Point cloud coding processing method, point cloud decoding processing method, and related device

Abstract

The present application relates to the technical field of computers, and discloses a point cloud coding processing method, a point cloud decoding processing method, and a related device. The point cloud coding processing method of embodiments of the present application comprises: a coding end performs transform processing on the basis of a transform tree structure corresponding to a point cloud to be coded, to obtain attribute transform coefficients of a node to be coded of the transform tree structure, wherein the attribute transform coefficients of said node comprise a first attribute transform coefficient corresponding to a first channel and a second attribute transform coefficient corresponding to a second channel, and the first channel and the second channel are different channels of a same color space; the coding end performs predictive coding processing on the second attribute transform coefficient on the basis of the first attribute transform coefficient, to obtain a residual attribute transform coefficient corresponding to the second attribute transform coefficient; and the coding end performs coding processing on the residual attribute transform coefficient to obtain a first coding result, wherein a code stream of said point cloud comprises the first coding result.

Description

Point cloud encoding processing method, point cloud decoding processing method and related equipment

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to Chinese patent application No. 202311273886.2 filed in China on September 28, 2023, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The present application belongs to the field of computer technology, and specifically relates to a point cloud encoding processing method, a point cloud decoding processing method and related equipment. Background Art

[0004] A point cloud is a representation of a three-dimensional object or scene. It is composed of a set of discrete points that are randomly distributed in space and express the spatial structure and surface properties of the three-dimensional object or scene. In order to accurately reflect the information in space, the number of discrete points required is quite large. In order to reduce the bandwidth occupied by point cloud data storage and transmission, the point cloud data needs to be encoded and compressed. Point cloud data is usually composed of geometric information describing the location, such as three-dimensional coordinates (x, y, z), and attribute information of the location, such as color (R, G, B) or reflectivity. During the point cloud coding and compression process, the encoding of geometric information and attribute information is performed separately.

[0005] In related technologies, in the process of performing Region Adaptive Hierarchal Transform (RAHT) attribute encoding on attribute information, the attribute transformation coefficients of the node to be encoded are obtained. The attribute transformation coefficients of the node to be encoded include the attribute transformation coefficients of multiple color space channels. The attribute transformation coefficients of each color space channel are directly encoded, resulting in low encoding efficiency.

[0006] Summary of the Invention

[0007] The embodiments of the present application provide a point cloud encoding processing method, a point cloud decoding processing method and related equipment, which can solve the problem of low encoding efficiency.

[0008] In a first aspect, a point cloud coding processing method is provided, comprising:

[0009] The encoder performs a transform process based on a transform tree structure corresponding to the to-be-encoded point cloud to obtain attribute transform coefficients of the to-be-encoded node in the transform tree structure, where the attribute transform coefficients of the to-be-encoded node include a first attribute transform coefficient corresponding to a first channel and a second attribute transform coefficient corresponding to a second channel, where the first channel and the second channel are different channels in the same color space;

[0010] The encoder performs predictive coding on the second attribute transform coefficient based on the first attribute transform coefficient to obtain a residual attribute transform coefficient corresponding to the second attribute transform coefficient;

[0011] The encoding end performs encoding processing on the residual attribute transformation coefficient to obtain a first encoding result;

[0012] The code stream of the point cloud to be encoded includes the first encoding result.

[0013] In a second aspect, a point cloud decoding processing method is provided, comprising:

[0014] The decoding end decodes the first encoding result in the bitstream to obtain a reconstructed value of a residual attribute transform coefficient, where the residual attribute transform coefficient is a residual attribute transform coefficient corresponding to the second attribute transform coefficient corresponding to the second channel;

[0015] The decoding end performs predictive decoding processing on the reconstructed value of the residual attribute transform coefficient based on the reconstructed value of the first attribute transform coefficient corresponding to the first channel to obtain the reconstructed value of the second attribute transform coefficient, where the first channel and the second channel are different channels of the same color space;

[0016] The decoding end obtains the attribute transformation coefficient of the node to be decoded based on the reconstructed value of the second attribute transformation coefficient.

[0017] In a third aspect, a point cloud coding processing device is provided, comprising:

[0018] a transform module, configured to perform transform processing based on a transform tree structure corresponding to the to-be-encoded point cloud, to obtain attribute transform coefficients of the to-be-encoded nodes in the transform tree structure, wherein the attribute transform coefficients of the to-be-encoded nodes include a first attribute transform coefficient corresponding to a first channel and a second attribute transform coefficient corresponding to a second channel, wherein the first channel and the second channel are different channels in the same color space;

[0019] a first encoding module, configured to perform predictive encoding processing on the second attribute transform coefficient based on the first attribute transform coefficient, to obtain a residual attribute transform coefficient corresponding to the second attribute transform coefficient;

[0020] A second encoding module, configured to perform encoding processing on the residual attribute transform coefficient to obtain a first encoding result;

[0021] The code stream of the point cloud to be encoded includes the first encoding result.

[0022] In a fourth aspect, a point cloud decoding and processing device is provided, comprising:

[0023] A first decoding module is configured to decode a first encoding result in a bitstream to obtain a reconstructed value of a residual attribute transform coefficient, where the residual attribute transform coefficient is a residual attribute transform coefficient corresponding to a second attribute transform coefficient corresponding to the second channel;

[0024] a second decoding module, configured to perform predictive decoding processing on the reconstructed value of the residual attribute transform coefficient based on the reconstructed value of the first attribute transform coefficient corresponding to the first channel, to obtain the reconstructed value of the second attribute transform coefficient, wherein the first channel and the second channel are different channels of the same color space;

[0025] The acquisition module is configured to acquire the attribute transformation coefficient of the node to be decoded based on the reconstructed value of the second attribute transformation coefficient.

[0026] In a fifth aspect, an electronic device is provided, which includes a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the program or instructions are executed by the processor, the steps of the method described in the first aspect are implemented, or the steps of the method described in the second aspect are implemented.

[0027] In a sixth aspect, an electronic device is provided, comprising a processor and a communication interface, wherein the processor is used to: perform transformation processing based on a transformation tree structure corresponding to a point cloud to be encoded, to obtain attribute transformation coefficients of the nodes to be encoded in the transformation tree structure, wherein the attribute transformation coefficients of the nodes to be encoded include a first attribute transformation coefficient corresponding to a first channel and a second attribute transformation coefficient corresponding to a second channel, and the first channel and the second channel are different channels of the same color space; based on the first attribute transformation coefficient, perform predictive coding processing on the second attribute transformation coefficient to obtain a residual attribute transformation coefficient corresponding to the second attribute transformation coefficient; perform encoding processing on the residual attribute transformation coefficient to obtain a first encoding result; wherein the code stream of the point cloud to be encoded includes the first encoding result. Or, the processor is used to: decode the first encoding result in the code stream to obtain a reconstructed value of the residual attribute transform coefficient, where the residual attribute transform coefficient is the residual attribute transform coefficient corresponding to the second attribute transform coefficient corresponding to the second channel; based on the reconstructed value of the first attribute transform coefficient corresponding to the first channel, perform predictive decoding on the reconstructed value of the residual attribute transform coefficient to obtain a reconstructed value of the second attribute transform coefficient, where the first channel and the second channel are different channels in the same color space; based on the reconstructed value of the second attribute transform coefficient, obtain the attribute transform coefficient of the node to be decoded.

[0028] In a seventh aspect, an electronic device is provided, comprising: a memory configured to store video data, and a processing circuit configured to implement the steps of the method described in the first aspect, or to implement the steps of the method described in the second aspect.

[0029] In an eighth aspect, a readable storage medium is provided, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented, or the steps of the method described in the second aspect are implemented.

[0030] In the ninth aspect, a coding and decoding system is provided, comprising: a coding end device and a decoding end device, wherein the coding end device can be used to execute the steps of the method described in the first aspect, and the decoding end device can be used to execute the steps of the method described in the second aspect.

[0031] In the tenth aspect, a chip is provided, which includes a processor and a communication interface, the communication interface and the processor are coupled, and the processor is used to run programs or instructions to implement the method as described in the first aspect, or to implement the method as described in the second aspect.

[0032] In the eleventh aspect, a computer program / program product is provided, which is stored in a storage medium and is executed by at least one processor to implement the steps of the method described in the first aspect, or to implement the steps of the method described in the second aspect.

[0033] In an embodiment of the present application, an encoder performs transform processing based on a transform tree structure corresponding to a point cloud to be encoded, obtaining attribute transform coefficients for nodes to be encoded in the transform tree structure. The attribute transform coefficients for the nodes to be encoded include a first attribute transform coefficient corresponding to a first channel and a second attribute transform coefficient corresponding to a second channel, wherein the first channel and the second channel are different channels in the same color space. The encoder performs predictive coding on the second attribute transform coefficient based on the first attribute transform coefficient to obtain residual attribute transform coefficients corresponding to the second attribute transform coefficient. The encoder performs encoding on the residual attribute transform coefficient to obtain a first encoding result. The bitstream of the point cloud to be encoded includes the first encoding result. In this way, by performing predictive coding on the second attribute transform coefficient based on the first attribute transform coefficient corresponding to the first channel to obtain residual attribute transform coefficients corresponding to the second attribute transform coefficient, and encoding the residual attribute transform coefficient to obtain the first encoding result, compared to directly encoding the second attribute transform coefficient corresponding to the second channel, further compression encoding can be achieved through correlation between color space channels, thereby improving encoding efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] FIG1 is a framework diagram of a coding and decoding system in the related art;

[0035] FIG2a is a flowchart of encoding performed by an encoder based on an AVS-PCC encoding framework in the related art;

[0036] FIG2 b is a flowchart of encoding performed by an encoder based on an MPEG G-PCC encoding framework in the related art;

[0037] FIG3a is a decoding flowchart performed by a decoder based on an AVS-PCC decoding framework in the related art;

[0038] FIG3 b is a decoding flow chart of a decoder based on an MPEG G-PCC decoding framework in the related art;

[0039] FIG3c is a diagram of a point cloud G-PCC encoder framework in the related art;

[0040] FIG4 is a flow chart of a point cloud coding processing method provided in an embodiment of the present application;

[0041] FIG5 is a flowchart of a point cloud decoding processing method provided in an embodiment of the present application;

[0042] FIG6 is a schematic diagram of a coding and decoding test result provided in an embodiment of the present application;

[0043] FIG7 is a schematic diagram of another encoding and decoding test result provided in an embodiment of the present application;

[0044] FIG8 is a schematic structural diagram of a point cloud coding processing device provided in an embodiment of the present application;

[0045] FIG9 is a schematic structural diagram of a point cloud decoding and processing device provided in an embodiment of the present application;

[0046] FIG10 is a schematic structural diagram of an electronic device provided in an embodiment of the present application;

[0047] FIG11 is a schematic structural diagram of another electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0048] The following will be combined with the accompanying drawings in the embodiments of this application to clearly describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of this application.

[0049] The terms "first", "second", etc. in this application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way are interchangeable where appropriate, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first" and "second" are generally of the same type, and do not limit the number of objects, for example, the first object can be one or more. In addition, "or" in this application represents at least one of the connected objects. For example, "A or B" covers three options, namely, Option 1: including A but not including B; Option 2: including B but not including A; Option 3: including both A and B. The character " / " generally indicates that the objects associated before and after are in an "or" relationship.

[0050] Before introducing the technical solutions provided by the embodiments of the present application, the meanings of some of the terms therein are first introduced.

[0051] Point Cloud: A point cloud is a set of irregularly distributed discrete points in space that represent the spatial structure and surface properties of a 3D object or scene. Point clouds can be categorized into different types based on different classification criteria. For example, based on how the point cloud is acquired, they can be divided into dense point clouds and sparse point clouds. Similarly, based on the temporal nature of the point cloud, they can be divided into static point clouds and dynamic point clouds.

[0052] Point Cloud Data: The geometric coordinate information and attribute information of each point in the point cloud together constitute point cloud data. Among them, geometric coordinate information can also be called three-dimensional position information. The geometric coordinate information of a point in the point cloud refers to the spatial coordinates (x, y, z) of the point, which can include the coordinate values ​​of the point in each coordinate axis direction of the three-dimensional coordinate system, for example, the coordinate value x in the X-axis direction, the coordinate value y in the Y-axis direction, and the coordinate value z in the Z-axis direction. The attribute information of a point in the point cloud may include at least one of the following: color information, material information, laser reflection intensity information (also called reflectivity). Usually, each point in the point cloud has the same amount of attribute information. For example, each point in the point cloud can have two kinds of attribute information: color information and laser reflection intensity. For another example, each point in the point cloud can have three kinds of attribute information: color information, material information, and laser reflection intensity information.

