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WO2025007353A9 - Procédés de codage et de décodage, flux de code, codeur, décodeur et support de stockage - Google Patents

Procédés de codage et de décodage, flux de code, codeur, décodeur et support de stockage Download PDF

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Publication number
WO2025007353A9
WO2025007353A9 PCT/CN2023/106176 CN2023106176W WO2025007353A9 WO 2025007353 A9 WO2025007353 A9 WO 2025007353A9 CN 2023106176 W CN2023106176 W CN 2023106176W WO 2025007353 A9 WO2025007353 A9 WO 2025007353A9
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Prior art keywords
point cloud
current point
azimuth
prediction value
value
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Chinese (zh)
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WO2025007353A1 (fr
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杨付正
霍俊彦
马彦卓
李明
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Guangdong Oppo Mobile Telecommunications Corp Ltd
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Guangdong Oppo Mobile Telecommunications Corp Ltd
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Priority to CN202380097073.4A priority Critical patent/CN121100528A/zh
Priority to PCT/CN2023/106176 priority patent/WO2025007353A1/fr
Publication of WO2025007353A1 publication Critical patent/WO2025007353A1/fr
Publication of WO2025007353A9 publication Critical patent/WO2025007353A9/fr
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/50—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding
    • H04N19/597—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding specially adapted for multi-view video sequence encoding

Definitions

  • the embodiments of the present application relate to the field of point cloud encoding and decoding technology, and in particular, to an encoding and decoding method, a bit stream, an encoder, a decoder, and a storage medium.
  • the geometry information of the point cloud and the attribute information corresponding to the points in the point cloud are encoded separately.
  • the coding and decoding of the geometry information can include octree-based geometry coding and decoding and prediction tree-based geometry coding and decoding.
  • the embodiments of the present application provide a coding and decoding method, a bit stream, an encoder, a decoder and a storage medium, which can improve the accuracy of the prediction effect, thereby improving the point cloud compression performance.
  • an embodiment of the present application provides a decoding method, which is applied to a decoder, and the method includes:
  • An initial prediction value corresponding to the current point cloud is determined according to the azimuth sampling information corresponding to the current point cloud, and a prediction value of a midpoint of the current point cloud is determined according to the initial prediction value.
  • an embodiment of the present application provides an encoding method, which is applied to an encoder, and the method includes:
  • An initial prediction value corresponding to the current point cloud is determined according to the azimuth sampling information corresponding to the current point cloud, and a prediction value of a midpoint of the current point cloud is determined according to the initial prediction value.
  • an embodiment of the present application provides a code stream, which is generated by bit encoding according to information to be encoded; wherein the information to be encoded includes at least:
  • Initialize mode identification information azimuth sampling information, radial distance information, quantization parameters, quantized prediction residual, and mode identification information.
  • an embodiment of the present application provides an encoder, the encoder comprising: a first determining unit and an encoding unit; wherein,
  • the first determining unit is configured to determine azimuth sampling information corresponding to the current point cloud
  • the encoding unit is configured to write the azimuth sampling information into a bit stream
  • the first determination unit is further configured to determine an initial prediction value corresponding to the current point cloud according to azimuth sampling information corresponding to the current point cloud, and determine a prediction value of a midpoint of the current point cloud according to the initial prediction value.
  • an embodiment of the present application provides an encoder, the encoder comprising a first memory and a first processor; wherein,
  • the first memory is used to store a computer program that can be run on the first processor
  • the first processor is used to execute the encoding method as described above when running the computer program.
  • an embodiment of the present application provides a decoder, the decoder comprising: a decoding unit and a second determining unit; wherein:
  • the decoding unit is configured to decode the code stream
  • the second determination unit is configured to determine the azimuth sampling information corresponding to the current point cloud; determine the initial prediction value corresponding to the current point cloud according to the azimuth sampling information corresponding to the current point cloud, and determine the prediction value of the midpoint of the current point cloud according to the initial prediction value.
  • an embodiment of the present application provides a decoder, the decoder comprising a second memory and a second processor; wherein:
  • the second memory is used to store a computer program that can be run on the second processor
  • the second processor is used to execute the decoding method as described above when running the computer program.
  • an embodiment of the present application provides a computer-readable storage medium, which stores a computer program.
  • the computer program When executed, it implements the decoding method as described in the first aspect, or implements the encoding method as described in the second aspect.
  • the embodiment of the present application provides a coding and decoding method, a code stream, an encoder, a decoder and a storage medium.
  • the code stream is decoded to determine the azimuth sampling information corresponding to the current point cloud; the initial prediction value corresponding to the current point cloud is determined according to the azimuth sampling information corresponding to the current point cloud, and the prediction value of the midpoint of the current point cloud is determined according to the initial prediction value.
  • the azimuth sampling information corresponding to the current point cloud is determined, and the azimuth sampling information is written into the code stream; the initial prediction value corresponding to the current point cloud is determined according to the azimuth sampling information corresponding to the current point cloud, and the prediction value of the midpoint of the current point cloud is determined according to the initial prediction value.
  • the corresponding initial prediction value can be determined by the azimuth sampling information corresponding to the current point cloud, which can fully consider the actual size of the azimuth of the root node of the current point cloud, and realize more reasonable initialization processing of the prediction value. That is to say, in the embodiment of the present application, the initialization of the prediction value in combination with the azimuth sampling information can improve the accuracy of the prediction effect, thereby improving the point cloud compression performance.
  • FIG1 is a schematic diagram of a network architecture of point cloud encoding and decoding provided in an embodiment of the present application
  • FIG. 2 is a schematic diagram of a composition framework of a G-PCC encoder
  • FIG3 is a schematic diagram of a composition framework of a G-PCC decoder
  • FIG4 is a schematic diagram of geometric coding
  • FIG5 is a schematic diagram of geometric decoding
  • FIG6 is a schematic diagram of an arrangement of geometric information
  • FIG. 7 is a schematic diagram of a first implementation flow of a point cloud decoding method proposed in an embodiment of the present application.
  • FIG8 is a second schematic diagram of the implementation flow of the point cloud decoding method proposed in an embodiment of the present application.
  • FIG9 is a third schematic diagram of the implementation flow of the point cloud decoding method proposed in an embodiment of the present application.
  • FIG10 is a schematic diagram of a first implementation flow of a point cloud encoding method proposed in an embodiment of the present application.
  • FIG. 11 is a second schematic diagram of the implementation flow of the point cloud encoding method proposed in an embodiment of the present application.
  • FIG. 12 is a third schematic diagram of the implementation flow of the point cloud encoding method proposed in the embodiment of the present application.
  • FIG13 is a schematic diagram of the structure of the encoder
  • FIG14 is a second schematic diagram of the structure of the encoder
  • FIG15 is a schematic diagram of the structure of a decoder
  • FIG. 16 is a second schematic diagram of the structure of the decoder.
  • PCC Point Cloud Compression
  • G-PCC Geometry-based Point Cloud Compression
  • V-PCC Video-based Point Cloud Compression
  • LOD Level of Detail
  • RAHT Region Adaptive Hierarchal Transform
  • CABAC Context-based Adaptive Binary Arithmetic Coding
  • CABAC Context-based Adaptive Binary Arithmetic Coding
  • Point cloud is a three-dimensional representation of the surface of an object.
  • Point cloud (data) of the surface of an object can be collected through acquisition equipment such as photoelectric radar, lidar, laser scanner, and multi-view camera.
  • a point cloud refers to a collection of massive three-dimensional points
  • the points in the point cloud may include the location information of the points and the attribute information of the points.
  • the location information of the points may be the three-dimensional coordinate information of the points.
  • the location information of the points may also be referred to as the geometric information of the points.
  • the attribute information of the points may include color information and/or reflectivity, etc.
