual value of the current point and encoded. That is to say, the encoder determines attrResidualQuant2 as the final quantized residual value at the current point (that is, the third quantized residual value mentioned above), and encodes attrResidualQuant2.

S462. If J ₁ ≤ J ₂ , the encoder encodes attrResidualQuant1.

For example, if J ₁ ≤ J ₂ , it means that the encoding cost of attrResidualQuant1 is relatively small. At this time, the encoding scheme of attrResidualQuant1 can be selected, that is, attrResidualQuant1 is directly used as the final quantized residual value of the current point and encoded, that is, That is, the encoder determines attrResidualQuant1 as the final quantized residual value at the current point (that is, the third quantized residual value mentioned above), and encodes attrResidualQuant1.

In this embodiment, the encoder calculates the rate-distortion cost of attrResidualQuant1 and the rate-distortion cost of attrResidualQuant2, and determines attrResidualQuant with the smallest rate-distortion cost as the final quantized residual value of the current point, and finally calculates the final quantized residual value of the current point Encoding is performed to realize the encoding of the attribute information of the current point, which not only makes the quantization scales of attrResidualQuant1 and attrResidualQuant2 different, but also makes the rate-distortion cost of attrResidualQuant1 and attrResidualQuant2 take into account the reconstruction quality and encoding of the current point at the same time Efficiency, so that the final quantized residual value selected at the current point is the third quantized residual value with the best quantization scale, which avoids low coding efficiency or low reconstruction quality at the current point, and improves the coding performance of the encoder.

Certainly, FIG. 18 is only an example of the present application, and should not be construed as limiting the present application.

For example, in FIG. 18 , the encoder sets attrResidualQuant2 obtained by setting attrResidualQuant1 to 0 as at least one second quantized residual value at the current point, but the present application is not limited thereto. For example, in other alternative embodiments, when the value of the first quantization residual value is N, the number of the at least one second quantization residual value is N-1, and the at least one second The values of the quantized residual values are respectively: 0,...,N-1. Wherein, N is a positive integer. Based on this, the encoder can obtain N quantized residual values and attribute reconstruction values, that is, the rate-distortion cost of each quantized residual value in the N quantized residual values can be obtained, that is, J ₁ , J _{2 ,} … , J _N . For example, assuming that the first quantized residual at the current point is 10, the at least one second quantized residual value can be set to 0-9 in sequence, and then 10 attribute reconstruction values can be obtained, at this time, the utilization rate distortion cost The calculation formula can get 10 rate-distortion costs, that is, J ₁ , J _2, ..., J ₁₀ ; then compare the size of J ₁ , J _2, ..., J ₁₀ , and choose the rate-distortion cost Min(J ₁ , J _2, ..., J ₁₀ ) and determine the quantized residual value corresponding to the minimum rate-distortion cost as the final quantized residual value of the current point, and finally encode the final quantized residual value of the current point, and repeat the above process, Until all the points in the current point cloud are encoded, the attribute information of the current point is encoded.

The present application also provides a decoding method corresponding to the above encoding method.

Fig. 19 is a schematic flowchart of a decoding method provided by an embodiment of the present application.

As shown in Figure 19, the decoder first obtains APS and ABH by decoding the attribute code stream for subsequent use, then obtains the attribute prediction value attrPred of the first point from the attribute code stream, and obtains the first point from the attribute code stream The quantized residual value of the point, after inverse quantization, the residual value of the first point can be obtained. Based on this, the decoder can obtain the predicted value of the attribute of the first point based on the residual value of the first point. The attribute reconstruction value of the first point, the attribute reconstruction value of the first point can be used as the neighbor candidate of the subsequent point, and then the quantized residual value of the second point is parsed from the attribute code stream, dequantized and The result of dequantization is added to the attribute prediction value of the second point to obtain the attribute reconstruction value of the second point, and so on until the last point of the point cloud is decoded.