[0053] Point Cloud Compression (PCC): Point cloud coding refers to the process of encoding the geometric coordinate information and attribute information of each point in the point cloud to obtain a compressed code stream. Point cloud coding can include two main processes: geometric coordinate information encoding and attribute information encoding. Currently, the point cloud coding framework that can compress point clouds can be the geometry-based point cloud compression (G-PCC) codec framework or the video-based point cloud compression (V-PCC) codec framework provided by the Moving Picture Experts Group (MPEG), or the AVS-PCC codec framework provided by the Audio Video Standard (AVS).

[0054] Point cloud decoding: Point cloud decoding refers to the process of decoding the compressed bitstream obtained from point cloud encoding to reconstruct the point cloud. Specifically, it refers to the process of reconstructing the geometric coordinate information and attribute information of each point in the point cloud based on the geometric bitstream and attribute bitstream in the compressed bitstream. After obtaining the compressed bitstream at the decoding end, the geometric bitstream is first entropy decoded to obtain the quantized information of each point in the point cloud, and then dequantized to reconstruct the geometric coordinate information of each point in the point cloud. For the attribute bitstream, entropy decoding is first performed to obtain the quantized attribute residual information or quantized transform coefficients of each point in the point cloud. The quantized attribute residual information is then dequantized to obtain the reconstructed residual information, and the quantized transform coefficients are dequantized to obtain the reconstructed transform coefficients. The reconstructed transform coefficients are then inversely transformed to obtain the reconstructed residual information. Based on the reconstructed residual information of each point in the point cloud, the attribute information of each point in the point cloud can be reconstructed. The reconstructed attribute information of each point in the point cloud is sequentially associated with the reconstructed geometric coordinate information to reconstruct the point cloud.

[0055] Figure 1 is a schematic diagram of a codec system 10 provided in an embodiment of the present application. The technical solution of the embodiment of the present application involves performing codec (including encoding or decoding) on ​​point cloud data.

[0056] As shown in FIG1 , a codec system 10 includes a source device 100 that provides encoded point cloud data to be decoded and displayed by a destination device 110. Specifically, the source device 100 provides the point cloud data to the destination device 110 via a communication medium 120. The source device 100 and the destination device 110 may include any one or more of a desktop computer, a notebook (i.e., laptop) computer, a tablet computer, a set-top box, a mobile phone, a wearable device (e.g., a smartwatch or a wearable camera), a television, a camera, a display device, an in-vehicle device, a virtual reality (VR) device, an augmented reality (AR) device, a mixed reality (MR) device, a digital media player, a video game console, a video conferencing device, a video streaming device, a broadcast receiver device, a broadcast transmitter device, a spacecraft, an aircraft, a robot, a satellite, and the like.

[0057] In the example of Figure 1, the source device 100 includes a data source 101, a memory 102, an encoder 200, and an output interface 104. The destination device 110 includes an input interface 111, a decoder 300, a memory 113, and a display device 114. The source device 100 represents an example of an encoding device, while the destination device 110 represents an example of a decoding device. In other examples, the source device 100 and the destination device 110 may not include some of the components in Figure 1, or may include other components other than Figure 1. For example, the source device 100 may obtain point cloud data through an external capture device. Similarly, the destination device 110 may be connected to an external display device interface without including an integrated display device. For another example, the memory 102 and the memory 113 may be external memories.

[0058] Although FIG1 illustrates source device 100 and destination device 110 as separate devices, in some examples, the two may be integrated into a single device. In such embodiments, the functions corresponding to source device 100 and the functions corresponding to destination device 110 may be implemented using the same hardware or software, or using separate hardware or software, or any combination thereof.

[0059] In some examples, source device 100 and destination device 110 can perform unidirectional data transmission or bidirectional data transmission. If bidirectional data transmission is performed, source device 100 and destination device 110 can operate in a substantially symmetrical manner, that is, each of source device 100 and destination device 110 includes an encoder and a decoder.

[0060] The data source 101 represents the source of point cloud data (i.e., raw, unencoded point cloud data) and provides the point cloud data to the encoder 200, and the encoder 103 encodes the point cloud data. The source device 100 may include a capture device (e.g., a camera device, a sensor device, or a scanning device), an archive of previously captured point cloud data, or a feed interface for receiving point cloud data from a data content provider. Among them, the camera device may include an ordinary camera, a stereo camera, and a light field camera, etc., the sensor device may include a laser device, a radar device, etc., and the scanning device may include a three-dimensional laser scanning device, etc. Point cloud data can be obtained by capturing a real-world visual scene through a capture device. Alternatively, the data source 101 may generate computer graphics-based data as source data, or combine real-time data, archived data, and computer-generated data. For example, the data source generates point cloud data based on a virtual object (e.g., a virtual three-dimensional object and a virtual three-dimensional scene obtained by three-dimensional modeling).

[0061] The encoder 200 encodes captured, pre-captured, or computer-generated data. The encoder 200 can rearrange the point cloud data from the order in which it was received (sometimes referred to as "display order") into an encoding order. The encoder 200 can generate a bitstream comprising the encoded point cloud data. The source device 100 can then output the encoded point cloud data to the communication medium 120 via the output interface 104 for receipt or retrieval by, for example, the input interface 111 of the destination device 110.

[0062] Memory 102 of source device 100 and memory 113 of destination device 110 represent general-purpose memories. In some examples, memory 102 may store raw data from data source 101, and memory 113 may store decoded point cloud data from decoder 300. Additionally or alternatively, memories 102 and 113 may store software instructions executable by, for example, encoder 200 and decoder 300, respectively. Although memory 102 and memory 113 are shown separately from encoder 200 and decoder 300 in this example, it should be understood that encoder 200 and decoder 300 may also include internal memory for functionally similar or equivalent purposes. If encoder 200 and decoder 300 are deployed on the same hardware device, memory 102 and memory 113 may be the same memory. Furthermore, memories 102 and 113 may store, for example, encoded point cloud data output from encoder 200 and input to decoder 300. In some examples, portions of memory 102 , 113 may be allocated as one or more point cloud buffers, eg, for storing raw, decoded, or encoded point cloud data.

[0063] In some examples, source device 100 can output the encoded data from output interface 104 to memory 113. Similarly, destination device 110 can access the encoded data from memory 113 via input interface 111. Memory 113 or storage 102 can include any of a variety of distributed or locally accessed data storage media, such as a hard drive, a Blu-ray disc, a Digital Versatile Disc (DVD), a Compact Disc Read-Only Memory (CD-ROM), flash memory, volatile or non-volatile memory, or any other suitable digital storage medium for storing encoded point cloud data.

[0064] The output interface 104 may include any type of medium or device capable of transmitting the encoded point cloud data from the source device 100 to the destination device 110. For example, the output interface 104 may include a transmitter or transceiver, such as an antenna, configured to transmit the encoded point cloud data directly from the source device 100 to the destination device 110 in real time. The encoded point cloud data may be modulated according to a communication standard of a wireless communication protocol and transmitted to the destination device 110.

[0065] The communication medium 120 may include a transient medium such as a wireless broadcast or a wired network transmission. For example, the communication medium 120 may include a radio frequency (RF) spectrum or one or more physical transmission lines (e.g., cables). The communication medium 120 may form part of a packet-based network such as a local area network, a wide area network, or a global network such as the Internet. The communication medium 120 may also take the form of a storage medium (e.g., a non-transitory storage medium) such as a hard disk, a flash drive, a compact disk, a digital point cloud disk, a Blu-ray disc, a volatile or non-volatile memory, or any other suitable digital storage medium for storing the encoded point cloud data.

[0066] In some embodiments, the communication medium 120 may include a router, a switch, a base station, or any other device that can be used to facilitate communication from the source device 100 to the destination device 110. For example, a server (not shown) can receive the encoded point cloud data from the source device 100 and provide it to the destination device 110, for example, by transmitting it to the destination device 110 via a network. The server may include, for example, a web server (for a website), a server configured to provide a file transfer protocol service (such as the File Transfer Protocol (FTP) or the File Delivery Over Unidirectional Transport (FLUTE) protocol), a content delivery network (CDN) device, a Hypertext Transfer Protocol (HTTP) server, a Multimedia Broadcast Multicast Services (MBMS) or an evolved Multimedia Broadcast Multicast Service (eMBMS) server, or a Network-attached storage (NAS) device, etc. The server can implement one or more HTTP streaming protocols, such as MPEG Media Transport (MMT) protocol, Dynamic Adaptive Streaming over HTTP (DASH) protocol, HTTP Live Streaming (HLS) protocol or Real Time Streaming Protocol (RTSP).

[0067] The destination device 110 can access the encoded point cloud data from the server, for example, via a wireless channel (e.g., a Wireless Fidelity (WIFI) connection) or a wired connection (e.g., a Digital Subscriber Line (DSL), a cable modem, etc.) for accessing the encoded point cloud data stored on the server.

[0068] Output interface 104 and input interface 111 may represent wireless transmitters / receivers, modems, wired networking components (e.g., Ethernet cards), wireless communication components operating according to the IEEE 802.11 standard or the IEEE 802.15 standard (e.g., ZigBee™), the Bluetooth standard, or other physical components. In examples where output interface 104 and input interface 111 include wireless components, output interface 104 and input interface 111 may be configured to operate according to WIFI, Ethernet, a cellular network (such as 4G (4G)), or a wireless network. th Generation, 4G) communication system, Long Term Evolution (LTE), Advanced LTE, 5G th Generation, 5G) communication system, 6th generation (6 th Generation, 6G) communication systems, etc.) to transmit data, such as encoded point cloud data.

[0069] The technology provided in the embodiments of the present application can be applied to support one or more application scenarios such as: machine perception of point cloud, which can be used in scenarios such as autonomous navigation systems, real-time inspection systems, geographic information systems, visual sorting robots, emergency rescue robots, etc.; human eye perception of point cloud, which can be used in point cloud application scenarios such as digital cultural heritage, free viewpoint broadcasting, three-dimensional immersive communication, and three-dimensional immersive interaction.

[0070] The input interface 111 of the destination device 110 receives an encoded bitstream from the communication medium 120. The encoded bitstream may include high-level syntax elements and encoded data units (such as sequences, groups of pictures, pictures, slices, blocks, etc.), wherein the high-level syntax elements are used to decode the encoded data units to obtain decoded point cloud data. The display device 114 displays the decoded point cloud data to the user. The display device 114 may include a cathode ray tube (CRT), a liquid crystal display (LCD), a plasma display, an organic light-emitting diode (OLED) display, or other types of display devices. In some examples, the destination device 110 may not have a display device 114. For example, if the decoded point cloud data is used to determine the position of a physical object, the display device 114 may be replaced by a processor.

[0071] The encoder 200 and the decoder 300 may be implemented as one or more of a variety of processing circuits, which may include a microprocessor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), discrete logic, hardware, or any combination thereof. When the technology is implemented in whole or in part in software, the device may store instructions for the software in an appropriate non-transitory computer-readable storage medium and use one or more processors to execute the instructions in hardware to perform the technology provided in the embodiments of the present application.

[0072] The following describes the basic principles of the encoder 200 and decoder 300 provided in the embodiments of the present application, taking the G-PCC and AVS-PCC codec frameworks as examples.

[0073] The encoding and decoding frameworks of G-PCC and AVS-PCC are roughly the same. Figure 2a shows a coding flow chart executed by an encoder based on the AVS-PCC coding framework, and Figure 2b shows a coding flow chart executed by an encoder based on the MPEG G-PCC coding framework. The above encoder may be the encoder 200 shown in Figure 1. The above coding frameworks can be roughly divided into a geometric coordinate information encoding process and an attribute information encoding process. In the geometric information encoding process, the geometric coordinate information of each point in the point cloud is encoded to obtain a geometric bit stream; in the attribute information encoding process, the attribute information of each point in the point cloud is encoded to obtain an attribute bit stream; the geometric bit stream and the attribute bit stream together constitute the compressed code stream of the point cloud.

[0074] For the geometric information encoding process, the encoding process performed by the encoder 200 is as follows:

[0075] 1. Pre-processing: This may include coordinate transformation and voxelization. Pre-processing converts point cloud data in three-dimensional space into integer form through scaling and translation operations, and moves its minimum geometric position to the coordinate origin. In some examples, encoder 200 may not perform pre-processing.

[0076] 2. Geometric coding: For the AVS-PCC coding framework, geometric coding includes two modes, namely, octree-based geometric coding and prediction tree-based geometric coding. For the G-PCC coding framework, geometric coding includes three modes, namely, octree-based geometric coding, trisoup-based geometric coding, and prediction tree-based prediction coding. Among them:

[0077] Octree-based geometric coding: An octree is a tree-like data structure that evenly divides a predefined bounding box in three-dimensional space, with each node having eight children. By indicating whether each child node in the octree is occupied with "1" or "0," an occupancy code is generated as the code stream of the point cloud geometry information.