  • the color information may be information on any color space.
  • the color information may be RGB information. Among them, R represents red (Red, R), G represents green (Green, G), and B represents blue (Blue, B).
  • the color information may be brightness and chromaticity (YCbCr, YUV) information. Among them, Y represents brightness, Cb (U) represents blue chromaticity, and Cr (V) represents red chromaticity.
  • the points in the point cloud may include the three-dimensional coordinate information of the points and the laser reflection intensity (reflectance) of the points.
  • the points in the point cloud may include the three-dimensional coordinate information of the points and the color information of the points.
  • a point cloud obtained by combining the principles of laser measurement and photogrammetry may include the three-dimensional coordinate information of the points, the laser reflection intensity (reflectance) of the points, and the color information of the points.
  • Point clouds can be divided into the following categories according to the way they are obtained:
  • the first type of static point cloud the object is stationary, and the device that obtains the point cloud is also stationary;
  • the second type of dynamic point cloud the object is moving, but the device that obtains the point cloud is stationary;
  • the third type of dynamic point cloud acquisition the device that acquires the point cloud is moving.
  • point clouds can be divided into two categories according to their usage:
  • Category 1 Machine perception point cloud, which can be used in autonomous navigation systems, real-time inspection systems, geographic information systems, visual sorting robots, disaster relief robots, etc.
  • Category 2 Point cloud perceived by the human eye, which can be used in point cloud application scenarios such as digital cultural heritage, free viewpoint broadcasting, 3D immersive communication, and 3D immersive interaction.
  • point clouds are a collection of massive points, storing point clouds not only consumes a lot of memory, but is also not conducive to transmission. There is also not enough bandwidth to support direct transmission of point clouds at the network layer without compression. Therefore, point clouds need to be compressed.
  • 3D point cloud has become a new generation of immersive multimedia after audio, image and video, and is widely used in applications such as virtual reality, augmented reality, autonomous driving and environmental modeling.
  • point cloud usually has a large amount of data, which is not conducive to its transmission and storage, so it is necessary to efficiently encode and decode the point cloud.
  • the point cloud coding framework that can compress the point cloud can be the G-PCC codec framework or the V-PCC codec framework provided by the Moving Picture Experts Group (MPEG), or the AVS-PCC codec framework provided by the Audio Video Standard (AVS).
  • MPEG Moving Picture Experts Group
  • AVS-PCC codec framework provided by the Audio Video Standard (AVS).
  • the G-PCC codec framework can be used to compress the first type of static point cloud and the third type of dynamically acquired point cloud
  • the V-PCC codec framework can be used to compress the second type of dynamic point cloud.
  • the G-PCC codec framework is mainly described here.
  • FIG1 is a schematic diagram of a network architecture of a point cloud encoding and decoding provided by the embodiment of the present application.
  • the network architecture includes one or more electronic devices 13 to 1N and a communication network 01, wherein the electronic devices 13 to 1N can perform video interaction through the communication network 01.
  • the electronic device can be various types of devices with point cloud encoding and decoding functions.
  • the electronic device can include a mobile phone, a tablet computer, a personal computer, a personal digital assistant, a navigator, a digital phone, a video phone, a television, a sensor device, a server, etc., which is not limited by the embodiment of the present application.
  • the decoder or encoder in the embodiment of the present application can be the above-mentioned electronic device.
  • the electronic device in the embodiment of the present application has a point cloud encoding and decoding function, generally including a point cloud encoder (ie, encoder) and a point cloud decoder (ie, decoder).
  • a point cloud encoder ie, encoder
  • a point cloud decoder ie, decoder
  • the point cloud data is first divided into multiple slices by slice division.
  • the geometric information of the point cloud and the attribute information corresponding to each point cloud are encoded separately.
  • FIG2 shows a schematic diagram of the composition framework of a G-PCC encoder.
  • the geometric information is transformed so that all point clouds are contained in a bounding box, and then quantized.
  • This step of quantization mainly plays a role in scaling. Due to the quantization rounding, the geometric information of a part of the point cloud is the same, so whether to remove duplicate points is determined based on parameters.
  • the process of quantization and removal of duplicate points is also called voxelization.
  • the Bounding Box is divided into octrees or a prediction tree is constructed.
  • arithmetic coding is performed on the points in the leaf nodes of the division to generate a binary geometric bit stream; or, arithmetic coding is performed on the intersection points (Vertex) generated by the division (surface fitting is performed based on the intersection points) to generate a binary geometric bit stream.
  • color conversion is required first to convert the color information (i.e., attribute information) from the RGB color space to the YUV color space. Then, the point cloud is recolored using the reconstructed geometric information so that the uncoded attribute information corresponds to the reconstructed geometric information. Attribute encoding is mainly performed on color information.
  • FIG3 shows a schematic diagram of the composition framework of a G-PCC decoder.
  • the geometric bit stream and the attribute bit stream in the binary bit stream are first decoded independently.
  • the geometric information of the point cloud is obtained through arithmetic decoding-reconstruction of the octree/reconstruction of the prediction tree-reconstruction of the geometry-coordinate inverse conversion;
  • the attribute information of the point cloud is obtained through arithmetic decoding-inverse quantization-LOD partitioning/RAHT-color inverse conversion, and the point cloud data to be encoded (i.e., the output point cloud) is restored based on the geometric information and attribute information.
  • FIG4 is a schematic diagram of geometric coding.
  • the geometric information is firstly transformed so that all point clouds are contained in a bounding box determined by two extreme points (0, 0, 0) and ( 2d , 2d , 2d). Then voxelization is performed, that is, quantization, rounding, and removal of duplicate points, wherein whether to remove duplicate points is determined according to the encoder parameters.
  • the geometric coding of G-PCC can be divided into: octree-based geometric coding and prediction tree-based geometric coding.
  • the input geometric information is first sorted.
  • the default sorting method of the current G-PCC standard test conditions is based on azimuth.
  • the cylindrical coordinates (r, y, z) of the input point cloud are calculated based on the Cartesian coordinates (x, y, z). ⁇ ), where r is the radial distance of the point, also known as the radius; is the azimuth angle of the point; ⁇ is the elevation angle of the laser beam to which the point belongs.
  • tan ⁇ are sorted in order. First compare If the size If they are the same, then compare r. If r is the same, then compare tan ⁇ . Finally, the entire point cloud is reordered.
  • the prediction tree geometry coding can be divided into an azimuth-based geometry prediction coding scheme and a KD-tree-based geometry prediction coding scheme.
  • the encoder when building the prediction tree, the encoder first converts the input point cloud from Cartesian coordinates (x, y, z) to cylindrical coordinates (r, ⁇ ). Each point in the input point cloud is then divided into different laser beams according to its pitch angle. Since the azimuth-based sorting method is used by default when sorting the input geometric information, the geometric information of the entire point cloud is arranged in ascending order of azimuth. For the i-th laser beam, the first point visited is the point with the smallest azimuth among all the points belonging to the laser beam. And the parent node of the points subsequently visited by the i-th beam is the first point in the same laser beam with a smaller azimuth than itself. The parent node of the first point of the i-th laser beam is the first point of the i-1-th laser beam. For the first point of the 0th laser beam, since it has no parent node, it serves as the root node of the entire tree.
  • the predicted value of the geometric information is first determined according to the intra-frame prediction algorithm or the inter-frame prediction algorithm.
  • intra-frame prediction a prediction list is used to predict the point to be coded.
  • inter-frame prediction the prediction point is found in the previous coded frame to predict the point to be coded.
  • the prediction value with the minimum bit rate is selected as the prediction value of the code point to be coded (decoded) through rate-distortion optimization.
  • the prediction residual is obtained by subtracting the actual value of the node geometry information from the predicted value, and the prediction residual is quantized using the quantization parameter.