Exemplarily, the decoder may dequantize the quantized residual value of the current point (ie, the third quantized residual value involved in the present application) based on the following formula to obtain the residual value of the current point:

attrResidual=attrResidualQuant×Qstep;

Among them, attrResidual indicates the residual value of the current point, attrResidualQuant indicates the quantization residual value of the current point, and Qstep indicates the quantization step size. Among them, Qstep is calculated by the quantization parameter (Quantization Parameter, Qp).

Exemplarily, the decoder can obtain the attribute reconstruction value of the current point based on the following formula:

Recon = attrResidual + attrPred;

Wherein, Recon represents the attribute reconstruction value of the current point determined based on the quantized residual value of the current point, attrResidual represents the residual value of the current point, and attrPred represents the attribute prediction value of the current point.

The preferred embodiments of the present application have been 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 scope of the technical concept of the present application, various simple modifications can be made to the technical solutions of the present application. These simple modifications all belong to the protection scope of the present application. For example, the various specific technical features described in the above specific implementation manners can be combined in any suitable manner if there is no contradiction. Separately. As another example, any combination of various implementations of the present application can also be made, as long as they do not violate the idea of the present application, they should also be regarded as the content disclosed in the present application. It should also be understood that, in various method embodiments of the present application, the sequence numbers of the above-mentioned processes do not mean the order of execution, and the order of execution of the processes should be determined by their functions and internal logic, and should not be used in this application. The implementation of the examples constitutes no limitation.

The encoder or decoder provided by the embodiments of the present application will be described below with reference to the accompanying drawings.

FIG. 20 is a schematic block diagram of an encoder 500 provided by an embodiment of the present application.

As shown in Figure 20, the encoder 500 may include:

Determining unit 510, configured to:

Based on the geometric information of the current point cloud, determine the encoding sequence of the attribute information of the current point cloud;

Determining a first quantized residual value of the current point based on at least one neighbor point before the current point to be encoded in the encoding order;

determining at least one second quantized residual value for the current point;

determining a third quantized residual value based on the first quantized residual value and the at least one second quantized residual value;

The encoding unit 520 is configured to encode the third quantized residual value to obtain a code stream.

In some embodiments, the determining unit 510 is specifically configured to:

Based on the first quantized residual value, the at least one second quantized residual value is determined.

In some embodiments, the determining unit 510 is specifically configured to:

An integer smaller than the first quantized residual value is determined as the at least one second quantized residual value.

In some embodiments, the determining unit 510 is specifically configured to:

A default residual value, is determined as the at least one second quantized residual value.

In some embodiments, the determining unit 510 is specifically configured to:

calculating a rate-distortion cost for the first quantized residual value and a rate-distortion cost for the second quantized residual value;

A quantized residual value having the smallest rate-distortion cost among the rate-distortion cost of the first quantized residual value and the rate-distortion cost of the second quantized residual value is determined as the third quantized residual value.

In some embodiments, the rate-distortion cost of the first quantized residual value is calculated in the same manner as the rate-distortion cost of the second quantized residual value.

In some embodiments, the determining unit 510 is specifically configured to:

Calculate the rate-distortion cost of the first quantized residual value or the second quantized residual value according to the following formula:

J ₁ =D ₁ +λ×R ₁ ;

Wherein, when J ₁ represents the rate-distortion cost of the first quantized residual value, D ₁ represents the original attribute value of the current point and the reconstructed attribute value of the current point determined based on the first quantized residual value The error between , λ is determined according to the quantization parameter of the current point, R ₁ represents the number of bits required by the first quantization residual value in the code stream; or, J ₁ represents the second quantization residual value When the rate-distortion cost of the difference, D ₁ represents the error between the original attribute value of the current point and the attribute reconstruction value of the current point determined based on the second quantized residual value, and λ is based on the current point The quantization parameter is determined, and R ₁ represents the number of bits required by the second quantization residual value in the code stream.

In some embodiments, λ is determined according to the quantization parameter of the current point and at least one of the following:

The sequence type of the current point cloud and the current component of the current point.

In some embodiments, the determining unit 510 is specifically configured to:

Based on at least one neighbor point before the current point to be encoded in the encoding order, determine the prediction mode adopted by the current point;

Based on the prediction mode adopted by the current point, determine the first quantization residual value of the current point.