[0078] Geometric coding based on prediction tree: A prediction strategy is used to generate a prediction tree, and each node is traversed from the root node of the prediction tree, and the residual coordinate value corresponding to each traversed node is encoded.

[0079] Triangle-based geometric encoding: Divide the point cloud into blocks of a certain size and locate the intersection points (called vertices) of the point cloud surface at the edges of the blocks. The geometric information is compressed by encoding whether each edge on the block has an intersection and the location of the intersection point.

[0080] 3. Geometry Entropy Encoding: This performs statistical compression encoding on the octtree's occupancy code information, the prediction tree's prediction residual information, and the vertex information of the triangle representation, ultimately outputting a binary (0 or 1) compressed bitstream. Statistical coding is a lossless encoding method that can effectively reduce the bitrate required to express the same signal. A commonly used statistical coding method is context-adaptive binary arithmetic coding (CABAC).

[0081] 4. Geometric reconstruction: Decode and reconstruct the geometric information after geometric encoding.

[0082] For the attribute information encoding process, the encoding process performed by the encoder 200 is as follows:

[0083] 1. Color transformation: Apply a transformation to transform the color information of an attribute to a different domain. For example, the color information can be transformed from the RGB color space to the YCbCr color space.

[0084] 2. Attribute Recoloring: In lossy encoding, after encoding the geometric coordinates, the encoder needs to decode and reconstruct the geometry, restoring the geometry of each point in the point cloud. The attribute information of one or more neighboring points in the original point cloud is searched for and used as the attribute information for the reconstructed point.

[0085] In some examples, encoder 200 may not perform color conversion or attribute recoloring.

[0086] 3. Attribute information processing: In AVS-PCC, attribute information processing can include three modes, namely prediction coding, transform coding, and prediction and transform coding. These three coding modes can be used under different conditions.

[0087] Predictive coding involves determining neighboring points of the point to be coded from among the coded points based on information such as distance or spatial relationships, and then calculating predicted attribute information for the point to be coded based on the attribute information of the predicted points, based on a set criterion. The difference between the actual attribute information of the point to be coded and the predicted attribute information is calculated as the attribute residual information, which is then quantized, transformed (optionally), and entropy coded.

[0088] Transform coding refers to the use of transformation methods such as Discrete Cosine Transform (DCT) and Haar Transform (Haar) to group and transform attribute information and quantize the transform coefficients; through inverse quantization and inverse transformation, attribute reconstruction information is obtained; the difference between the real attribute information and the attribute reconstruction information is calculated to obtain attribute residual information and quantize it; and the quantized transform coefficients and attribute residuals are entropy coded.

[0089] Predictive transform coding refers to using the attribute residual information obtained by prediction to perform transformation, quantize the transform coefficients, and perform entropy coding.

[0090] In MPEG G-PCC, attribute information processing can include three modes: prediction transform coding, lifting transform coding, and region adaptive hierarchical transform (RAHT) coding. These three coding modes can be used under different conditions.

[0091] Predictive transform coding involves dividing the point cloud into multiple levels of detail (LoDs) by selecting subsets of points based on distance, achieving a multi-level point cloud representation from coarse to fine quality. Adjacent layers can be predicted from the bottom up, whereby neighboring points in the coarse layer predict the attributes of points introduced in the fine layer, obtaining the corresponding attribute residual information. The points in the lowest layer are encoded as reference information.

[0092] Lifting transform coding refers to introducing a weight update strategy for neighborhood points based on the prediction of adjacent layers of LoD, ultimately obtaining the predicted attribute information of each point and the corresponding attribute residual information.

[0093] Hierarchical region adaptive transform coding means that the attribute information is transformed into a transform domain through RAHT transformation, which is called transform coefficient.

[0094] 4. Attribute Quantization: The degree of quantization is usually determined by the quantization parameter. The transform coefficients or attribute residual information obtained by attribute information processing are quantized and entropy coded. For example, in predictive transform coding and lifting transform coding, entropy coding is performed on the quantized attribute residual information; in RAHT, entropy coding is performed on the quantized transform coefficients.

[0095] 5. Entropy Coding: Run-length coding and arithmetic coding are typically used to achieve final compression of quantized attribute residual information and / or transform coefficients. The corresponding coding mode, quantization parameters, and other information are also encoded using an entropy encoder.

[0096] The encoder 200 encodes the geometric coordinate information of each point in the point cloud to obtain a geometry bitstream, and encodes the attribute information of each point in the point cloud to obtain an attribute bitstream. The encoder 200 can transmit the encoded geometry bitstream and attribute bitstream together to the decoder 300.

[0097] Figure 3a shows a decoding flowchart performed by a decoder based on the AVS-PCC decoding framework, and Figure 3b shows a decoding flowchart performed by a decoder based on the MPEG G-PCC decoding framework. The above decoder may be decoder 300 shown in Figure 1. After receiving the compressed code stream (i.e., the attribute bitstream and the geometry bitstream) transmitted by encoder 200, decoder 300 decodes the geometry bitstream to reconstruct the geometric coordinate information of each point in the point cloud, and decodes the attribute bitstream to reconstruct the attribute information of each point in the point cloud.

[0098] The decoding process performed by the decoder 300 is as follows:

[0099] 1. Entropy Decoding: Entropy decoding is performed on the geometry bitstream and attribute bitstream respectively to obtain geometry syntax elements and attribute syntax elements.

[0100] 2. Geometric decoding: For the AVS-PCC coding framework, geometric decoding includes two modes: octree-based geometric decoding and prediction tree-based geometric decoding. For the G-PCC coding framework, geometric coding includes three modes: octree-based geometric decoding, trisoup-based geometric decoding, and prediction tree-based prediction decoding.

[0101] Octree-based geometry decoding: Reconstructs the octree based on the geometry syntax elements parsed from the geometry bitstream.

[0102] Prediction tree-based geometry decoding: The prediction tree is reconstructed based on the geometry syntax elements parsed from the geometry bitstream.

[0103] Geometry decoding based on triangle representation: reconstructs the triangle model based on the geometry syntax elements parsed from the geometry bitstream.

[0104] 3. Geometric reconstruction: Perform reconstruction to obtain the geometric coordinate information of the points in the point cloud.

[0105] 4. Coordinate inverse transformation: Perform inverse transformation on the reconstructed geometric coordinate information to convert the reconstructed coordinates (positions) of the points in the point cloud from the transformed domain back to the initial domain.

[0106] 5. Dequantization: Dequantize the attribute syntax elements.

[0107] 6. Attribute information processing: In AVS-PCC, attribute information processing determines the color information of the midpoint in the point cloud by predicting or transforming the inverse quantized prediction residual or prediction residual transformation coefficient, or by transforming the inverse quantized transformation coefficient to determine the color information of the midpoint in the point cloud.

[0108] In MPEG G-PCC, attribute information processing determines the color information of the point in the point cloud by RAHT on the inverse quantized attribute information, or determines the color information of the point in the point cloud by LOD and inverse lifting on the inverse quantized attribute information.

[0109] 7. Color inversion: Convert the color information from the YCbCr color space to the RGB color space. In some examples, the color inversion operation may not be performed.

[0110] About G-PCC attribute encoding:

[0111] In the point cloud G-PCC encoder framework, the geometric information and attribute information of the point cloud are encoded separately. As shown in Figure 3c, the attribute encoding of G-PCC can be divided into a region-adaptive transformation based on upsampling prediction and a lifting transformation based on hierarchical structure division.

[0112] The region adaptive transform based on upsampling prediction includes: first, constructing a transform tree structure. Starting from the bottom layer, an octree structure is constructed from the bottom up. In the process of constructing the transform tree, it is necessary to generate corresponding Morton code information, attribute information and weight information for the merged nodes. Then, upsampling prediction and region adaptive transform (RAHT) are performed layer by layer starting from the root node from top to bottom. If the current node is a root node, no upsampling prediction is performed, and the attribute information of the node is directly RAHT transformed. Then, the DC coefficient and AC coefficient obtained by the transformation are quantized and entropy coded. If it is not a root node, whether to predict the current node is determined based on the number of grandparent nodes and parent nodes. If prediction is required, for the current node to be coded, a weighted prediction is performed on its child nodes, including the parent node of the current child node, the neighboring parent node coplanar and collinear with the current child node, and the neighboring child nodes coplanar and collinear with the current child node. The predicted attribute value of the current child node is then subjected to a RAHT transform on the predicted attribute value and the original attribute value of the current node to be coded. The resulting AC coefficient residual is then quantized and entropy coded. If prediction is not required, a RAHT transform is directly performed on the original attribute value of the current node to be coded, and the resulting AC coefficient is quantized and entropy coded to obtain the attribute bitstream.

[0113] The lifting transformation based on hierarchical structure partitioning involves: first, dividing the point cloud to be encoded into layers using detail level partitioning to establish a hierarchical structure of the point cloud. During this process, the bottom-level points are encoded and decoded first. Therefore, the bottom-level points and reconstructed points at the same level can be used to predict the points at the higher levels, thus achieving progressive encoding and decoding. Then, using the bottom-level points and points at the same level as reference points, the point to be encoded is searched within the reference points. The K nearest reference points are selected as prediction reference points. Linear interpolation prediction is performed using the reconstructed attribute values ​​of these K nearest neighbors, with the weight being the inverse of the Euclidean distance between the nearest neighbor and the point to be encoded. Finally, the lifting transformation is performed, which consists of three steps: segmentation, prediction, and update. The segmentation step spatially segments the input point cloud data into a high-level point cloud and a low-level point cloud. In the prediction step, the attribute information of the low-level point cloud is used to predict the attribute information of the high-level point cloud, resulting in a prediction residual. During the segmentation and prediction process, since the prediction strategy in LoD partitioning makes the points in the lower LoD layer have higher weights, it is necessary to define and recursively update the influence weight of each point based on the prediction residual and the distance between the predicted point and its neighbors, and finally obtain the code stream of attribute information.

[0114] The attribute encoding process based on RAHT is as follows:

[0115] 1) Reorder the point cloud and construct an N-layer RAHT transform tree using a bottom-up construction method. The bottom layer contains all nodes, and the top layer is the root node layer, which contains only one node.

[0116] 2) Based on the transformation tree structure, upsampling prediction and region-adaptive hierarchical transformation are performed on each node layer by layer starting from the root node from top to bottom.

[0117] If the current node is the root node, no upsampling prediction is performed, and the node's child node attribute information is directly subjected to RAHT transformation to obtain 1 direct current (DC) coefficient and at most 7 alternating current (AC) coefficients.

[0118] If the current node is not the root node, assume that the current node consists of 2*2*2 child nodes, and determine whether it is necessary to predict the child nodes of the current node.

[0119] First, when the current node has only one occupied child node, no prediction is performed; when the number of neighbor parent nodes of the current node (that is, the number of grandparent neighbors of the child nodes of the current node) is less than a threshold of 1, no prediction is performed, and the original attribute information of the child nodes of the current node is directly subjected to RAHT transformation, and then the obtained AC coefficients are quantized and entropy encoded. For example, the threshold 1 can be equal to 2; if the threshold 1 is met, neighbors are searched for the child nodes of the current node, and the neighbor search range includes: the current node, the neighbor parent nodes that are coplanar and colinear with the child nodes of the current node, and the neighbor child nodes that are coplanar and colinear with the child nodes of the current node.

[0120] If the number of found neighboring parent nodes is less than a threshold of 2, no prediction is performed. Instead, the original attribute information of the current node's child nodes is directly subjected to a RAHT transform to obtain the AC transform coefficients. For example, threshold 2 can be equal to 6. If threshold 2 is met, a prediction is performed based on the neighboring nodes to obtain the predicted attribute value of the current child node. RAHT transforms are performed on the original attribute value and the predicted attribute value, and the resulting AC transform coefficients are subtracted to obtain the AC residual transform coefficients.

[0121] 4) Quantize and entropy encode the obtained transform coefficients. For the root node, both its DC transform coefficients and AC transform coefficients need to be quantized and entropy coded. Except for the root node, other nodes only need to quantize and entropy encode the AC transform coefficients or AC residual transform coefficients.

[0122] The point cloud encoding processing method, point cloud decoding processing method and related equipment provided in the embodiments of the present application are described in detail below with reference to some embodiments and their application scenarios in combination with the accompanying drawings.

[0123] Referring to FIG. 4 , FIG. 4 is a flowchart of a point cloud coding processing method provided in an embodiment of the present application, which can be applied to an encoding end. As shown in FIG. 4 , the point cloud coding processing method includes the following steps:

[0124] Step 11: The encoding end performs transformation processing based on the transformation tree structure corresponding to the point cloud to be encoded to obtain the attribute transformation coefficients of the nodes to be encoded in the transformation tree structure, where the attribute transformation coefficients of the nodes to be encoded include a first attribute transformation coefficient corresponding to the first channel and a second attribute transformation coefficient corresponding to the second channel, where the first channel and the second channel are different channels in the same color space.