  • the quantization parameter, the selected prediction value index, the prediction residual and other information are encoded to generate a binary code stream.
  • FIG5 is a schematic diagram of geometric decoding.
  • decoding is the reverse process of encoding.
  • the quantization coefficient and prediction index corresponding to each node are first decoded, and the corresponding prediction value is uniquely determined according to the prediction index; then the prediction residual of the geometric information is decoded and inversely quantized; then the geometric reconstruction information of the point to be decoded is restored according to the prediction value and the prediction residual; then the coordinate inverse transformation of the obtained reconstructed geometric information is performed to obtain the final reconstructed geometric information.
  • the original point cloud is reordered to build the prediction tree more efficiently.
  • the available sorting methods are unordered, Morton order, azimuth order, and radial distance order.
  • the default sorting method is to sort by azimuth, that is, to sort the points according to azimuth, radial distance, and tangent value of pitch angle in turn.
  • the encoder first converts the input point cloud from Cartesian coordinates (x, y, z) to cylindrical coordinates (r, ⁇ ). Then each point in the input point cloud is divided into different laser beams according to its pitch angle. ⁇ ) can be converted to (r, i), where i represents the index of the laser beam. Since the default sorting method based on azimuth is used when sorting the input geometric information, the geometric information of the entire point cloud is arranged in ascending order of azimuth.
  • FIG6 is a schematic diagram of the arrangement of geometric information.
  • the first point visited is the point with the smallest azimuth angle among all the points belonging to the laser beam.
  • the parent node of the points subsequently visited by the i-th laser beam is the first point with a smaller azimuth angle than itself in the same laser beam.
  • the parent node of the first point of the i-th laser beam is the first point of the i-1-th laser beam.
  • For the first point of the 0-th laser beam since it has no parent node, it serves as the root node of the entire tree.
  • the cylindrical coordinates of the input point cloud (r, i) is initialized to (r min , 0, 0).
  • the code point to be encoded (decoded) is the root node of the prediction tree
  • its cylindrical coordinates (r, i) is (r min , 0, 0)
  • r min is the minimum radius of the current input point cloud, which is obtained by traversing at the encoder and transmitted to the decoder through pgeom_min_radius. Since the azimuth is in the range of (- ⁇ , ⁇ ], considering that the geometric information of the input point cloud is arranged in ascending order of azimuth, the azimuth of the root node is very close to - ⁇ . It is unreasonable to use 0 as the predicted value of the root node azimuth.
  • the code stream is decoded to determine the azimuth sampling information corresponding to the current point cloud; the initial prediction value corresponding to the current point cloud is determined according to the azimuth sampling information corresponding to the current point cloud, and the prediction value of the midpoint of the current point cloud is determined according to the initial prediction value.
  • the azimuth sampling information corresponding to the current point cloud is determined, and the azimuth sampling information is written into the code stream; the initial prediction value corresponding to the current point cloud is determined according to the azimuth sampling information corresponding to the current point cloud, and the prediction value of the midpoint of the current point cloud is determined according to the initial prediction value.
  • the corresponding initial prediction value in the process of initializing the prediction value of the current point cloud, can be determined by the azimuth sampling information corresponding to the current point cloud, which can fully consider the actual size of the azimuth of the root node of the current point cloud, and realize more reasonable initialization processing of the prediction value. That is to say, in the embodiment of the present application, the initialization of the prediction value in combination with the azimuth sampling information can improve the accuracy of the prediction effect, thereby improving the point cloud compression performance.
  • the point cloud encoding method in the embodiment of the present application can be applied to the predictive encoding and arithmetic encoding parts as shown in Figure 4.
  • the point cloud decoding method in the embodiment of the present application can also be applied to the predictive decoding and arithmetic decoding parts as shown in Figure 5.
  • the point cloud encoding and decoding method in the embodiment of the present application can be applied to both the encoder and the decoder, and can even be applied to both the encoder and the decoder at the same time, but the embodiment of the present application does not make specific limitations.
  • FIG. 7 is a schematic diagram of a first implementation flow of the point cloud decoding method proposed in the embodiment of the present application.
  • the method for the decoder to perform point cloud decoding processing may include the following steps:
  • Step 101 Decode the code stream to determine the azimuth sampling information corresponding to the current point cloud.
  • the decoder may first decode the code stream, thereby determining the azimuth sampling information corresponding to the current point cloud.
  • the azimuth sampling information corresponding to the current point cloud can determine the azimuth sampling interval corresponding to the current point cloud.
  • the azimuth sampling information corresponding to the current point cloud can be represented in the form of syntax elements, that is, the decoder decodes the code stream to obtain the syntax elements representing the azimuth sampling information, thereby determining the azimuth sampling information.
  • the azimuth sampling information corresponding to the current point cloud and representing the azimuth sampling interval can be directly obtained, and the relevant information used to calculate the azimuth sampling information can also be obtained, and then the azimuth sampling information corresponding to the current point cloud and representing the azimuth sampling interval can be derived based on the relevant information.
  • the code stream when determining the azimuth sampling information, can also be decoded to determine the first azimuth parameter, and then the azimuth sampling information corresponding to the current point cloud can be further determined based on the first azimuth parameter and a preset value.
  • the preset value can be any preset value, and the present application does not specifically limit it.
  • the preset value can be 1.
  • the azimuth sampling information corresponding to the current point cloud can be directly determined by decoding the bit stream, that is, the azimuth sampling interval
  • the first azimuth parameter geom_angular_azimuth_speed_minus1 can be determined by decoding the bit stream, and then the corresponding azimuth sampling information, that is, the azimuth sampling interval, is calculated by the first azimuth parameter geom_angular_azimuth_speed_minus1 and a preset value (such as 1). For example, formula (1):
  • the syntax element geom_angular_azimuth_speed_minus1 can be transmitted from the encoding end to the decoding end through the bit stream, and the geom_angular_azimuth_speed_minus1 can be a syntax element in a geometric parameter set (GPS).
  • GPS geometric parameter set
  • the code stream while decoding the code stream to determine the azimuth sampling information corresponding to the current point cloud, the code stream can also be decoded to determine the radial distance information corresponding to the current point cloud.
  • the radial distance information corresponding to the current point cloud can determine the minimum radial distance corresponding to the current point cloud, that is, the minimum radius.
  • the radial distance information corresponding to the current point cloud can be represented in the form of a syntax element, that is, the decoder decodes the code stream to obtain the syntax element representing the radial distance information, thereby determining the radial distance information.
  • the syntax element pgeom_min_radius representing the radial distance information may be determined by decoding the bitstream, and then the corresponding radial distance information r min may be determined by pgeom_min_radius.
  • the syntax element pgeom_min_radius can be transmitted from the encoding end to the decoding end through the bit stream, and the pgeom_min_radius can be a syntax element in the geometry block header (GPS) information.
  • GPS geometry block header
  • the point cloud decoding method proposed in the embodiment of the present application can be used in the geometric decoding process based on the prediction tree, wherein the current point cloud can be the point cloud to be decoded.
  • the geometric information of the point in the current point cloud can be expressed in the form of cylindrical coordinates.
  • the geometric information of the point in the current point cloud can be expressed as (r, i), where r is the radial distance of the point, also known as the radius; is the azimuth angle of the point; ⁇ is the elevation angle of the laser beam to which the point belongs.
  • the geometric information of the point in the current point cloud can be represented by a radial distance component, an azimuth component, and a pitch angle component.
  • Step 102 determine an initial prediction value corresponding to the current point cloud according to the azimuth sampling information corresponding to the current point cloud, and determine a prediction value of a midpoint of the current point cloud according to the initial prediction value.