In some embodiments, the determining unit 510 is specifically configured to:

determining the attribute difference of the at least one neighbor point based on the difference in each component of the at least one neighbor point;

If the attribute difference of the at least one neighbor point is less than the first threshold, the first prediction mode is determined as the prediction mode adopted by the current point; the first prediction mode refers to a weighted average based on the at least one neighbor point A prediction mode for predicting the attribute value of the current point.

In some embodiments, the determining unit 510 is specifically configured to:

The attribute difference of the at least one neighbor point is determined by using the maximum difference among the differences of the at least one neighbor point on each component.

In some embodiments, the coding unit 520 is also used for:

If the attribute difference of the at least one neighbor point is greater than or equal to the first threshold, determine the prediction mode with the smallest rate-distortion cost in the at least one second prediction mode as the prediction mode adopted by the current point; the at least A second prediction mode has a one-to-one correspondence with the at least one neighbor point, and the second prediction mode refers to using the attribute reconstruction value of the neighbor point corresponding to the second prediction mode among the at least one neighbor point as the current The prediction mode of the predicted value of the attribute of the point;

Write the prediction mode adopted by the current point into the code stream.

In some embodiments, the determining unit 510 is further configured to:

Determine the quantized residual value of the current point on each component based on the attribute reconstruction value on each component of the neighbor point corresponding to the second prediction mode;

Determining the sum of the quantized residual values of the current point on each component as the rate-distortion cost of the second prediction mode.

In some embodiments, the determining unit 510 is specifically configured to:

Determine the predicted attribute value of the current point based on the prediction mode adopted by the current point;

calculating the difference between the original value of the attribute of the current point and the predicted value of the attribute of the current point;

Determining the ratio of the difference to the quantization step used by the current point as the first quantization residual value; the quantization step used by the current point is determined according to the quantization parameter of the current point.

In some embodiments, the encoding order comprises a layer of detail LOD order.

In some embodiments, the determining unit 510 is specifically configured to:

Determining the weighted average of the at least one neighbor point as the attribute prediction value of the current point;

calculating the difference between the original value of the attribute of the current point and the predicted value of the attribute of the current point;

Determining the ratio of the difference to the quantization step used by the current point as the first quantization residual value; the quantization step used by the current point is determined according to the quantization parameter of the current point.

In some embodiments, the encoding order comprises a Morton's order or a Hilbert order.

It should be noted that the encoder 500 can also be combined with the encoding framework shown in FIG. 4 , that is, units in the encoder 500 can be replaced or combined with relevant parts in the encoding framework. For example, the determining unit 510 may correspond to the attribute transformation (Transfer attributes) unit 111, the region adaptive hierarchical transformation (Region Adaptive Hierarchical Transform, RAHT) unit 112, the predicting change (predicting transform) unit 113 or the lifting change in the encoding framework. (lifting transform) unit 114, and for another example, the coding unit 520 may correspond to the second arithmetic coding unit 116 in the coding framework.

It should be understood that the device embodiment and the method embodiment may correspond to each other, and similar descriptions may refer to the method embodiment. To avoid repetition, details are not repeated here. Specifically, the encoder 500 may correspond to the corresponding subject in the method 300 or the method 400 of the embodiment of the present application, and each unit in the encoder 500 is to realize the corresponding process in the method 300 or the method 400 respectively, for the sake of brevity, in This will not be repeated here.