[0125] Among them, the transformation processing based on the transformation tree structure corresponding to the point cloud to be encoded can include: obtaining the geometric information and attribute information of the point cloud to be encoded; generating a multi-layer transformation tree structure corresponding to the point cloud to be encoded based on the geometric information of the point cloud to be encoded; transforming or predicting the child nodes of each node to be encoded in each layer based on the transformation tree structure, and determining multiple attribute transformation coefficients corresponding to each node to be encoded.

[0126] In addition, each node to be coded may contain n child nodes (up to 2*2*2), and transforming the child nodes may obtain 1 DC coefficient and n-1 AC correlation coefficients, where the AC correlation coefficients include AC residual coefficients and AC coefficients.

[0127] In one embodiment, the transformation process may be RAHT processing. The attribute transformation coefficient of the node to be encoded may include the AC correlation coefficient.

[0128] Step 12: The encoding end performs predictive coding processing on the second attribute transform coefficient based on the first attribute transform coefficient to obtain a residual attribute transform coefficient corresponding to the second attribute transform coefficient.

[0129] The encoder, based on the first attribute transform coefficient, performs predictive coding on the second attribute transform coefficient to obtain a residual attribute transform coefficient corresponding to the second attribute transform coefficient. This may include: the encoder encoding the first attribute transform coefficient to obtain a second encoding result; the encoder reconstructing the first attribute transform coefficient based on the second encoding result to obtain a reconstructed value of the first attribute transform coefficient; the encoder performing predictive coding on the second attribute transform coefficient based on the reconstructed value of the first attribute transform coefficient to obtain a residual attribute transform coefficient corresponding to the second attribute transform coefficient. Alternatively, the encoder performing predictive coding on the second attribute transform coefficient based on the first attribute transform coefficient to obtain a residual attribute transform coefficient corresponding to the second attribute transform coefficient may include: the encoder determining a predicted value of the second attribute transform coefficient based on the first attribute transform coefficient and a target parameter, the target parameter being a correlation parameter between the first channel and the second channel; the encoder obtaining a residual attribute transform coefficient corresponding to the second attribute transform coefficient based on the predicted value of the second attribute transform coefficient and the second attribute transform coefficient. The predicted value of the second attribute transform coefficient may be the product of the first attribute transform coefficient and the target parameter.

[0130] Step 13: The encoding end performs encoding processing on the residual attribute transform coefficient to obtain a first encoding result;

[0131] The code stream of the point cloud to be encoded includes the first encoding result.

[0132] In addition, encoding the residual attribute transformation coefficient to obtain the first encoding result may include quantizing and entropy encoding the residual attribute transformation coefficient to obtain the first encoding result.

[0133] In addition, the encoding end may further perform encoding processing on the first attribute transformation coefficient to obtain a second encoding result, and the code stream of the point cloud to be encoded may further include the second encoding result.

[0134] It should be noted that the first channel and the second channel can be different channels in the color space YCbCr; or, the first channel and the second channel can be different channels in the color space YUV; or, the first channel and the second channel can be different channels in the color space YIQ; this embodiment does not limit the color space of the first channel and the second channel.

[0135] In one embodiment, the first channel and the second channel are both chrominance channels.

[0136] In one embodiment, the first channel and the second channel are both chrominance channels in the color space YCbCr. For example, the first channel may be the Cb channel and the second channel may be the Cr channel; or the second channel may be the Cb channel and the first channel may be the Cr channel.

[0137] In one embodiment, the first channel is a chroma channel, and the second channel is a luminance channel.

[0138] In one embodiment, the first channel is a chroma channel in the YCbCr color space, and the second channel is a luminance channel in the YCbCr color space. For example, the first channel may be a Cb channel and the second channel may be a Y channel; or the first channel may be a Cr channel and the second channel may be a Y channel.

[0139] In one embodiment, the first channel is a luminance channel, and the second channel is a chrominance channel.

[0140] In one embodiment, the first channel is a luma channel in the YCbCr color space, and the second channel is a chroma channel in the YCbCr color space. For example, the first channel may be a Y channel, and the second channel may be a Cb channel; or the first channel may be a Y channel, and the second channel may be a Cr channel.

[0141] It should be noted that, taking the color space as YCbCr, and the first channel and the second channel as chroma channels (Chroma) as an example, there may be a strong correlation between the multi-channel attributes. When the GPCC color attribute is lossy encoded, the YCbCr component format is used for encoding, wherein Y is the luminance channel (Luma), Cb and Cr are the chroma channels (Chroma), and there is a high correlation between the Cb channel and the Cr channel. In the related art, in the GPCC RAHT attribute encoding, when encoding the transform coefficients, the correlation between the chroma channels is not fully utilized, resulting in low encoding efficiency. The embodiment of the present application supports predictive encoding of the attribute transform coefficients of the Cr channel based on the attribute transform coefficients of the Cb channel, or supports predictive encoding of the attribute transform coefficients of the Cb channel based on the attribute transform coefficients of the Cr channel, and can use the correlation between the chroma channels to predictively encode the attribute transform coefficients, which can improve encoding efficiency.

[0142] In an embodiment of the present application, an encoder performs transform processing based on a transform tree structure corresponding to a point cloud to be encoded, obtaining attribute transform coefficients for nodes to be encoded in the transform tree structure. The attribute transform coefficients for the nodes to be encoded include a first attribute transform coefficient corresponding to a first channel and a second attribute transform coefficient corresponding to a second channel, wherein the first channel and the second channel are different channels in the same color space. The encoder performs predictive coding on the second attribute transform coefficient based on the first attribute transform coefficient to obtain residual attribute transform coefficients corresponding to the second attribute transform coefficient. The encoder performs encoding on the residual attribute transform coefficient to obtain a first encoding result. The bitstream of the point cloud to be encoded includes the first encoding result. In this way, by performing predictive coding on the second attribute transform coefficient based on the first attribute transform coefficient corresponding to the first channel to obtain residual attribute transform coefficients corresponding to the second attribute transform coefficient, and encoding the residual attribute transform coefficient to obtain the first encoding result, compared to directly encoding the second attribute transform coefficient corresponding to the second channel, further compression encoding can be achieved through correlation between color space channels, thereby improving encoding efficiency.

[0143] Optionally, the encoding end performs predictive coding processing on the second attribute transform coefficient based on the first attribute transform coefficient to obtain a residual attribute transform coefficient corresponding to the second attribute transform coefficient, including:

[0144] The encoding end performs encoding processing on the first attribute transformation coefficient to obtain a second encoding result;

[0145] The encoding end reconstructs the first attribute transform coefficient based on the second encoding result to obtain a reconstructed value of the first attribute transform coefficient;

[0146] The encoding end performs predictive encoding processing on the second attribute transform coefficient based on the reconstructed value of the first attribute transform coefficient to obtain a residual attribute transform coefficient corresponding to the second attribute transform coefficient.

[0147] The encoding end may perform quantization and entropy encoding on the first attribute transformation coefficient to obtain a second encoding result.

[0148] In addition, the encoding end may perform inverse quantization on the second encoding result to reconstruct and obtain a reconstructed value of the first attribute transformation coefficient.

[0149] In addition, the encoding end performs predictive encoding processing on the second attribute transform coefficient based on the reconstructed value of the first attribute transform coefficient to obtain the residual attribute transform coefficient corresponding to the second attribute transform coefficient, which may include: the encoding end determines the predicted value of the second attribute transform coefficient based on the reconstructed value of the first attribute transform coefficient and the target parameter, the target parameter is the correlation parameter between the first channel and the second channel, and the encoding end obtains the residual attribute transform coefficient corresponding to the second attribute transform coefficient based on the predicted value of the second attribute transform coefficient and the second attribute transform coefficient.

[0150] In this embodiment, the encoding end performs predictive coding on the second attribute transform coefficient based on the reconstructed value of the first attribute transform coefficient to obtain the residual attribute transform coefficient corresponding to the second attribute transform coefficient. The encoding end performs coding on the residual attribute transform coefficient to obtain a first coding result. In this way, since the decoding end obtains the reconstructed value of the first attribute transform coefficient when decoding the first attribute transform coefficient, the decoding end can decode the residual attribute transform coefficient corresponding to the second attribute transform coefficient through the reconstructed value of the first attribute transform coefficient to obtain the second attribute transform coefficient. When predictive coding or decoding is performed on the second attribute transform coefficient, the encoding end and the decoding end both use the reconstructed value of the first attribute transform coefficient, thereby achieving consistency between the predictive coding at the encoding end and the predictive decoding at the decoding end.

[0151] Optionally, the encoding end performs predictive coding on the second attribute transform coefficient based on the reconstructed value of the first attribute transform coefficient to obtain a residual attribute transform coefficient corresponding to the second attribute transform coefficient, including:

[0152] The encoder determines, based on the reconstructed value of the first attribute transform coefficient and a target parameter, a predicted value of the second attribute transform coefficient, where the target parameter is a correlation parameter between the first channel and the second channel;

[0153] The encoding end obtains a residual attribute transform coefficient corresponding to the second attribute transform coefficient based on the predicted value of the second attribute transform coefficient and the second attribute transform coefficient.

[0154] Among them, the predicted value of the second attribute transformation coefficient can be the product of the reconstructed value of the first attribute transformation coefficient and the target parameter; or, the predicted value of the second attribute transformation coefficient can be the sum of the product of the reconstructed value of the first attribute transformation coefficient and the target parameter and a first preset value; or, the predicted value of the second attribute transformation coefficient can be the difference between the product of the reconstructed value of the first attribute transformation coefficient and the target parameter and the first preset value; and so on. This embodiment does not limit the specific implementation of determining the predicted value of the second attribute transformation coefficient based on the reconstructed value and target parameter of the first attribute transformation coefficient.

[0155] In addition, the residual attribute transformation coefficient corresponding to the second attribute transformation coefficient may be the difference between the second attribute transformation coefficient and the predicted value of the second attribute transformation coefficient; or, the residual attribute transformation coefficient corresponding to the second attribute transformation coefficient may be the sum of the difference between the second attribute transformation coefficient and the predicted value of the second attribute transformation coefficient and a second preset value; or, the residual attribute transformation coefficient corresponding to the second attribute transformation coefficient may be the difference between the second attribute transformation coefficient and the predicted value of the second attribute transformation coefficient and the second preset value; and so on. This embodiment does not limit the specific implementation of obtaining the residual attribute transformation coefficient corresponding to the second attribute transformation coefficient based on the predicted value of the second attribute transformation coefficient and the second attribute transformation coefficient.

[0156] In one embodiment, the predicted value of the second attribute transform coefficient is the product of the reconstructed value of the first attribute transform coefficient and the target parameter. The residual attribute transform coefficient corresponding to the second attribute transform coefficient is the difference between the second attribute transform coefficient and the predicted value of the second attribute transform coefficient.

[0157] For example, the first channel is the Cb channel, the second channel is the Cr channel, and the reconstruction value of the first attribute transformation coefficient is Res Cb_recon , the target parameter is s, and the second attribute transformation coefficient is: Res Cr , the predicted value of the second attribute transformation coefficient is: s·Res Cb_recon The residual attribute transformation coefficient Res corresponding to the second attribute transformation coefficient Cr ' is: Res' Cr =Res Cr -s·Res Cb_recon .

[0158] In this embodiment, the encoder determines a predicted value of the second attribute transform coefficient based on the reconstructed value of the first attribute transform coefficient and a target parameter, where the target parameter is a correlation parameter between the first channel and the second channel. The encoder then obtains a residual attribute transform coefficient corresponding to the second attribute transform coefficient based on the predicted value of the second attribute transform coefficient and the second attribute transform coefficient. In this way, the predicted value of the second attribute transform coefficient is predicted from the reconstructed value of the first attribute transform coefficient using the correlation parameter between the first channel and the second channel. Predictive coding of the second attribute transform coefficient is performed using the correlation between the first channel and the second channel, enabling further compression coding through correlation between color space channels, thereby improving coding efficiency.

[0159] Optionally, the transform tree structure includes multiple layers of nodes, and before the encoder determines the predicted value of the second attribute transform coefficient based on the reconstructed value of the first attribute transform coefficient and the target parameter, the method further includes:

[0160] The encoding end determines the target parameter based on original attribute values ​​of multiple nodes in the layer where the node to be encoded is located;

[0161] The target parameters corresponding to the multiple nodes in the layer where the node to be encoded is located are the same.