  • the prediction value after decoding the code stream and determining the azimuth sampling information corresponding to the current point cloud, the prediction value can be initialized according to the azimuth sampling information corresponding to the current point cloud, and the initial prediction value corresponding to the current point cloud can be determined, so that the prediction value of the midpoint of the current point cloud can be further determined based on the initial prediction value.
  • the azimuth sampling information corresponding to the current point cloud can be used to determine the initial prediction value, and the radial distance information corresponding to the current point cloud can also be used to determine the initial prediction value.
  • the azimuth sampling information can be used to determine the azimuth component of the initial prediction value.
  • the radial distance information can be used to determine the radial distance component of the initial prediction value.
  • the initial prediction value corresponding to the current point cloud may be the initialized geometric information prediction value corresponding to the current point cloud.
  • the geometric information of any point in the current point cloud can be expressed as cylindrical coordinates (r, i), therefore, when initializing the prediction value of the current point cloud, the final obtained initial prediction value can also be represented by cylindrical coordinates, so the initial prediction value can also be represented by radial distance components, azimuth components, and pitch angle components.
  • the azimuth sampling information corresponding to the current point cloud can be used to determine the azimuth component in the initial predicted value.
  • the radial distance information corresponding to the current point cloud can be used to determine the radial distance component in the initial predicted value.
  • the azimuth component in the initial prediction value finally obtained is determined by the azimuth sampling information, and at the same time, the radial distance component is determined by the radial distance information.
  • the initialization parameters when determining the initial prediction value corresponding to the current point cloud based on the azimuth sampling information and the radial distance information, can be first determined based on the azimuth sampling information; and then the azimuth component corresponding to the initial prediction value can be determined based on the azimuth sampling information and the initialization parameters.
  • the azimuth sampling information can be used to calculate the initialization parameters, and the initialization parameters can be used to adjust and control the azimuth component in the process of initializing the predicted value. Then, the azimuth sampling information and the initialization parameters can be combined to further calculate the azimuth component in the initial predicted value.
  • the initialization parameters may be determined based on the azimuth sampling information in combination with any numerical value and by any calculation method.
  • the azimuth sampling information is the azimuth sampling interval Then according to Calculate the initialization parameter k:
  • round() is a Round function, which means returning a value that is the result of rounding to a specified number of decimal places.
  • the azimuth component of the initial prediction value may be determined by any calculation method in combination with any numerical value based on the azimuth sampling information and the initialization parameters.
  • the azimuth sampling information is the azimuth sampling interval
  • the initialization parameter is k, and the azimuth component is finally calculated.
  • the radial distance component corresponding to the initial prediction value can be directly determined based on the radial distance information.
  • the radial distance information is the minimum radial value r min of the current point cloud, and the radial distance component r 0 is finally calculated:
  • the radial distance component of the initial prediction value finally obtained is r min
  • the azimuth component is
  • the initial prediction value can be expressed as (r min , 0).
  • the initial prediction value after decoding the code stream and determining the radial distance information corresponding to the current point cloud, when determining the initial prediction value, you can also choose to determine the radial distance component of the initial prediction value based on the radial distance information, and set the azimuth component and the pitch angle component to fixed values, for example, set the azimuth component to 0, and set the pitch angle component to 0 at the same time.
  • a common initialization scheme may also be selected, and the initial prediction value finally obtained may be expressed as (r min , 0, 0).
  • the point cloud decoding method proposed in the embodiment of the present application when initializing the prediction value of the current point cloud, can choose to determine the azimuth component of the initial prediction value according to the azimuth sampling information corresponding to the current point cloud, so that the setting of the initial prediction value is more reasonable.
  • the prediction value of the midpoint of the current point cloud can be determined according to the initial prediction value.
  • the initial prediction value when determining the prediction value of a point in the current point cloud based on the initial prediction value, can be first determined as the prediction value of the root node of the prediction tree corresponding to the current point cloud; and then based on the prediction value of the root node, the prediction values of other points in the current point cloud are determined.
  • the code stream can be further decoded to determine the quantization parameter and the quantized prediction residual corresponding to the current point in the current point cloud; then the quantized prediction residual can be inverse quantized to determine the prediction residual corresponding to the current point; finally, the reconstruction value corresponding to the current point can be determined based on the prediction value and prediction residual corresponding to the current point.
  • the code stream when performing prediction processing on the geometric information of the current point, can also be decoded to determine the mode identification information corresponding to the current point; wherein the mode identification information is used to indicate whether the prediction mode corresponding to the current point is an inter-frame mode or an intra-frame mode.
  • the mode identification information of the current point in the current point cloud can be determined by decoding the code stream, thereby determining whether to adopt an intra-frame prediction mode or an inter-frame prediction mode for the current point according to the mode identification information, and finally determining the prediction value of the current point according to the corresponding prediction mode.
  • the quantization coefficient and the prediction index (mode identification information) corresponding to the current point are first decoded, and then the corresponding prediction value is uniquely determined according to the prediction index, wherein, for the root node of the current point cloud, the initial prediction value is the geometric prediction value determined after the initialization processing; then, the quantized prediction residual of the geometric information is decoded, and dequantized to obtain the prediction residual; then, the geometric reconstruction information of the current point is restored based on the prediction value and the prediction residual; then, the obtained reconstructed geometric information is subjected to a coordinate inverse transformation to obtain the final reconstructed geometric information.
  • FIG8 is a second schematic diagram of the implementation flow of the point cloud decoding method proposed in an embodiment of the present application.
  • the method for the decoder to perform point cloud decoding may further include the following steps:
  • Step 103 Determine initialization mode identification information.
  • initialization identification information may also be determined.
  • the initialization mode identification information may determine and indicate an initialization mode of a prediction value used by the current point cloud, wherein the initialization mode of a prediction value used by the current point cloud may include a first initialization mode and a second initialization mode.
  • the initialization mode used by the current point cloud is the first initialization mode or the second initialization mode.
  • the first initialization mode may represent a mode for initializing the prediction value using the azimuth angle related information transmitted in the code stream; the second initialization mode may represent a mode for initializing the prediction value without using the azimuth angle related information transmitted in the code stream.
  • a decoding code stream may be selected to determine the initialization mode identification information.
  • the initialization mode identification information can be transmitted from the encoding end to the decoding end through the code stream.
  • the initialization mode identification information when determining the initialization mode identification information, it is possible to choose to determine the initialization mode identification information through a syntax table corresponding to the current point cloud.
  • the initialization mode identification information can be set in the syntax table corresponding to the current point cloud, such as the syntax table.
  • the initialization mode identification information when the value of the initialization mode identification information is a first value, it can be determined that the initialization mode identification information indicates that the current point cloud uses the first initialization mode; when the value of the initialization mode identification information is a second value, it can be determined that the initialization mode identification information indicates that the current point cloud uses the second initialization mode.
  • the first value is different from the second value, and the first value and the second value may be in parameter form or in digital form.
  • the first value may be 0, and the second value may be 1.
  • the value of the initialization mode identification information when the value of the initialization mode identification information is 0, it can be determined that the first initialization mode is used to initialize the prediction value of the current point cloud, that is, the azimuth component of the initial prediction value of the current point cloud is determined according to the azimuth sampling information corresponding to the current point cloud; when the value of the initialization mode identification information is 1, it can be determined that the second initialization mode is used to initialize the prediction value of the current point cloud, that is, the initial prediction value of the current point cloud is not determined according to the azimuth sampling information corresponding to the current point cloud, but the azimuth component of the initial prediction value of the current point cloud is directly set to a third value, for example, the third value is 0.
  • the initialization mode identification information may be a parameter written in a profile, or may be a value of a flag, which is not specifically limited here.
  • the initialization mode identification information may be used as a syntax element at the GPS level or as a syntax element at the GBH level, and the present application does not make any specific limitation thereto.