It should also be understood that the various units in the encoder 500 involved in the embodiment of the present application may be separately or all combined into one or several other units to form, or one (some) of the units may be further divided into functional Multiple smaller units can realize the same operation without affecting the realization of the technical effects of the embodiments of the present application. The above-mentioned units are divided based on logical functions. In practical applications, the functions of one unit may also be realized by multiple units, or the functions of multiple units may be realized by one unit. In other embodiments of the present application, the encoder 500 may also include other units. In practical applications, these functions may also be assisted by other units, and may be implemented cooperatively by multiple units. According to another embodiment of the present application, a general-purpose computing device including a general-purpose computer such as a central processing unit (CPU), a random access storage medium (RAM), a read-only storage medium (ROM) and a storage element Run a computer program (including program code) capable of executing each step involved in the corresponding method on the computer to construct the encoder 500 involved in the embodiment of the present application and implement the encoding method provided in the embodiment of the present application. The computer program can be recorded in, for example, a computer-readable storage medium, and loaded on any electronic device with data processing capability through the computer-readable storage medium, and run in it to implement the corresponding method of the embodiment of the present application.

In other words, the units mentioned above can be implemented in the form of hardware, can also be implemented by instructions in the form of software, and can also be implemented in the form of a combination of software and hardware. Specifically, each step of the method embodiment in the embodiment of the present application can be completed by an integrated logic circuit of the hardware in the processor and/or instructions in the form of software, and the steps of the method disclosed in the embodiment of the present application can be directly embodied as hardware The execution of the decoding processor is completed, or the combination of hardware and software in the decoding processor is used to complete the execution. Optionally, the software may be located in mature storage media in the field such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, and registers. The storage medium is located in the memory, and the processor reads the information in the memory, and completes the steps in the above method embodiments in combination with its hardware.

FIG. 21 is a schematic structural diagram of a codec device 600 provided by an embodiment of the present application.

As shown in FIG. 21 , the codec device 600 includes at least a processor 610 and a computer-readable storage medium 620 . Wherein, the processor 610 and the computer-readable storage medium 620 may be connected through a bus or in other ways. The computer-readable storage medium 620 is used for storing a computer program 621 , and the computer program 621 includes computer instructions, and the processor 610 is used for executing the computer instructions stored in the computer-readable storage medium 620 . The processor 610 is the computing core and the control core of the codec device 600, which is suitable for realizing one or more computer instructions, and is specifically suitable for loading and executing one or more computer instructions so as to realize corresponding method procedures or corresponding functions.

As an example, the processor 610 may also be called a central processing unit (Central Processing Unit, CPU). The processor 610 may include but not limited to: a general-purpose processor, a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field programmable gate array (Field Programmable Gate Array, FPGA) Or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

As an example, the computer-readable storage medium 620 can be a high-speed RAM memory, or a non-volatile memory (Non-VolatileMemory), such as at least one disk memory; computer readable storage medium. Specifically, the computer-readable storage medium 620 includes, but is not limited to: volatile memory and/or non-volatile memory. Among them, the non-volatile memory can be read-only memory (Read-Only Memory, ROM), programmable read-only memory (Programmable ROM, PROM), erasable programmable read-only memory (Erasable PROM, EPROM), electronically programmable Erase Programmable Read-Only Memory (Electrically EPROM, EEPROM) or Flash. The volatile memory can be Random Access Memory (RAM), which acts as external cache memory. By way of illustration and not limitation, many forms of RAM are available, such as Static Random Access Memory (Static RAM, SRAM), Dynamic Random Access Memory (Dynamic RAM, DRAM), Synchronous Dynamic Random Access Memory (Synchronous DRAM, SDRAM), double data rate synchronous dynamic random access memory (Double Data Rate SDRAM, DDR SDRAM), enhanced synchronous dynamic random access memory (Enhanced SDRAM, ESDRAM), synchronous connection dynamic random access memory (synch link DRAM, SLDRAM) and Direct Memory Bus Random Access Memory (Direct Rambus RAM, DR RAM).

In one implementation, the codec device 600 may be the encoding framework shown in FIG. 6 or the encoder 500 shown in FIG. 20; the computer-readable storage medium 620 stores a first computer instruction; The first computer instructions stored in the computer-readable storage medium 620 are loaded and executed to implement corresponding steps in the encoding method provided in the embodiment of the present application. To avoid repetition, details are not repeated here.