[0162] The encoder determining the target parameter based on the original attribute values ​​of the plurality of nodes in the layer where the node to be encoded resides may include determining the target parameter based on attribute transform coefficients of the original attribute values ​​of the plurality of nodes in the layer where the node to be encoded resides. The plurality of nodes may be all nodes, and the encoder may determine the target parameter based on the attribute transform coefficients of the original attribute values ​​of all nodes in the layer where the node to be encoded resides.

[0163] In one embodiment, the attribute transformation coefficients of the original attribute values ​​may include a fifth attribute transformation coefficient corresponding to the first channel and a sixth attribute transformation coefficient corresponding to the second channel. The target parameter may be positively correlated with a third value, the third value being the sum of second products corresponding to each attribute transformation coefficient in the attribute transformation coefficients of the original attribute values ​​of the multiple nodes, the second product being the product of the fifth attribute transformation coefficient and the sixth attribute transformation coefficient; and the target parameter may be negatively correlated with a fourth value, the fourth value being the sum of second square values ​​corresponding to each attribute transformation coefficient in the attribute transformation coefficients of the original attribute values ​​of the multiple nodes, the second square value being the square value of the fifth attribute transformation coefficient.

[0164] In one implementation, the target parameter may be a ratio of the third value to the fourth value.

[0165] For example, the target parameter may be determined based on the original attribute values ​​of all nodes in the layer where the node to be encoded is located. The first channel is the Cb channel, the second channel is the Cr channel, and the target parameter s may be:

[0166] Among them, s L S is the node corresponding to the L layer; The AC coefficient of the Cb channel obtained by transforming the original attribute value of the i-th node in the L layer; is the AC coefficient of the Cr channel obtained by transforming the original attribute value of the i-th node in the L layer, i is a positive integer greater than 0 and less than or equal to n, and n is the total number of nodes in the L layer.

[0167] In this embodiment, the encoding end determines the target parameter based on the original attribute values ​​of multiple nodes in the layer where the node to be encoded resides; wherein the target parameters corresponding to the multiple nodes in the layer where the node to be encoded resides are the same. Thus, by determining the correlation parameter between the first channel and the second channel based on the original attribute values ​​of the multiple nodes in the layer where the node to be encoded resides, the channel correlation of the attribute transform coefficients corresponding to the nodes in the layer where the node to be encoded resides can be better reflected. Furthermore, since the target parameters corresponding to the multiple nodes in the layer where the node to be encoded resides are the same, the target parameters corresponding to the multiple nodes can be determined simultaneously, reducing the amount of computation and thus further improving encoding efficiency.

[0168] Optionally, the code stream of the point cloud to be encoded also includes the encoding result of the target parameter.

[0169] In this embodiment, the code stream of the point cloud to be encoded also includes the encoding result of the target parameter, thereby supporting the decoding end to decode the encoding result of the target parameter in the code stream to obtain the target parameter, so that the decoding end can determine the predicted value of the second attribute transform coefficient based on the reconstructed value of the first attribute transform coefficient and the target parameter, and obtain the reconstructed value of the second attribute transform coefficient based on the predicted value of the second attribute transform coefficient and the reconstructed value of the residual attribute transform coefficient.

[0170] Optionally, before the encoder determines the predicted value of the second attribute transform coefficient based on the reconstructed value of the first attribute transform coefficient and the target parameter, the method further includes:

[0171] The encoding end performs reconstruction processing on at least one target attribute transform coefficient to obtain a reconstructed value of the at least one target attribute transform coefficient, where the target attribute transform coefficient is an encoded attribute transform coefficient;

[0172] The encoding end determines a target parameter corresponding to the node to be encoded based on a reconstructed value of the at least one target attribute transformation coefficient.

[0173] The encoding end may perform quantization and inverse quantization processing on at least one target attribute transformation coefficient to reconstruct and obtain a reconstructed value of the at least one target attribute transformation coefficient.

[0174] In addition, the reconstructed value of the target attribute transform coefficient may include the reconstructed value of the third attribute transform coefficient corresponding to the first channel and the reconstructed value of the fourth attribute transform coefficient corresponding to the second channel. The encoding end may determine a first value, the first value being the sum of the first products corresponding to each target attribute transform coefficient in the at least one target attribute transform coefficient, the first product being the product of the reconstructed value of the third attribute transform coefficient corresponding to the target attribute transform coefficient and the reconstructed value of the corresponding fourth attribute transform coefficient; the encoding end may determine a second value, the second value being the sum of the first square values ​​corresponding to each target attribute transform coefficient in the at least one target attribute transform coefficient, the first square value being the square value of the reconstructed value of the third attribute transform coefficient corresponding to the target attribute transform coefficient; the encoding end may determine the target parameter corresponding to the node to be encoded based on the first value and the second value.

[0175] The target parameter corresponding to the node to be encoded is negatively correlated with the second value, the second value is the sum of the first square values ​​corresponding to each target attribute transformation coefficient in the at least one target attribute transformation coefficient, and the first square value is the square value of the reconstructed value of the third attribute transformation coefficient corresponding to the target attribute transformation coefficient.

[0176] It should be noted that, for the node to be encoded, a target parameter can be calculated for each attribute transformation coefficient of the node to be encoded; or, the t child nodes of the node to be encoded can be divided into a group, and a target parameter is calculated for each group, where t can be 1 or an integer greater than 1.

[0177] In this embodiment, the encoder reconstructs at least one target attribute transform coefficient to obtain a reconstructed value of the at least one target attribute transform coefficient, where the target attribute transform coefficient is an encoded attribute transform coefficient. The encoder then determines the target parameter corresponding to the node to be encoded based on the reconstructed value of the at least one target attribute transform coefficient. Thus, the correlation parameter between the first channel and the second channel is determined using the reconstructed value of the at least one target attribute transform coefficient. Since the decoder can decode the attribute transform coefficient to obtain the reconstructed value of the attribute transform coefficient, the decoder can determine the target parameter corresponding to the node to be decoded based on the reconstructed value of the decoded attribute transform coefficient. This eliminates the need for the encoder to carry the target parameter in the bitstream and does not require encoding the target parameter, further improving encoding efficiency.

[0178] Optionally, the reconstructed value of the target attribute transform coefficient includes a reconstructed value of a third attribute transform coefficient corresponding to the first channel and a reconstructed value of a fourth attribute transform coefficient corresponding to the second channel;

[0179] The target parameter corresponding to the node to be encoded is positively correlated with a first value, the first value being the sum of first products corresponding to each target attribute transform coefficient in the at least one target attribute transform coefficient, the first product being the product of a reconstructed value of the third attribute transform coefficient corresponding to the target attribute transform coefficient and a reconstructed value of the corresponding fourth attribute transform coefficient; or

[0180] The target parameter corresponding to the node to be encoded is negatively correlated with the second value, the second value is the sum of the first square values ​​corresponding to each target attribute transformation coefficient in the at least one target attribute transformation coefficient, and the first square value is the square value of the reconstructed value of the third attribute transformation coefficient corresponding to the target attribute transformation coefficient.

[0181] Among them, the target parameter corresponding to the node to be encoded can be the ratio of the first value to the second value; or, the target parameter corresponding to the node to be encoded can be the sum of the ratio of the first value to the second value and a third preset value; or, the target parameter corresponding to the node to be encoded can be the difference between the ratio of the first value to the second value and the third preset value; and so on. This embodiment does not limit this.

[0182] For example, the target parameter corresponding to the node to be encoded is the ratio of the first value to the second value, the first channel is the Cb channel, the second channel is the Cr channel, and the target parameter s may be:

[0183] Among them, s L,k is the s value corresponding to the kth node of the L layer; is the AC coefficient of the encoded and reconstructed Cb channel of the j-th node in the L layer; is the AC coefficient of the encoded and reconstructed Cr channel of the j-th node of the L layer, where j is a positive integer greater than 0 and less than or equal to k.

[0184] In this embodiment, the target parameter corresponding to the node to be encoded is positively correlated with a first value, where the first value is the sum of the first products corresponding to each target attribute transform coefficient in the at least one target attribute transform coefficient, where the first product is the product of the reconstructed value of the third attribute transform coefficient corresponding to the target attribute transform coefficient and the reconstructed value of the corresponding fourth attribute transform coefficient; or the target parameter corresponding to the node to be encoded is negatively correlated with a second value, where the second value is the sum of the first square values ​​corresponding to each target attribute transform coefficient in the at least one target attribute transform coefficient, where the first square value is the square of the reconstructed value of the third attribute transform coefficient corresponding to the target attribute transform coefficient. In this way, the target parameter corresponding to the node to be encoded can be determined based on the reconstructed value of the third attribute transform coefficient corresponding to the first channel and the reconstructed value of the fourth attribute transform coefficient corresponding to the second channel of the encoded attribute transform coefficient. The determined target parameter can better reflect the correlation between the first channel and the second channel. The decoder can also determine the target parameter corresponding to the node to be decoded based on the reconstructed value of the third attribute transform coefficient corresponding to the first channel and the reconstructed value of the fourth attribute transform coefficient corresponding to the second channel. This eliminates the need for the encoder to carry the target parameter in the bitstream and does not require the encoder to encode the target parameter, thereby further improving encoding efficiency.

[0185] Optionally, the at least one target attribute transformation coefficient is any one of the following:

[0186] A plurality of encoded attribute transformation coefficients of the layer where the node to be encoded is located;

[0187] Attribute transform coefficients of multiple coded nodes.

[0188] The multiple encoded attribute transformation coefficients of the layer where the node to be encoded is located may include all the encoded attribute transformation coefficients of the layer where the node to be encoded is located; or the first m encoded attribute transformation coefficients of the layer where the node to be encoded is located, where m is a positive integer.

[0189] In this embodiment, the at least one target attribute transformation coefficient is a plurality of encoded attribute transformation coefficients of the layer where the node to be encoded is located, so that the target parameter can be calculated using the encoded attribute transformation coefficients of fewer nodes that are relatively close to the node to be encoded, and the channel correlation parameters of the attribute transformation coefficients of the node to be encoded can be calculated more quickly and accurately; or, the at least one target attribute transformation coefficient is the attribute transformation coefficient of multiple encoded nodes, so that it is not limited to the encoded attribute transformation coefficients of the nodes in the layer where the node to be encoded is located, and the target parameter can be calculated using more encoded attribute transformation coefficients, and the channel correlation parameters of the attribute transformation coefficients of the node to be encoded can be calculated more accurately.

[0190] Optionally, the target parameter corresponding to the node to be encoded is a ratio of the first value to the second value.

[0191] Optionally, the reconstructed value of the target attribute transform coefficient includes a reconstructed value of a third attribute transform coefficient corresponding to the first channel and a reconstructed value of a fourth attribute transform coefficient corresponding to the second channel, and the target parameter is:

[0192] Wherein, L is the number of levels where the node to be coded is located, k indicates that the node to be coded is the kth node of the Lth level, and s L,k is the target parameter corresponding to the node to be encoded; is the reconstructed value of the third attribute transformation coefficient corresponding to the target attribute transformation coefficient of the j-th node of the L layer; It is the reconstructed value of the fourth attribute transformation coefficient corresponding to the target attribute transformation coefficient of the j-th node of the L layer, where j is a positive integer greater than 0 and less than or equal to k.

[0193] Optionally, both the first channel and the second channel are chroma channels;

[0194] Or, the first channel is a chroma channel, and the second channel is a luminance channel;

[0195] Alternatively, the first channel is a luminance channel, and the second channel is a chrominance channel.

[0196] In this embodiment, the first channel and the second channel are both chroma channels, so that the attribute transformation coefficients corresponding to the chroma channels can be further compressed and encoded through the correlation between the chroma channels, thereby improving the coding efficiency; or, the first channel is the chroma channel and the second channel is the luminance channel, so that the attribute transformation coefficients corresponding to the chroma channels can be further compressed and encoded through the correlation between the chroma channels and the luminance channels, thereby improving the coding efficiency; or, the first channel is the luminance channel and the second channel is the chroma channel, so that the attribute transformation coefficients corresponding to the luminance channel can be further compressed and encoded through the correlation between the chroma channels and the luminance channels, thereby improving the coding efficiency.

[0197] Referring to FIG5 , FIG5 is a flowchart of a point cloud decoding processing method provided in an embodiment of the present application, which can be applied to a decoding end. As shown in FIG5 , the point cloud decoding processing method includes the following steps:

[0198] Step 21: The decoding end decodes the first encoding result in the bitstream to obtain a reconstructed value of a residual attribute transform coefficient, where the residual attribute transform coefficient is a residual attribute transform coefficient corresponding to the second attribute transform coefficient corresponding to the second channel;

[0199] Step 22: The decoding end performs predictive decoding processing on the reconstructed value of the residual attribute transform coefficient based on the reconstructed value of the first attribute transform coefficient corresponding to the first channel to obtain a reconstructed value of the second attribute transform coefficient, where the first channel and the second channel are different channels of the same color space.