  • Step 104 When the initialization mode identification information indicates that the current point cloud uses the first initialization mode, a process for determining the azimuth sampling information and the radial distance information is executed.
  • the process of determining the azimuth sampling information and radial distance information corresponding to the current point cloud can be executed, so that the predicted value of the current point cloud can be further initialized according to the azimuth sampling information and radial distance information corresponding to the current point cloud to determine the corresponding initial prediction value.
  • the azimuth component of the initial predicted value is determined according to the azimuth sampling interval (azimuth sampling information) corresponding to the current point cloud, for example, the azimuth component of the initial predicted value is determined as
  • Step 105 When the initialization mode identification information indicates that the current point cloud uses the second initialization mode, a radial distance information determination process is executed.
  • the process of determining the radial distance information corresponding to the current point cloud can be executed, so that the predicted value of the current point cloud can be further initialized according to the radial distance information corresponding to the current point cloud to determine the corresponding initial prediction value.
  • FIG. 9 is a schematic diagram of a third implementation flow of the point cloud decoding method proposed in an embodiment of the present application.
  • the method for performing point cloud decoding by the decoder may further include the following steps:
  • Step 106 When the initialization mode identification information indicates that the current point cloud uses the first initialization mode, determine the azimuth component of the initial prediction value according to the azimuth sampling information.
  • the initialization mode identification information indicates that the current point cloud uses the first initialization mode
  • Step 107 When the initialization mode identification information indicates that the current point cloud uses the second initialization mode, the azimuth component of the initial prediction value is set to a third value.
  • the azimuth component of the initial predicted value can be directly determined as the third value.
  • the third value can be any value, such as 0.
  • the azimuth component of the predicted value There are many ways to initialize the predicted value, and the specific method is indicated by the header information such as the initialization mode identification information.
  • a syntax element such as geom_angular_phi_ini_zero
  • the header information such as GPS or/and GBH
  • the syntax element can represent the cylindrical coordinates (r, i) Azimuth
  • the azimuth sampling information (such as the azimuth sampling threshold) transmitted in the code stream can be used to determine the azimuth component of the initial prediction value, instead of directly initializing the azimuth component of the initial prediction value to 0.
  • This prediction value initialization method is more reasonable and can effectively improve the prediction effect of geometric information.
  • the azimuth sampling information and radial distance information corresponding to the current point cloud can be determined first, and then the predicted value of the current point cloud can be initialized in combination with the azimuth sampling information and the radial distance information.
  • the azimuth component of the initial predicted value can be determined according to the azimuth sampling information, and at the same time, the radial distance component of the initial predicted value can be determined according to the radial distance information.
  • the decoding method proposed in the embodiment of the present application is experimentally verified based on the general test software TMC13V21 of G-PCC. Compared with it, the decoding method proposed in the embodiment of the present application is compared with the prediction tree geometry coding code stream performance under lossless conditions as shown in Table 1. It can be seen from Table 1 that the decoding method has some small gains in the geometry code streams of these point cloud sequences.
  • the embodiment of the present application discloses a point cloud decoding method, at the decoding end, the code stream is decoded to determine the azimuth sampling information corresponding to the current point cloud; the initial prediction value corresponding to the current point cloud is determined according to the azimuth sampling information corresponding to the current point cloud, and the prediction value of the midpoint of the current point cloud is determined according to the initial prediction value.
  • the corresponding initial prediction value can be determined by the azimuth sampling information corresponding to the current point cloud, which can fully consider the actual size of the azimuth of the root node of the current point cloud, and realize more reasonable initialization processing of the prediction value.
  • the initialization of the prediction value in combination with the azimuth sampling information can improve the accuracy of the prediction effect, thereby improving the point cloud compression performance.
  • FIG. 10 is a schematic diagram of a first implementation flow of the point cloud encoding method provided in the embodiment of the present application.
  • the method for the encoder to perform point cloud encoding processing may include the following steps:
  • Step 201 Determine the azimuth sampling information corresponding to the current point cloud, and write the azimuth sampling information into the bitstream.
  • the encoder may first determine the azimuth sampling information corresponding to the current point cloud, and may write the azimuth sampling information into a bit stream and transmit it to a decoding end.
  • the point cloud coding method proposed in the embodiment of the present application can be applied to the geometric coding process based on the prediction tree.
  • the input geometric information can be sorted first, for example, by sorting in the azimuth angle.
  • the encoder when building the prediction tree, the encoder first converts the input point cloud from Cartesian coordinates (x, y, z) to cylindrical coordinates (r, ⁇ ). Each point in the input point cloud is then divided into different laser beams according to its pitch angle. Since the azimuth-based sorting method is used by default when sorting the input geometric information, the geometric information of the entire point cloud is arranged in ascending order of azimuth. For the i-th laser beam, the first point visited is the point with the smallest azimuth among all the points belonging to the laser beam. And the parent node of the points subsequently visited by the i-th beam is the first point in the same laser beam with a smaller azimuth than itself. The parent node of the first point of the i-th laser beam is the first point of the i-1-th laser beam. For the first point of the 0th laser beam, since it has no parent node, it serves as the root node of the entire tree.
  • the predicted value of the geometric information is first determined according to the intra-frame prediction algorithm or the inter-frame prediction algorithm.
  • intra-frame prediction a prediction list is used to predict the point to be coded.
  • inter-frame prediction the prediction point is found in the previous coded frame to predict the point to be coded.
  • the prediction value with the minimum bit rate is selected as the prediction value of the code point to be coded (decoded) through rate-distortion optimization.
  • the prediction residual is obtained by subtracting the actual value of the node geometry information from the predicted value, and the prediction residual is quantized using the quantization parameter.
  • the quantization parameter, the selected prediction value index, the prediction residual and other information are encoded to generate a binary code stream.
  • the available sorting methods are unordered, Morton order, azimuth order and radial distance order.
  • the selected sorting method is sorting by azimuth, that is, sorting each point according to azimuth, radial distance and pitch tangent value in turn.
  • a prediction tree structure is established.
  • the encoder will first convert the input point cloud from Cartesian coordinates (x, y, z) to cylindrical coordinates (r, ⁇ ). Then each point in the input point cloud is divided into different laser beams according to its pitch angle. ⁇ ) can be converted to (r, i), where i represents the index of the laser beam.
  • the geometric information of the entire point cloud is arranged in ascending order of azimuth.
  • the first point visited is the point with the smallest azimuth among all the points belonging to the laser beam.
  • the parent node of the points subsequently visited by the i-th beam is the first point in the same laser beam with a smaller azimuth than itself.
  • the parent node of the first point of the i-th laser beam is the first point of the i-1-th laser beam.
  • the first point of the 0th laser beam since it has no parent node, it serves as the root node of the entire tree.
  • the azimuth sampling information corresponding to the current point cloud can determine the azimuth sampling interval corresponding to the current point cloud.
  • the azimuth sampling information corresponding to the current point cloud can be represented in the form of syntax elements, that is, after the encoder encodes the azimuth sampling information and transmits the code stream to the decoding end, the decoder decodes the code stream and obtains the syntax elements representing the azimuth sampling information, thereby determining the azimuth sampling information.
  • the encoder can directly write the azimuth sampling information corresponding to the current point cloud and representing the azimuth sampling interval into the bitstream, or it can choose to write the relevant information used to calculate the azimuth sampling information into the bitstream, so that the decoder can derive the azimuth sampling information corresponding to the current point cloud and representing the azimuth sampling interval based on the relevant information.
  • the encoder may write the first azimuth parameter into a code stream for transmission to a decoding end. Accordingly, when determining the azimuth sampling information, the decoder may decode the code stream, determine the first azimuth parameter, and then further determine the azimuth sampling information corresponding to the current point cloud based on the first azimuth parameter and a preset value.