According to another aspect of the present application, the embodiment of the present application further provides a computer-readable storage medium (Memory). The computer-readable storage medium is a memory device in the codec device 600 and is used to store programs and data. For example, computer readable storage medium 620 . It can be understood that the computer-readable storage medium 620 here may include a built-in storage medium in the codec device 600 , and of course may also include an extended storage medium supported by the codec device 600 . The computer-readable storage medium provides storage space, and the storage space stores the operating system of the codec device 600 . Moreover, one or more computer instructions adapted to be loaded and executed by the processor 610 are also stored in the storage space, and these computer instructions may be one or more computer programs 621 (including program codes). These computer instruction instructions are used for the computer to execute the coding methods provided in the above-mentioned various optional modes.

According to another aspect of the present application, there is provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. For example, computer program 621 . At this point, the codec device 600 may be a computer, the processor 610 reads the computer instructions from the computer-readable storage medium 620, and the processor 610 executes the computer instructions, so that the computer executes the encoding method provided in the above-mentioned various optional modes .

In other words, when implemented using software, it may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the procedures of the embodiments of the present application are run in whole or in part or the functions of the embodiments of the present application are realized. The computer can be a general purpose computer, a special purpose computer, a computer network, or other programmable device. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, from a website, computer, server, or data center via Wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) transmission to another website site, computer, server or data center.

Those skilled in the art can appreciate that the units and process steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may use different methods to implement the described functions for each specific application, but such implementation should not be regarded as exceeding the scope of the present application.

Finally, it should be noted that the above content is only a specific embodiment of the application, but the scope of protection of the application is not limited thereto. Anyone familiar with the technical field can easily think of Any changes or substitutions shall fall within the scope of protection of this application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims (21)