[0200] Step 23: The decoding end obtains the attribute transformation coefficient of the node to be decoded based on the reconstructed value of the second attribute transformation coefficient.

[0201] Optionally, the decoding end performs predictive decoding processing on the reconstructed value of the residual attribute transform coefficient based on the reconstructed value of the first attribute transform coefficient corresponding to the first channel to obtain the reconstructed value of the second attribute transform coefficient, including:

[0202] The decoding end decodes the second encoding result in the code stream to obtain a reconstructed value of a first attribute transformation coefficient corresponding to the first channel;

[0203] The decoding end performs predictive decoding processing on the reconstructed value of the residual attribute transform coefficient based on the reconstructed value of the first attribute transform coefficient to obtain the reconstructed value of the second attribute transform coefficient.

[0204] Optionally, the decoding end performs predictive decoding processing on the reconstructed value of the residual attribute transform coefficient based on the reconstructed value of the first attribute transform coefficient to obtain the reconstructed value of the second attribute transform coefficient, including:

[0205] The decoding end determines a predicted value of the second attribute transform coefficient based on the reconstructed value of the first attribute transform coefficient and a target parameter, where the target parameter is a correlation parameter between the first channel and the second channel;

[0206] The decoding end obtains the reconstructed value of the second attribute transform coefficient based on the predicted value of the second attribute transform coefficient and the reconstructed value of the residual attribute transform coefficient.

[0207] Optionally, before the decoding end determines the predicted value of the second attribute transform coefficient based on the reconstructed value of the first attribute transform coefficient and the target parameter, the method further includes:

[0208] The decoding end decodes the encoding result of the target parameter in the code stream to obtain the target parameter.

[0209] Optionally, the transform tree structure includes multiple layers of nodes, and the target parameters corresponding to multiple nodes in the layer where the node to be decoded is located are the same.

[0210] Optionally, before the decoding end determines the predicted value of the second attribute transform coefficient based on the reconstructed value of the first attribute transform coefficient and the target parameter, the method further includes:

[0211] The decoding end determines a target parameter corresponding to the to-be-decoded node based on a reconstructed value of at least one target attribute transform coefficient, where the target attribute transform coefficient is a decoded attribute transform coefficient.

[0212] Optionally, the reconstructed value of the target attribute transform coefficient includes a reconstructed value of a third attribute transform coefficient corresponding to the first channel and a reconstructed value of a fourth attribute transform coefficient corresponding to the second channel;

[0213] The target parameter corresponding to the node to be decoded is positively correlated with a first value, the first value being the sum of first products corresponding to each target attribute transform coefficient in the at least one target attribute transform coefficient, the first product being the product of a reconstructed value of the third attribute transform coefficient corresponding to the target attribute transform coefficient and a reconstructed value of the corresponding fourth attribute transform coefficient; or

[0214] The target parameter corresponding to the node to be decoded is negatively correlated with a second value, the second value being the sum of the first square values ​​corresponding to each target attribute transformation coefficient in the at least one target attribute transformation coefficient, and the first square value being the square value of the reconstructed value of the third attribute transformation coefficient corresponding to the target attribute transformation coefficient.

[0215] Optionally, the at least one target attribute transformation coefficient is any one of the following:

[0216] A plurality of decoded attribute transformation coefficients of the layer where the node to be decoded is located;

[0217] Attribute transform coefficients of multiple decoded nodes.

[0218] Optionally, the target parameter corresponding to the node to be decoded is a ratio of the first value to the second value.

[0219] Optionally, the reconstructed value of the target attribute transform coefficient includes a reconstructed value of a third attribute transform coefficient corresponding to the first channel and a reconstructed value of a fourth attribute transform coefficient corresponding to the second channel, and the target parameter is:

[0220] Where L is the number of levels where the node to be decoded is located, k indicates that the node to be decoded is the kth node in the L layer, and s L,k is the target parameter corresponding to the node to be decoded; is the reconstructed value of the third attribute transformation coefficient corresponding to the target attribute transformation coefficient of the j-th node of the L layer; It is the reconstructed value of the fourth attribute transformation coefficient corresponding to the target attribute transformation coefficient of the j-th node of the L layer, where j is a positive integer greater than 0 and less than or equal to k.

[0221] Optionally, both the first channel and the second channel are chroma channels;

[0222] Or, the first channel is a chroma channel, and the second channel is a luminance channel;

[0223] Alternatively, the first channel is a luminance channel, and the second channel is a chrominance channel.

[0224] It should be noted that this embodiment is an implementation of the decoding side corresponding to the embodiment shown in Figure 4. Its specific implementation can refer to the relevant description of the embodiment shown in Figure 4. In order to avoid repetition, this embodiment will not be repeated, and the same beneficial effects can still be achieved.

[0225] The point cloud encoding processing method and the point cloud decoding processing method of the embodiment of the present application are described below through two specific embodiments:

[0226] Example 1:

[0227] This embodiment proposes a prediction algorithm between color components of point cloud attributes. Specifically, the correlation between color and chrominance channels is utilized, and the transform coefficients of the Cr channel are reconstructed through the transform coefficients of the Cb channel to perform secondary predictive coding, thereby achieving the purpose of improving coding efficiency.

[0228] The point cloud encoding processing method at the encoding end includes the following steps:

[0229] Step 31: Reorder the point cloud and construct an N-layer RAHT transform tree using a bottom-up construction method. The bottom layer contains all nodes, and the top layer is the root node layer, which contains only one node.

[0230] Step 32: Based on the transformation tree structure, perform upsampling prediction and region-adaptive hierarchical transformation on each node layer by layer starting from the root node from top to bottom.

[0231] Determine whether the current node needs to be predicted. The specific prediction method has been described in the introduction of the RAHT-based attribute encoding process above and will not be repeated here.

[0232] If prediction is not performed, the original attribute information of the child nodes of the current node is directly subjected to RAHT transformation to obtain the AC transformation coefficient. If prediction is performed, the original attribute value and the attribute prediction value are subjected to RAHT transformation respectively, and the obtained AC transformation coefficients are subtracted to obtain the AC residual transformation coefficient.

[0233] Step 33: For attribute lossy encoding, the color attribute is encoded in YCbCr format. For the chrominance component, let Res Cb and Res Cr are the transformation coefficients of the Cb and Cr channels respectively. The transformation coefficients of the Cb channel and the transformation coefficients of the Cr channel can be approximated by the following relationship:

[0234] Therefore, the transform coefficients of the Cb channel are first encoded, and the reconstructed transform coefficients Res of the Cb channel are obtained through quantization and dequantization. Cb__recon , the transformation coefficient of the Cr channel is further processed to obtain Res Cr ′: Res′ Cr =Res Cr -s·Res Cb_recon

[0235] Res Cr ' Perform subsequent quantization and entropy coding.

[0236] Where s is a scalar value. When the two channels are correlated, s can be positive or negative. In some cases, when the two channels may not have a significant correlation, s is 0.

[0237] This embodiment does not limit the calculation method of s. A set of fixed s values ​​can be set, or s can be calculated based on some known information. Specific calculation methods of s include the following:

[0238] The first method: The transform tree constructed by RAHT has multiple layers. All layers are traversed, and an s value is calculated for each layer, stored in LastCompPredCoeff[L], and sent to the bitstream.

[0239] Assuming the current layer is L layer, given and Calculate the AC coefficients obtained by transforming the original attribute values ​​of all nodes in a layer, and obtain the S values ​​corresponding to the nodes in this layer:

[0240] Among them, s L S is the node corresponding to the L layer; The AC coefficient of the Cb channel obtained by transforming the original attribute value of the i-th node in the L layer; is the AC coefficient of the Cr channel obtained by transforming the original attribute value of the i-th node in the L layer, i is a positive integer greater than 0 and less than or equal to n, and n is the total number of nodes in the L layer.

[0241] The second method is to traverse all layers. Assume that the current layer is L. The current node k in L consists of 2*2*2 child nodes. Each node can obtain multiple transform coefficients. The transform coefficient of node k used for inter-component prediction can be calculated from the transform coefficient that has been encoded and reconstructed in the previous order:

[0242] Among them, s L,k is the s value corresponding to the kth node of the L layer; is the AC coefficient of the encoded and reconstructed Cb channel of the j-th node in the L layer; is the AC coefficient of the encoded and reconstructed Cr channel of the j-th node of the L layer, where j is a positive integer greater than 0 and less than or equal to k.

[0243] It should be noted that s can be calculated for each transform coefficient, or n nodes can be grouped together and only one s can be calculated for each group, where n can be 1 or an integer greater than 1. Furthermore, the range of coded transform coefficients used to calculate s can include all coded transform coefficients of the same layer, the first m coded transform coefficients of the same layer, or all coded transform coefficients of the first k nodes.

[0244] In addition, the s determined by the second method can be calculated using the encoded information, and the s does not need to be input into the code stream.

[0245] Example 2:

[0246] This embodiment is a solution at the decoding end corresponding to the encoding end of the first embodiment, and this embodiment is a solution at the decoding end for the first method of calculating s in the first embodiment.

[0247] The point cloud decoding processing method at the decoding end includes the following steps:

[0248] Step 41: Determine LastCompPredCoeff from the bitstream, that is, the value of s.

[0249] Step 42: Obtain the data to be decoded from the bit stream, perform entropy decoding and inverse quantization on the data to be decoded to obtain a reconstructed value of the transform coefficient.

[0250] Step 43: Reorder the point cloud and construct an N-layer RAHT transform tree using a bottom-up construction method. The bottom layer contains all nodes, and the top layer is the root node layer, which contains only one node.

[0251] Step 44: Based on the transformation tree structure, perform upsampling prediction and RAHT inverse transformation on each node layer by layer starting from the root node from top to bottom.

[0252] Assume that the current layer is L layer. For the current node of L layer, get the s corresponding to the current layer from LastCompPredCoeff. Let the reconstruction values ​​of the transform coefficients of Cb and Cr be Res respectively. Cb__recon and Res Cr__recon , the processed Cr transform coefficient reconstruction value Res Cr__recon ′ is: Res Cr__recon ′=Res Cr__recon +s*Res Cb__recon

[0253] First, when the current node has only one occupied child node, no prediction is performed; when the number of neighbor parent nodes of the current node (i.e., the number of grandparent neighbors of the child node of the current node) is less than a threshold of 1 (for example, threshold 1 = 2), no prediction is performed, and the AC coefficient reconstruction value is obtained from the transformation coefficient reconstruction value. The DC coefficient can be inherited from the parent node, and the RAHT inverse transform is performed to obtain the reconstruction value of the child node of the current node;

[0254] If threshold 1 is met, neighbors are searched for the child nodes of the current node. The neighbor search range includes: the current node, neighbor parent nodes that are coplanar and colinear with the child nodes of the current node, and neighbor child nodes that are coplanar and colinear with the child nodes of the current node. When the number of neighbor parent nodes found is less than threshold 2 (for example, threshold 2 = 6), no prediction is performed, and the AC coefficient reconstruction value is obtained from the transformed coefficient reconstruction value. Its DC coefficient can be inherited from the parent node, and the RAHT inverse transform is performed to obtain the reconstruction value of the child node of the current node;

[0255] If threshold 2 is met, the predicted attribute value of the current child node is obtained based on the neighboring nodes. The predicted attribute value is transformed to obtain the AC coefficient of the predicted value, and added to the AC residual coefficient reconstruction value to obtain the AC coefficient reconstruction value. Its DC coefficient can be inherited from the parent node, and the RAHT inverse transformation is performed to obtain the reconstruction value of the child node of the current node.

[0256] When traversing to the bottom layer, what is obtained is the reconstructed attribute values ​​of all nodes, thus completing the attribute decoding.

[0257] Example 3:

[0258] This embodiment is a solution at the decoding end corresponding to the encoding end of the first embodiment, and this embodiment is a solution at the decoding end for the second method of calculating s in the first embodiment.

[0259] The point cloud decoding processing method at the decoding end includes the following steps:

[0260] Step 51: Obtain data to be decoded from the bitstream, perform entropy decoding and inverse quantization on the data to be decoded to obtain a reconstructed value of a transform coefficient.

[0261] Step 52: Reorder the point cloud and construct an N-layer RAHT transform tree using a bottom-up construction method. The bottom layer contains all nodes, and the top layer is the root node layer, which contains only one node.

[0262] Step 53: Based on the transformation tree structure, perform upsampling prediction and RAHT inverse transformation on each node layer by layer starting from the root node from top to bottom.

[0263] Assume that the current layer is L layer, and the current node k of the current L layer is composed of 2*2*2 child nodes. The transform coefficient of this node is used for inter-component prediction. The transform coefficient s is calculated by the transform coefficient of the node that has been decoded in the previous order:

[0264] Among them, s L,k is the s value corresponding to the kth node of the L layer; is the decoded AC coefficient of the Cb channel of the j-th node in the L layer; is the AC coefficient of the decoded Cr channel of the j-th node in the L layer, where j is a positive integer greater than 0 and less than or equal to k.