  • the encoder may first determine the first azimuth parameter according to the azimuth sampling information and the preset value; and then write the first azimuth parameter into the bitstream.
  • the preset value can be any preset value, and the present application does not specifically limit it.
  • the preset value can be 1.
  • the encoder may directly send the azimuth sampling information corresponding to the current point cloud, such as the azimuth sampling interval Write code stream.
  • the encoder may first determine the first azimuth parameter geom_angular_azimuth_speed_minus1, and then write the first azimuth parameter into the bitstream. ) and a preset value (eg, 1) to calculate the corresponding first azimuth parameter geom_angular_azimuth_speed_minus1. For example, formula (1).
  • the syntax element geom_angular_azimuth_speed_minus1 can be transmitted from the encoding end to the decoding end through the bit stream, and the geom_angular_azimuth_speed_minus1 can be a syntax element in a geometric parameter set (GPS).
  • GPS geometric parameter set
  • the radial distance information corresponding to the current point cloud can also be determined, and the radial distance information corresponding to the current point cloud can be written into the code stream and transmitted to the decoding end.
  • the radial distance information corresponding to the current point cloud can determine the minimum radial distance corresponding to the current point cloud, that is, the minimum radius.
  • the radial distance information corresponding to the current point cloud can be represented in the form of a syntax element, that is, after the encoder encodes the radial distance information and transmits the code stream to the decoding end, the decoder decodes the code stream to obtain the syntax element representing the radial distance information, thereby determining the radial distance information.
  • the encoder may first determine the radial distance information r min , and then transmit the radial distance information r min through the syntax element pgeom_min_radius.
  • the decoder may first determine the syntax element pgeom_min_radius representing the radial distance information when decoding the bitstream, and then determine the corresponding radial distance information r min through pgeom_min_radius. For example, formula (2).
  • the syntax element pgeom_min_radius can be transmitted from the encoding end to the decoding end through the bit stream, and the pgeom_min_radius can be a syntax element in the geometry block header (GPS) information.
  • GPS geometry block header
  • the point cloud encoding method proposed in the embodiment of the present application can be used in the geometric encoding processing based on the prediction tree, wherein the current point cloud can be the point cloud to be encoded.
  • the geometric information of the point in the current point cloud can be expressed in the form of cylindrical coordinates.
  • the geometric information of the point in the current point cloud can be expressed as (r, i), where r is the radial distance of the point, also known as the radius; is the azimuth angle of the point; ⁇ is the elevation angle of the laser beam to which the point belongs.
  • the geometric information of the point in the current point cloud can be represented by a radial distance component, an azimuth component, and a pitch angle component.
  • Step 202 determine an initial prediction value corresponding to the current point cloud according to the azimuth sampling information corresponding to the current point cloud, and determine a prediction value of a midpoint of the current point cloud according to the initial prediction value.
  • the prediction value after determining the azimuth sampling information corresponding to the current point cloud, the prediction value can be initialized according to the azimuth sampling information corresponding to the current point cloud, and the initial prediction value corresponding to the current point cloud can be determined, so that the prediction value of the midpoint of the current point cloud can be further determined based on the initial prediction value.
  • the azimuth sampling information corresponding to the current point cloud can be used to determine the initial prediction value, and the radial distance information corresponding to the current point cloud can also be used to determine the initial prediction value.
  • the azimuth sampling information can be used to determine the azimuth component of the initial prediction value.
  • the radial distance information can be used to determine the radial distance component of the initial prediction value.
  • the initial prediction value corresponding to the current point cloud may be the initialized geometric information prediction value corresponding to the current point cloud.
  • the azimuth sampling information corresponding to the current point cloud can be used to determine the azimuth component in the initial predicted value.
  • the azimuth sampling information can be used to calculate the initialization parameters, and the initialization parameters can be used to adjust and control the azimuth component in the process of initializing the predicted value. Then, the azimuth sampling information and the initialization parameters can be combined to further calculate the azimuth component in the initial predicted value.
  • round() is a Round function, which means returning a value that is the result of rounding to a specified number of decimal places.
  • the azimuth component of the initial prediction value may be determined by any calculation method in combination with any numerical value based on the azimuth sampling information and the initialization parameters.
  • the azimuth sampling information is the azimuth sampling interval
  • the initialization parameter is k
  • the azimuth component is finally calculated. As shown in formula (4).
  • the radial distance component corresponding to the initial prediction value can be directly determined based on the radial distance information.
  • the radial distance information is the minimum radial value r min of the current point cloud, and the radial distance component r 0 is finally calculated, as shown in formula (5).
  • the radial distance component of the initial prediction value finally obtained is r min
  • the azimuth component is
  • the pitch angle component of the initial predicted value can also be set to a fixed value, for example, the pitch angle component can be set to 0.
  • the initial prediction value can be expressed as (r min , 0).
  • a common initialization scheme may also be selected, and the initial prediction value finally obtained may be expressed as (r min , 0, 0).
  • the point cloud encoding method proposed in the embodiment of the present application when initializing the prediction value of the current point cloud, can choose to determine the azimuth component of the initial prediction value according to the azimuth sampling information corresponding to the current point cloud, so that the setting of the initial prediction value is more reasonable.
  • the prediction value of the midpoint of the current point cloud can be determined according to the initial prediction value.
  • the initial prediction value when determining the prediction value of a point in the current point cloud based on the initial prediction value, can be first determined as the prediction value of the root node of the prediction tree corresponding to the current point cloud; and then based on the prediction value of the root node, the prediction values of other points in the current point cloud are determined.
  • the prediction residual corresponding to the current point can be further determined based on the prediction value corresponding to the current point in the current point cloud; then the prediction residual can be quantized according to the quantization parameter corresponding to the current point to determine the quantized coefficient residual corresponding to the current point.
  • the mode identification information corresponding to the current point when performing prediction processing on the geometric information of the current point, can also be determined; wherein the mode identification information is used to indicate whether the prediction mode corresponding to the current point is an inter-frame mode or an intra-frame mode.
  • the encoder can determine the mode identification information corresponding to the current point based on the rate-distortion algorithm, wherein, for the current point (the point to be encoded), the prediction value of the geometric information can be determined according to the intra-frame prediction algorithm or the inter-frame prediction algorithm, and then the prediction value with the smallest bit rate is selected as the prediction value of the current point through the rate-distortion optimization algorithm, and the corresponding mode identification information is determined at the same time.
  • the encoder may also write the mode identification information corresponding to the current point into the code stream and transmit it to the decoding end.
  • the mode identification information corresponding to the current point in the current point cloud can also be determined by a rate-distortion algorithm, and the mode identification information can be written into the bit stream.
  • the decoder determines the mode identification information of the current point in the current point cloud by decoding the bit stream, and thus determines whether to adopt an intra-frame prediction mode or an inter-frame prediction mode for the current point according to the mode identification information, and finally determines the prediction value of the current point according to the corresponding prediction mode.
  • FIG. 11 is a second schematic diagram of the implementation flow of the point cloud encoding method proposed in an embodiment of the present application.
  • the method for performing point cloud encoding by the encoder may further include the following steps:
  • Step 203 Determine initialization mode identification information.
  • initialization identification information may also be determined.
  • the initialization mode identification information may determine and indicate an initialization mode of a prediction value used by the current point cloud, wherein the initialization mode of a prediction value used by the current point cloud may include a first initialization mode and a second initialization mode.
  • the first initialization mode may represent a mode for initializing the prediction value using the azimuth angle related information transmitted in the code stream; the second initialization mode may represent a mode for initializing the prediction value without using the azimuth angle related information transmitted in the code stream.