A coding method, characterized in that, comprising: Based on the geometric information of the current point cloud, determine the encoding sequence of the attribute information of the current point cloud; Determining a first quantized residual value of the current point based on at least one neighbor point before the current point to be encoded in the encoding order; determining at least one second quantized residual value for the current point; determining a third quantized residual value based on the first quantized residual value and the at least one second quantized residual value; Encoding the third quantized residual value to obtain a code stream. The method according to claim 1, wherein said determining at least one second quantized residual value of said current point comprises: Based on the first quantized residual value, the at least one second quantized residual value is determined. The method according to claim 2, wherein said determining said at least one second quantized residual value based on said first quantized residual value comprises: An integer smaller than the first quantized residual value is determined as the at least one second quantized residual value. The method according to claim 1, wherein said determining at least one second quantized residual value of said current point comprises: A default residual value, is determined as the at least one second quantized residual value. The method according to any one of claims 1 to 4, wherein said determining a third quantized residual value according to said first quantized residual value and said at least one second quantized residual value, include: calculating a rate-distortion cost for the first quantized residual value and a rate-distortion cost for the second quantized residual value; A quantized residual value having the smallest rate-distortion cost among the rate-distortion cost of the first quantized residual value and the rate-distortion cost of the second quantized residual value is determined as the third quantized residual value. The method according to claim 5, wherein the rate-distortion cost of the first quantized residual value is calculated in the same manner as the rate-distortion cost of the second quantized residual value. The method according to claim 5 or 6, wherein the calculating the rate-distortion cost of the first quantized residual value and the rate-distortion cost of the second quantized residual value comprises: Calculate the rate-distortion cost of the first quantized residual value or the second quantized residual value according to the following formula: J ₁ =D ₁ +λ×R ₁ ; Wherein, when J ₁ represents the rate-distortion cost of the first quantized residual value, D ₁ represents the original attribute value of the current point and the reconstructed attribute value of the current point determined based on the first quantized residual value The error between , λ is determined according to the quantization parameter of the current point, R ₁ represents the number of bits required by the first quantization residual value in the code stream; or, J ₁ represents the second quantization residual value When the rate-distortion cost of the difference, D ₁ represents the error between the original attribute value of the current point and the attribute reconstruction value of the current point determined based on the second quantized residual value, and λ is based on the current point The quantization parameter is determined, and R ₁ represents the number of bits required by the second quantization residual value in the code stream. The method according to claim 7, wherein λ is determined according to the quantization parameter of the current point and at least one of the following: The sequence type of the current point cloud and the current component of the current point. The method according to any one of claims 1 to 8, wherein the first quantization residual of the current point is determined based on at least one neighbor point before the current point to be coded in the coding order. difference, including: Based on at least one neighbor point before the current point to be encoded in the encoding order, determine the prediction mode adopted by the current point; Based on the prediction mode adopted by the current point, determine the first quantization residual value of the current point. The method according to claim 9, wherein the determining the prediction mode adopted by the current point based on at least one neighbor point before the current point to be encoded in the encoding order comprises: determining the attribute difference of the at least one neighbor point based on the difference in each component of the at least one neighbor point; If the attribute difference of the at least one neighbor point is less than the first threshold, the first prediction mode is determined as the prediction mode adopted by the current point; the first prediction mode refers to a weighted average based on the at least one neighbor point A prediction mode for predicting the attribute value of the current point. The method according to claim 10, wherein the determining the attribute difference of the at least one neighbor point based on the difference in each component of the at least one neighbor point comprises: The attribute difference of the at least one neighbor point is determined by using the maximum difference among the differences of the at least one neighbor point on each component. The method according to claim 10 or 11, wherein the method further comprises: If the attribute difference of the at least one neighbor point is greater than or equal to the first threshold, determine the prediction mode with the smallest rate-distortion cost in the at least one second prediction mode as the prediction mode adopted by the current point; the at least A second prediction mode has a one-to-one correspondence with the at least one neighbor point, and the second prediction mode refers to using the attribute reconstruction value of the neighbor point corresponding to the second prediction mode among the at least one neighbor point as the current The prediction mode of the predicted value of the attribute of the point; Write the prediction mode adopted by the current point into the code stream. The method according to claim 12, characterized in that the method further comprises: Determine the quantized residual value of the current point on each component based on the attribute reconstruction value on each component of the neighbor point corresponding to the second prediction mode; Determining the sum of the quantized residual values of the current point on each component as the rate-distortion cost of the second prediction mode. The method according to any one of claims 10 to 13, wherein the determining the first quantized residual value of the current point based on the prediction mode adopted by the current point includes: Determine the predicted attribute value of the current point based on the prediction mode adopted by the current point; calculating the difference between the original value of the attribute of the current point and the predicted value of the attribute of the current point; Determining the ratio of the difference to the quantization step used by the current point as the first quantization residual value; the quantization step used by the current point is determined according to the quantization parameter of the current point. The method according to any one of claims 9 to 14, wherein the encoding order comprises a layer of detail (LOD) order. The method according to any one of claims 1 to 8, wherein the first quantization residual of the current point is determined based on at least one neighbor point before the current point to be coded in the coding order. difference, including: Determining the weighted average of the at least one neighbor point as the attribute prediction value of the current point; calculating the difference between the original value of the attribute of the current point and the predicted value of the attribute of the current point; Determining the ratio of the difference to the quantization step used by the current point as the first quantization residual value; the quantization step used by the current point is determined according to the quantization parameter of the current point. The method according to claim 16, characterized in that the encoding order comprises Morton's reordering or Hilbert ordering. An encoder, characterized in that it comprises: Identify units for: Based on the geometric information of the current point cloud, determine the encoding sequence of the attribute information of the current point cloud; Determining a first quantized residual value of the current point based on at least one neighbor point before the current point to be encoded in the encoding order; determining at least one second quantized residual value for the current point; determining a third quantized residual value based on the first quantized residual value and the at least one second quantized residual value; A coding unit, configured to code the third quantized residual value to obtain a code stream. An encoding device, characterized in that it comprises: a processor adapted to execute a computer program; A computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and when the computer program is executed by the processor, the encoding method according to any one of claims 1 to 17 is implemented. A computer-readable storage medium is characterized in that it is used to store a computer program, the computer program causes a computer to execute the encoding method according to any one of claims 1 to 17. A code stream, characterized in that the code stream is the code stream generated by the method according to any one of claims 1 to 17.

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