[0265] It should be noted that s can be calculated for each transform coefficient, or n nodes can be grouped together and only one s can be calculated for each group, where n can be 1 or an integer greater than 1. Furthermore, the range of decoded transform coefficients used to calculate s can include all decoded transform coefficients of the same layer, the first m decoded transform coefficients of the same layer, or all decoded transform coefficients of the first k nodes.

[0266] Assume that the reconstruction values ​​of the transform coefficients of Cb and Cr are Res Cb__recon and Res Cr__recon , the processed Cr transform coefficient reconstruction value Res Cr__recon ′ is: Res Cr__recon ′=Res Cr__recon +s*Res Cb__recon

[0267] First, when the current node has only one occupied child node, no prediction is performed; when the number of neighbor parent nodes of the current node (i.e., the number of grandparent neighbors of the child node of the current node) is less than a threshold of 1 (for example, threshold 1 = 2), no prediction is performed, and the AC coefficient reconstruction value is obtained from the transformation coefficient reconstruction value. The DC coefficient can be inherited from the parent node, and the RAHT inverse transform is performed to obtain the reconstruction value of the child node of the current node;

[0268] If threshold 1 is met, neighbors are searched for the child nodes of the current node. The neighbor search range includes: the current node, neighbor parent nodes that are coplanar and colinear with the child nodes of the current node, and neighbor child nodes that are coplanar and colinear with the child nodes of the current node. When the number of neighbor parent nodes found is less than threshold 2 (for example, threshold 2 = 6), no prediction is performed, and the AC coefficient reconstruction value is obtained from the transformed coefficient reconstruction value. Its DC coefficient can be inherited from the parent node, and the RAHT inverse transform is performed to obtain the reconstruction value of the child node of the current node;

[0269] If threshold 2 is met, the predicted attribute value of the current child node is obtained based on the neighboring nodes. The predicted attribute value is transformed to obtain the AC coefficient of the predicted value, and added to the AC residual coefficient reconstruction value to obtain the AC coefficient reconstruction value. Its DC coefficient can be inherited from the parent node, and the RAHT inverse transformation is performed to obtain the reconstruction value of the child node of the current node.

[0270] When traversing to the bottom layer, what is obtained is the reconstructed attribute values ​​of all nodes, thus completing the attribute decoding.

[0271] The embodiment of the present application proposes a method for predicting the chromaticity components of point cloud attributes, which utilizes the correlation between color and chromaticity channels, reconstructs the transformation coefficients of the Cb channel, and performs secondary prediction encoding on the transformation coefficients of the Cr channel, thereby achieving the purpose of reducing the bit rate and improving the coding efficiency.

[0272] As shown in Figures 6 and 7, encoding and decoding by the point cloud encoding processing method and the point cloud decoding processing method of the embodiment of the present application can reduce the bit rate and improve the encoding efficiency. In Figures 6 and 7, the first vertical column characterizes six types of point clouds graded according to the density of the point cloud, such as: solid average, dense average, sparse average, etc. Overall average represents the average value of the above six types of point clouds. Avg.Enc Time represents the encoding time, and Avg.Dec Time represents the decoding time. luma, ChromaCb, ChromaCr and Reflectance are the attribute components. C1_ai and C2_ai are the point cloud encoding test conditions, C1_ai represents lossless geometry and lossy attributes (Lossless geometry, Lossy attributes [all intra]); C2_ai represents lossy geometry and lossy attributes (Lossy geometry, Lossy attributes [all intra]). End-to-End BD-AttrRate represents a parameter for evaluating point cloud attribute quality distortion. A negative value for End-to-End BD-AttrRate indicates that the overall quality of the point cloud produced by encoding the point cloud using this embodiment is higher than that produced by encoding using related technologies. Figures 6 and 7 show that, with encoding and decoding times remaining essentially unchanged, the overall quality of the point cloud produced by encoding the point cloud using this embodiment is higher than that produced by encoding using related technologies.

[0273] It should be noted that the above embodiment can also be used to perform secondary prediction coding on the transform coefficients of the Cb channel through the reconstructed transform coefficients of the Cr channel. For example, the relevant information of the Cb channel and the Cr channel in the above embodiment can be interchanged. The above embodiment can also be used for prediction between luminance and chrominance components, that is, prediction between the Y channel and the Cb channel, and between the Y channel and the Cr channel. For example, the transform coefficients of the Y channel can be secondary prediction coded through the reconstructed transform coefficients of the Cr channel; or the transform coefficients of the Y channel can be secondary prediction coded through the reconstructed transform coefficients of the Cb channel; or the transform coefficients of the Cr channel can be secondary prediction coded through the reconstructed transform coefficients of the Y channel; or the transform coefficients of the Cb channel can be secondary prediction coded through the reconstructed transform coefficients of the Y channel.

[0274] It should be noted that the point cloud coding processing method provided in the embodiments of the present application can be executed by a point cloud coding processing device, or by a control module in the point cloud coding processing device for executing the point cloud coding processing method. In the embodiments of the present application, the point cloud coding processing device provided in the embodiments of the present application is described by taking the point cloud coding processing device executing the point cloud coding processing method as an example.

[0275] Please refer to FIG8 , which is a structural diagram of a point cloud coding processing device provided in an embodiment of the present application. As shown in FIG8 , the point cloud coding processing device 400 includes:

[0276] A transformation module 401 is configured to perform transformation processing based on a transformation tree structure corresponding to a point cloud to be encoded, and obtain attribute transformation coefficients of nodes to be encoded in the transformation tree structure, wherein the attribute transformation coefficients of the nodes to be encoded include a first attribute transformation coefficient corresponding to a first channel and a second attribute transformation coefficient corresponding to a second channel, where the first channel and the second channel are different channels in the same color space;

[0277] A first encoding module 402 is configured to perform predictive encoding processing on the second attribute transform coefficient based on the first attribute transform coefficient to obtain a residual attribute transform coefficient corresponding to the second attribute transform coefficient;

[0278] A second encoding module 403 is configured to perform encoding processing on the residual attribute transform coefficient to obtain a first encoding result;

[0279] The code stream of the point cloud to be encoded includes the first encoding result.

[0280] Optionally, the first encoding module includes:

[0281] a first encoding unit, configured to perform encoding processing on the first attribute transformation coefficient to obtain a second encoding result;

[0282] a reconstruction unit, configured to reconstruct the first attribute transform coefficient based on the second encoding result to obtain a reconstructed value of the first attribute transform coefficient;

[0283] The second encoding unit is configured to perform predictive encoding processing on the second attribute transform coefficient based on the reconstructed value of the first attribute transform coefficient to obtain a residual attribute transform coefficient corresponding to the second attribute transform coefficient.

[0284] Optionally, the second encoding unit is specifically configured to:

[0285] determining a predicted value of the second attribute transform coefficient based on a reconstructed value of the first attribute transform coefficient and a target parameter, wherein the target parameter is a correlation parameter between the first channel and the second channel;

[0286] Based on the predicted value of the second attribute transformation coefficient and the second attribute transformation coefficient, a residual attribute transformation coefficient corresponding to the second attribute transformation coefficient is obtained.

[0287] Optionally, the transformation tree structure includes multiple layers of nodes, and the apparatus further includes:

[0288] A first determining module, configured to determine a target parameter based on original attribute values ​​of multiple nodes in the layer where the node to be encoded is located;

[0289] The target parameters corresponding to the multiple nodes in the layer where the node to be encoded is located are the same.

[0290] Optionally, the code stream of the point cloud to be encoded also includes the encoding result of the target parameter.

[0291] Optionally, the device further comprises:

[0292] a reconstruction module, configured to perform reconstruction processing on at least one target attribute transform coefficient to obtain a reconstructed value of the at least one target attribute transform coefficient, wherein the target attribute transform coefficient is an encoded attribute transform coefficient;

[0293] The second determination module is configured to determine a target parameter corresponding to the node to be encoded based on a reconstructed value of the at least one target attribute transformation coefficient.

[0294] Optionally, the reconstructed value of the target attribute transform coefficient includes a reconstructed value of a third attribute transform coefficient corresponding to the first channel and a reconstructed value of a fourth attribute transform coefficient corresponding to the second channel;

[0295] The target parameter corresponding to the node to be encoded is positively correlated with a first value, the first value being the sum of first products corresponding to each target attribute transform coefficient in the at least one target attribute transform coefficient, the first product being the product of a reconstructed value of the third attribute transform coefficient corresponding to the target attribute transform coefficient and a reconstructed value of the corresponding fourth attribute transform coefficient; or

[0296] The target parameter corresponding to the node to be encoded is negatively correlated with the second value, the second value is the sum of the first square values ​​corresponding to each target attribute transformation coefficient in the at least one target attribute transformation coefficient, and the first square value is the square value of the reconstructed value of the third attribute transformation coefficient corresponding to the target attribute transformation coefficient.

[0297] Optionally, the at least one target attribute transformation coefficient is any one of the following:

[0298] A plurality of encoded attribute transformation coefficients of the layer where the node to be encoded is located;

[0299] Attribute transform coefficients of multiple coded nodes.

[0300] Optionally, the target parameter corresponding to the node to be encoded is a ratio of the first value to the second value.

[0301] Optionally, the reconstructed value of the target attribute transform coefficient includes a reconstructed value of a third attribute transform coefficient corresponding to the first channel and a reconstructed value of a fourth attribute transform coefficient corresponding to the second channel, and the target parameter is:

[0302] Wherein, L is the number of levels where the node to be coded is located, k indicates that the node to be coded is the kth node of the Lth level, s L,k is the target parameter corresponding to the node to be encoded; is the reconstructed value of the third attribute transformation coefficient corresponding to the target attribute transformation coefficient of the j-th node of the L layer; It is the reconstructed value of the fourth attribute transformation coefficient corresponding to the target attribute transformation coefficient of the j-th node of the L layer, where j is a positive integer greater than 0 and less than or equal to k.

[0303] Optionally, both the first channel and the second channel are chroma channels;

[0304] Or, the first channel is a chroma channel, and the second channel is a luminance channel;

[0305] Alternatively, the first channel is a luminance channel, and the second channel is a chrominance channel.

[0306] The point cloud coding processing device in the embodiment of the present application can be a device, a device or electronic device with an operating system, or a component, integrated circuit, or chip in a terminal. The device or electronic device can be a mobile terminal or a non-mobile terminal. For example, the mobile terminal can include but is not limited to the types of terminals listed above, and the non-mobile terminal can be a server, a network attached storage (NAS), a personal computer (PC), a television (TV), an ATM or a self-service machine, etc., which is not specifically limited in the embodiment of the present application.

[0307] The point cloud coding processing device provided in the embodiment of the present application can implement each process implemented in the method embodiment of Figure 4 and achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0308] It should be noted that the point cloud decoding processing method provided in the embodiments of the present application can be executed by a point cloud decoding processing device, or by a control module in the point cloud decoding processing device for executing the point cloud decoding processing method. In the embodiments of the present application, the point cloud decoding processing device provided in the embodiments of the present application is described by taking the point cloud decoding processing device executing the point cloud decoding processing method as an example.

[0309] Please refer to FIG9 , which is a structural diagram of a point cloud decoding processing device provided in an embodiment of the present application. As shown in FIG9 , the point cloud decoding processing device 500 includes:

[0310] A first decoding module 501 is configured to decode a first encoding result in a bitstream to obtain a reconstructed value of a residual attribute transform coefficient, where the residual attribute transform coefficient is a residual attribute transform coefficient corresponding to a second attribute transform coefficient corresponding to a second channel;

[0311] A second decoding module 502 is configured to perform predictive decoding processing on the reconstructed value of the residual attribute transform coefficient based on the reconstructed value of the first attribute transform coefficient corresponding to the first channel to obtain the reconstructed value of the second attribute transform coefficient, where the first channel and the second channel are different channels in the same color space;

[0312] The acquisition module 503 is configured to acquire the attribute transformation coefficient of the node to be decoded based on the reconstructed value of the second attribute transformation coefficient.

[0313] Optionally, the second decoding module includes:

[0314] A first decoding unit, configured to decode the second encoding result in the code stream to obtain a reconstructed value of a first attribute transform coefficient corresponding to the first channel;

[0315] The second decoding unit is configured to perform predictive decoding processing on the reconstructed value of the residual attribute transform coefficient based on the reconstructed value of the first attribute transform coefficient to obtain the reconstructed value of the second attribute transform coefficient.