  • the initialization mode identification information can be determined by the syntax table corresponding to the current point cloud. That is, in an embodiment of the present application, the initialization mode identification information can be set in the syntax table corresponding to the current point cloud, such as syntax table.
  • the value of the initialization mode identification information when it is determined that the current point cloud uses the first initialization mode, can be set to a first value; when it is determined that the current point cloud uses the second initialization mode, the value of the initialization mode identification information can be set to a second value.
  • the first value may be 0, and the second value may be 1.
  • the value of the initialization mode identification information when the value of the initialization mode identification information is 0, it can be determined that the first initialization mode is used to initialize the prediction value of the current point cloud, that is, the azimuth component of the initial prediction value of the current point cloud is determined according to the azimuth sampling information corresponding to the current point cloud; when the value of the initialization mode identification information is 1, it can be determined that the second initialization mode is used to initialize the prediction value of the current point cloud, that is, the initial prediction value of the current point cloud is not determined according to the azimuth sampling information corresponding to the current point cloud, but the azimuth component of the initial prediction value of the current point cloud is directly set to a third value, for example, the third value is 0.
  • the process of determining the azimuth sampling information and radial distance information corresponding to the current point cloud can be executed, so that the predicted value of the current point cloud can be further initialized according to the azimuth sampling information and radial distance information corresponding to the current point cloud to determine the corresponding initial prediction value.
  • the azimuth component of the initial predicted value is determined according to the azimuth sampling interval (azimuth sampling information) corresponding to the current point cloud, for example, the azimuth component of the initial predicted value is determined as
  • the initialization mode identification information indicates that the current point cloud uses the first initialization mode
  • Step 207 When the initialization mode identification information indicates that the current point cloud uses the second initialization mode, the azimuth component of the initial prediction value is set to a third value.
  • a syntax element is set in the header information, such as GPS and/or GBH, such as
  • this syntax element can represent the cylindrical coordinates of the current point cloud (r, i) Azimuth The predicted initial value of
  • the azimuth sampling information (such as the azimuth sampling threshold) transmitted in the code stream can be used to determine the azimuth component of the initial prediction value, instead of directly initializing the azimuth component of the initial prediction value to 0.
  • This prediction value initialization method is more reasonable and can effectively improve the prediction effect of geometric information.
  • the azimuth sampling information and radial distance information corresponding to the current point cloud can be determined first, and then the predicted value of the current point cloud can be initialized in combination with the azimuth sampling information and the radial distance information.
  • the azimuth component of the initial predicted value can be determined according to the azimuth sampling information, and at the same time, the radial distance component of the initial predicted value can be determined according to the radial distance information.
  • This embodiment provides an encoding method, at the encoding end, determining the azimuth sampling information corresponding to the current point cloud, and writing the azimuth sampling information into the bitstream; determining the initial prediction value corresponding to the current point cloud according to the azimuth sampling information corresponding to the current point cloud, and determining the prediction value of the midpoint of the current point cloud according to the initial prediction value. It can be seen that in the embodiment of the present application, in the process of initializing the prediction value of the current point cloud, it is possible to choose to determine the corresponding initial prediction value through the azimuth sampling information corresponding to the current point cloud, which can fully consider the actual size of the azimuth of the root node of the current point cloud, and achieve more reasonable initialization processing of the prediction value. In other words, in the embodiment of the present application, initializing the prediction value in combination with the azimuth sampling information can improve the accuracy of the prediction effect, thereby improving the point cloud compression performance.
  • the first determining unit 111 is configured to determine the azimuth sampling information corresponding to the current point cloud
  • the encoding unit 112 is configured to write the azimuth sampling information into a bit stream
  • the first determination unit 111 is further configured to determine an initial prediction value corresponding to the current point cloud according to the azimuth sampling information corresponding to the current point cloud, and determine a prediction value of a midpoint of the current point cloud according to the initial prediction value.
  • the encoder 100 can also be regarded as a data processing mode (or “entropy encoder”), which is used to encode the values of the grammatical elements to be encoded.
  • entropy encoder a data processing mode
  • the integrated unit is implemented in the form of a software function module and is not sold or used as an independent product, it can be stored in a computer-readable storage medium.
  • the technical solution of this embodiment is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product.
  • the computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) or a processor to perform all or part of the steps of the method described in this embodiment.
  • the aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.
  • an embodiment of the present application provides a computer-readable storage medium, which is applied to the encoder 100.
  • the computer-readable storage medium stores a computer program.
  • the computer program When the computer program is executed by the first processor, it implements the encoding method described in any one of the aforementioned embodiments.
  • Figure 14 is a second schematic diagram of the composition structure of the encoder.
  • the encoder 100 may include: a first memory 121 and a first processor 122, a first communication interface 123 and a first bus system 124.
  • the first memory 121, the first processor 122, and the first communication interface 123 are coupled together through the first bus system 124.
  • the first bus system 124 is used to achieve connection and communication between these components.
  • the first bus system 124 also includes a power bus, a control bus, and a status signal bus.
  • various buses are labeled as the first bus system 124 in Figure 10. Among them,
  • the first communication interface 123 is used for receiving and sending signals during the process of sending and receiving information with other external network elements;
  • the first processor 122 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the first processor 122.
  • the above-mentioned first processor 122 may be 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 gates or transistor logic devices, discrete hardware components.
  • DSP Digital Signal Processor
  • ASIC Application Specific Integrated Circuit
  • FPGA Field Programmable Gate Array
  • the various methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed.
  • the general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.
  • the steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor to execute, or the hardware and software modules in the decoding processor can be executed.
  • the software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc.
  • the storage medium is located in the first memory 121, and the first processor 122 reads the information in the first memory 121 and completes the steps of the above method in combination with its hardware.
  • the processing unit can be implemented in one or more application specific integrated circuits (Application Specific Integrated Circuits, ASIC), digital signal processors (Digital Signal Processing, DSP), digital signal processing devices (DSP Device, DSPD), programmable logic devices (Programmable Logic Device, PLD), field programmable gate arrays (Field-Programmable Gate Array, FPGA), general processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described in this application or a combination thereof.
  • ASIC Application Specific Integrated Circuits
  • DSP Digital Signal Processing
  • DSP Device digital signal processing devices
  • PLD programmable logic devices
  • FPGA field programmable gate array
  • general processors controllers, microcontrollers, microprocessors, other electronic units for performing the functions described in this application or a combination thereof.
  • the technology described in this application can be implemented by a module (such as a process, function, etc.) that performs the functions described in this application.
  • the software code can be stored in a memory and executed by a processor.
  • the memory can be implemented in the processor or outside the processor.
  • the present embodiment provides an encoder that determines the azimuth sampling information corresponding to the current point cloud and writes the azimuth sampling information into the bitstream; determines the initial prediction value corresponding to the current point cloud based on the azimuth sampling information corresponding to the current point cloud, and determines the prediction value of the midpoint of the current point cloud based on the initial prediction value. It can be seen that in the embodiment of the present application, in the process of initializing the prediction value of the current point cloud, it is possible to choose to determine the corresponding initial prediction value through the azimuth sampling information corresponding to the current point cloud, which can fully consider the actual size of the azimuth of the root node of the current point cloud, and achieve more reasonable initialization processing of the prediction value. In other words, in the embodiment of the present application, the initialization of the prediction value in combination with the azimuth sampling information can improve the accuracy of the prediction effect, thereby improving the point cloud compression performance.
  • FIG. 15 is a schematic diagram of a structure of a decoder.
  • the decoder 200 may include: a decoding unit 211 and a second determining unit 212; wherein,
  • the decoding unit 211 is configured to decode the code stream
  • the second determination unit 212 is configured to determine the azimuth sampling information corresponding to the current point cloud; determine the initial prediction value corresponding to the current point cloud according to the azimuth sampling information corresponding to the current point cloud, and determine the prediction value of the midpoint of the current point cloud according to the initial prediction value.
  • the decoder 200 can also be regarded as a data processing mode (or “entropy decoder”), which is used to decode the values of the syntax elements to be decoded.
  • entropy decoder a data processing mode
  • a "unit" can be a part of a circuit, a part of a processor, a part of a program or software, etc., and of course it can also be a module, or it can be non-modular.
  • the components in this embodiment can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.
  • the above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional module.
  • the integrated unit is implemented in the form of a software function module and is not sold or used as an independent product, it can be stored in a computer-readable storage medium.
  • this embodiment provides a computer-readable storage medium, which is applied to the decoder 200, and the computer-readable storage medium stores a computer program. When the computer program is executed by the second processor, the method described in any one of the above embodiments is implemented.
  • Figure 16 is a second schematic diagram of the composition structure of the decoder.
  • the decoder 200 may include: a second memory 221 and a second processor 222, a second communication interface 223 and a second bus system 224.
  • the second memory 221 and the second processor 222, and the second communication interface 223 are coupled together through the second bus system 224.
  • the second bus system 224 is used to realize the connection and communication between these components.
  • the second bus system 224 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, various buses are marked as the second bus system 224 in Figure 12. Among them,
  • the second communication interface 223 is used for receiving and sending signals during the process of sending and receiving information with other external network elements;
  • the second memory 221 is used to store a computer program that can be run on the second processor
  • the second processor 222 is used to decode the code stream and determine the azimuth sampling information corresponding to the current point cloud when running the computer program; determine the initial prediction value corresponding to the current point cloud based on the azimuth sampling information corresponding to the current point cloud, and determine the prediction value of the midpoint of the current point cloud based on the initial prediction value.
  • the second memory 221 in the embodiment of the present application can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories.
  • the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory.
  • the volatile memory can be a random access memory (RAM), which is used as an external cache.
  • RAM static RAM
  • DRAM dynamic RAM
  • SDRAM synchronous DRAM
  • DDRSDRAM double data rate synchronous DRAM
  • ESDRAM enhanced synchronous DRAM
  • SLDRAM synchronous link DRAM
  • DRRAM direct RAM bus RAM
  • the second processor 222 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the second processor 222.
  • the above-mentioned second processor 222 can be a general 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 gates or transistor logic devices, discrete hardware components.
  • DSP Digital Signal Processor
  • ASIC Application Specific Integrated Circuit
  • FPGA Field Programmable Gate Array
  • the methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed.
  • the general processor can be a microprocessor or the processor can also be any conventional processor, etc.
  • the steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor to execute, or the hardware and software modules in the decoding processor can be executed.
  • the software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc.
  • the storage medium is located in the second memory 221, and the second processor 222 reads the information in the second memory 221 and completes the steps of the above method in combination with its hardware.
  • the processing unit can be implemented in one or more application specific integrated circuits (Application Specific Integrated Circuits, ASIC), digital signal processors (Digital Signal Processing, DSP), digital signal processing devices (DSP Device, DSPD), programmable logic devices (Programmable Logic Device, PLD), field programmable gate arrays (Field-Programmable Gate Array, FPGA), general processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described in this application or a combination thereof.
  • ASIC Application Specific Integrated Circuits
  • DSP Digital Signal Processing
  • DSP Device digital signal processing devices
  • PLD programmable logic devices
  • FPGA field programmable gate array
  • general processors controllers, microcontrollers, microprocessors, other electronic units for performing the functions described in this application or a combination thereof.
  • the technology described in this application can be implemented by a module (such as a process, function, etc.) that performs the functions described in this application.
  • the software code can be stored in a memory and executed by a processor.
  • the memory can be implemented in the processor or outside the processor.
  • the second processor 222 is further configured to execute any one of the methods described in the foregoing embodiments when running the computer program.
  • the present embodiment provides a decoder that decodes a code stream to determine the azimuth sampling information corresponding to the current point cloud; determines the initial prediction value corresponding to the current point cloud based on the azimuth sampling information corresponding to the current point cloud, and determines the prediction value of the midpoint of the current point cloud based on the initial prediction value.
  • the azimuth sampling information corresponding to the current point cloud in the process of initializing the prediction value of the current point cloud, can be used to determine the corresponding initial prediction value, which can fully consider the actual size of the azimuth of the root node of the current point cloud, and achieve more reasonable initialization processing of the prediction value.
  • the initialization of the prediction value in combination with the azimuth sampling information can improve the accuracy of the prediction effect, thereby improving the point cloud compression performance.
  • an embodiment of the present application also proposes a code stream, wherein the code stream is generated by bit encoding based on the information to be encoded; wherein the information to be encoded includes at least: initialization mode identification information, azimuth sampling information, radial distance information, quantization parameters, quantized prediction residual, and mode identification information.
  • the embodiment of the present application provides a coding and decoding method, a code stream, an encoder, a decoder and a storage medium.
  • the code stream is decoded to determine the azimuth sampling information corresponding to the current point cloud; the initial prediction value corresponding to the current point cloud is determined according to the azimuth sampling information corresponding to the current point cloud, and the prediction value of the midpoint of the current point cloud is determined according to the initial prediction value.
  • the azimuth sampling information corresponding to the current point cloud is determined, and the azimuth sampling information is written into the code stream; the initial prediction value corresponding to the current point cloud is determined according to the azimuth sampling information corresponding to the current point cloud, and the prediction value of the midpoint of the current point cloud is determined according to the initial prediction value.
  • the corresponding initial prediction value can be determined by the azimuth sampling information corresponding to the current point cloud, which can fully consider the actual size of the azimuth of the root node of the current point cloud, and realize more reasonable initialization processing of the prediction value. That is to say, in the embodiment of the present application, the initialization of the prediction value in combination with the azimuth sampling information can improve the accuracy of the prediction effect, thereby improving the point cloud compression performance.

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  • Engineering & Computer Science (AREA)
  • Multimedia (AREA)
  • Signal Processing (AREA)
  • Compression, Expansion, Code Conversion, And Decoders (AREA)
  • Compression Or Coding Systems Of Tv Signals (AREA)

Abstract

Des modes de réalisation de la présente demande concernent des procédés de codage et de décodage, un flux de code, un codeur, un décodeur et un support de stockage. Au niveau d'une extrémité de décodage, le flux de code est décodé, et des informations d'échantillonnage d'azimut correspondant à un nuage de points actuel sont déterminées ; selon les informations d'échantillonnage d'azimut correspondant au nuage de points actuel, une valeur de prédiction initiale correspondant au nuage de points actuel est déterminée, et une valeur de prédiction d'un point dans le nuage de points actuel est déterminée en fonction de la valeur de prédiction initiale. Au niveau d'une extrémité de codage, des informations d'échantillonnage d'azimut correspondant au nuage de points actuel sont déterminées, et les informations d'échantillonnage d'azimut sont écrites dans le flux de code ; selon les informations d'échantillonnage d'azimut correspondant au nuage de points actuel, la valeur de prédiction initiale correspondant au nuage de points actuel est déterminée, et la valeur de prédiction du point dans le nuage de points actuel est déterminée en fonction de la valeur de prédiction initiale.
PCT/CN2023/106176 2023-07-06 2023-07-06 Procédés de codage et de décodage, flux de code, codeur, décodeur et support de stockage Pending WO2025007353A1 (fr)

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CN202380097073.4A CN121100528A (zh) 2023-07-06 2023-07-06 编解码方法、码流、编码器、解码器以及存储介质
PCT/CN2023/106176 WO2025007353A1 (fr) 2023-07-06 2023-07-06 Procédés de codage et de décodage, flux de code, codeur, décodeur et support de stockage

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