[0316] Optionally, the second decoding unit is specifically configured to:

[0317] determining a predicted value of the second attribute transform coefficient based on a reconstructed value of the first attribute transform coefficient and a target parameter, wherein the target parameter is a correlation parameter between the first channel and the second channel;

[0318] The reconstructed value of the second attribute transform coefficient is obtained based on the predicted value of the second attribute transform coefficient and the reconstructed value of the residual attribute transform coefficient.

[0319] Optionally, the device further comprises:

[0320] The third decoding module is used to decode the encoding result of the target parameter in the code stream to obtain the target parameter.

[0321] Optionally, the transform tree structure includes multiple layers of nodes, and the target parameters corresponding to multiple nodes in the layer where the node to be decoded is located are the same.

[0322] Optionally, the device further comprises:

[0323] The determination module is configured to determine a target parameter corresponding to the to-be-decoded node based on a reconstructed value of at least one target attribute transformation coefficient, wherein the target attribute transformation coefficient is a decoded attribute transformation coefficient.

[0324] Optionally, the reconstructed value of the target attribute transform coefficient includes a reconstructed value of a third attribute transform coefficient corresponding to the first channel and a reconstructed value of a fourth attribute transform coefficient corresponding to the second channel;

[0325] The target parameter corresponding to the node to be decoded is positively correlated with a first value, the first value being the sum of first products corresponding to each target attribute transform coefficient in the at least one target attribute transform coefficient, the first product being the product of a reconstructed value of the third attribute transform coefficient corresponding to the target attribute transform coefficient and a reconstructed value of the corresponding fourth attribute transform coefficient; or

[0326] The target parameter corresponding to the node to be decoded is negatively correlated with a second value, the second value being the sum of the first square values ​​corresponding to each target attribute transformation coefficient in the at least one target attribute transformation coefficient, and the first square value being the square value of the reconstructed value of the third attribute transformation coefficient corresponding to the target attribute transformation coefficient.

[0327] Optionally, the at least one target attribute transformation coefficient is any one of the following:

[0328] A plurality of decoded attribute transformation coefficients of the layer where the node to be decoded is located;

[0329] Attribute transform coefficients of multiple decoded nodes.

[0330] Optionally, the target parameter corresponding to the node to be decoded is a ratio of the first value to the second value.

[0331] Optionally, the reconstructed value of the target attribute transform coefficient includes a reconstructed value of a third attribute transform coefficient corresponding to the first channel and a reconstructed value of a fourth attribute transform coefficient corresponding to the second channel, and the target parameter is:

[0332] Where L is the number of levels where the node to be decoded is located, k indicates that the node to be decoded is the kth node in the L layer, and s L,kis the target parameter corresponding to the node to be decoded; is the reconstructed value of the third attribute transformation coefficient corresponding to the target attribute transformation coefficient of the j-th node of the L layer; It is the reconstructed value of the fourth attribute transformation coefficient corresponding to the target attribute transformation coefficient of the j-th node of the L layer, where j is a positive integer greater than 0 and less than or equal to k.

[0333] Optionally, both the first channel and the second channel are chroma channels;

[0334] Or, the first channel is a chroma channel, and the second channel is a luminance channel;

[0335] Alternatively, the first channel is a luminance channel, and the second channel is a chrominance channel.

[0336] The point cloud decoding processing device in the embodiment of the present application can be a device, a device or electronic device with an operating system, or a component, integrated circuit, or chip in a terminal. The device or electronic device can be a mobile terminal or a non-mobile terminal. For example, the mobile terminal can include but is not limited to the types of terminals listed above, and the non-mobile terminal can be a server, a network attached storage (NAS), a personal computer (PC), a television (TV), an ATM or a self-service machine, etc., which is not specifically limited in the embodiment of the present application.

[0337] The point cloud decoding and processing device provided in the embodiment of the present application can implement the various processes implemented in the method embodiment of Figure 5 and achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0338] Optionally, as shown in FIG10 , an embodiment of the present application further provides an electronic device 600, comprising a processor 601 and a memory 602. The memory 602 stores programs or instructions that can be run on the processor 601. For example, when the electronic device 600 is an encoding end device, the program or instruction, when executed by the processor 601, implements the various steps of the above-mentioned point cloud encoding processing method embodiment and can achieve the same technical effect. When the electronic device 600 is a decoding end device, the program or instruction, when executed by the processor 601, implements the various steps of the above-mentioned point cloud decoding processing method embodiment and can achieve the same technical effect. To avoid repetition, they will not be described here.

[0339] The present application also provides an electronic device including: a memory configured to store video data; and a processing circuit configured to implement the steps of the above-described point cloud encoding method or point cloud decoding method. Optionally, the memory may be memory 102 or memory 113 in the embodiment shown in FIG. 1 , and the processing circuit may implement the functions of encoder 200 or decoder 300 in the embodiments shown in FIG. 1-3 .

[0340] The present application also provides an electronic device including a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is configured to execute a program or instruction to implement the steps in the method embodiment shown in Figures 4 or 5. This device embodiment corresponds to the aforementioned method embodiment, and each implementation process and implementation method of the aforementioned method embodiment is applicable to this terminal embodiment and can achieve the same technical effects.

[0341] The electronic device may be a terminal, or may be other devices other than a terminal, such as a server, a network attached storage (NAS), etc.

[0342] Among them, the terminal can be a mobile phone, tablet personal computer, laptop computer, notebook computer, personal digital assistant (PDA), handheld computer, netbook, ultra-mobile personal computer (UMPC), mobile Internet device (MID), augmented reality (AR), virtual reality (VR) equipment, mixed reality (MR) equipment, robot, wearable device, flight vehicle, vehicle user equipment (VUE), shipborne equipment, pedestrian user equipment (PUE), smart home (home appliances with wireless communication function, such as refrigerator, TV, washing machine or furniture, etc.), game console, personal computer (PC), ATM or self-service machine and other terminal-side devices. Wearable devices include: smart watches, smart bracelets, smart headphones, smart glasses, smart jewelry (smart bracelets, smart bracelets, smart rings, smart necklaces, smart anklets, smart anklets, etc.), smart wristbands, smart clothing, etc. Among them, vehicle-mounted devices can also be called vehicle-mounted terminals, vehicle-mounted controllers, vehicle-mounted modules, vehicle-mounted components, vehicle-mounted chips, or vehicle-mounted units, etc. It should be noted that the specific type of terminal is not limited in the embodiments of this application.

[0343] The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that can provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), or cloud computing services based on big data and artificial intelligence platforms.

[0344] For example, the electronic device may include but is not limited to the source device 100 or the destination device 110 shown in FIG. 1 .

[0345] Taking an electronic device as a terminal as an example, FIG11 is a schematic diagram of the hardware structure of a terminal implementing an embodiment of the present application.

[0346] The terminal 700 includes but is not limited to: a radio frequency unit 701, a network module 702, an audio output unit 703, an input unit 704, a sensor 705, a display unit 706, a user input unit 707, an interface unit 708, a memory 709 and at least some of the components of the processor 710.

[0347] Those skilled in the art will appreciate that the terminal 700 may also include a power supply (such as a battery) to power various components. The power supply may be logically connected to the processor 710 through a power management system, thereby implementing functions such as charging, discharging, and power consumption management through the power management system. The terminal structure shown in FIG11 does not constitute a limitation of the terminal. The terminal may include more or fewer components than shown, or combine certain components, or have different component arrangements, which will not be described in detail here.

[0348] It should be understood that in an embodiment of the present application, the input unit 704 may include a graphics processing unit (GPU) 7041 and a microphone 7042, and the graphics processor 7041 processes the image data of a static picture or video obtained by an image capture device (such as a camera) in a video capture mode or an image capture mode. The display unit 706 may include a display panel 7061, and the display panel 7061 may be configured in the form of a liquid crystal display, an organic light emitting diode, etc. The user input unit 707 includes a touch panel 7071 and at least one of other input devices 7072. The touch panel 7071 is also called a touch screen. The touch panel 7071 may include two parts: a touch detection device and a touch controller. Other input devices 7072 may include, but are not limited to, a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, and an operating stick, which will not be repeated here.

[0349] In the embodiment of the present application, after receiving downlink data from a network-side device, the RF unit 701 may transmit the data to the processor 710 for processing. Furthermore, the RF unit 701 may send uplink data to the network-side device. Typically, the RF unit 701 includes, but is not limited to, an antenna, an amplifier, a transceiver, a coupler, a low-noise amplifier, a duplexer, and the like.

[0350] The memory 709 can be used to store software programs or instructions and various data. The memory 709 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, applications or instructions required for at least one function (such as a sound playback function, an image playback function, etc.). In addition, the memory 709 may include a volatile memory or a non-volatile memory, or the memory 709 may include both volatile and non-volatile memories. Among them, the non-volatile memory may 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 may be a random access memory (RAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), a synchronous dynamic random access memory (SDRAM), a double data rate synchronous dynamic random access memory (DDRSDRAM), an enhanced synchronous dynamic random access memory (ESDRAM), a synchronous link dynamic random access memory (SLDRAM), and a direct memory bus random access memory (DRRAM). The memory 709 in the embodiment of the present application includes but is not limited to these and any other suitable types of memory.

[0351] Processor 710 may include one or more processing units. Optionally, processor 710 integrates an application processor and a modem processor. The application processor primarily handles operations related to the operating system, user interface, and application programs, while the modem processor primarily processes wireless communication signals, such as a baseband processor. It is understood that the modem processor may not be integrated into processor 710.

[0352] Wherein, when the terminal is an encoding terminal:

[0353] The processor 710 is configured to:

[0354] Performing a transformation process based on a transformation tree structure corresponding to the point cloud to be encoded, obtaining attribute transformation coefficients of the nodes to be encoded in the transformation tree structure, wherein the attribute transformation coefficients of the nodes to be encoded include a first attribute transformation coefficient corresponding to a first channel and a second attribute transformation coefficient corresponding to a second channel, where the first channel and the second channel are different channels of the same color space;

[0355] performing predictive coding processing on the second attribute transform coefficient based on the first attribute transform coefficient to obtain a residual attribute transform coefficient corresponding to the second attribute transform coefficient;

[0356] performing encoding processing on the residual attribute transformation coefficient to obtain a first encoding result;

[0357] The code stream of the point cloud to be encoded includes the first encoding result.

[0358] Optionally, the processor 710 is specifically configured to:

[0359] performing encoding processing on the first attribute transformation coefficient to obtain a second encoding result;

[0360] reconstructing the first attribute transform coefficient based on the second encoding result to obtain a reconstructed value of the first attribute transform coefficient;

[0361] Based on the reconstructed value of the first attribute transform coefficient, a predictive coding process is performed on the second attribute transform coefficient to obtain a residual attribute transform coefficient corresponding to the second attribute transform coefficient.

[0362] Optionally, the processor 710 is specifically configured to:

[0363] determining a predicted value of the second attribute transform coefficient based on a reconstructed value of the first attribute transform coefficient and a target parameter, wherein the target parameter is a correlation parameter between the first channel and the second channel;

[0364] Based on the predicted value of the second attribute transformation coefficient and the second attribute transformation coefficient, a residual attribute transformation coefficient corresponding to the second attribute transformation coefficient is obtained.

[0365] Optionally, the transformation tree structure includes multiple layers of nodes, and the processor 710 is further configured to:

[0366] Determine the target parameter based on the original attribute values ​​of multiple nodes in the layer where the node to be encoded is located;

[0367] The target parameters corresponding to the multiple nodes in the layer where the node to be encoded is located are the same.

[0368] Optionally, the code stream of the point cloud to be encoded also includes the encoding result of the target parameter.

[0369] Optionally, the processor 710 is further configured to:

[0370] Reconstructing at least one target attribute transform coefficient to obtain a reconstructed value of the at least one target attribute transform coefficient, where the target attribute transform coefficient is an encoded attribute transform coefficient;

[0371] A target parameter corresponding to the node to be encoded is determined based on the reconstructed value of the at least one target attribute transformation coefficient.

[0372] Optionally, the reconstructed value of the target attribute transform coefficient includes a reconstructed value of a third attribute transform coefficient corresponding to the first channel and a reconstructed value of a fourth attribute transform coefficient corresponding to the second channel;

[0373] The target parameter corresponding to the node to be encoded is positively correlated with a first value, the first value being the sum of first products corresponding to each target attribute transform coefficient in the at least one target attribute transform coefficient, the first product being the product of a reconstructed value of the third attribute transform coefficient corresponding to the target attribute transform coefficient and a reconstructed value of the corresponding fourth attribute transform coefficient; or

[0374] The target parameter corresponding to the node to be encoded is negatively correlated with the second value, the second value is the sum of the first square values ​​corresponding to each target attribute transformation coefficient in the at least one target attribute transformation coefficient, and the first square value is the square value of the reconstructed value of the third attribute transformation coefficient corresponding to the target attribute transformation coefficient.

[0375] Optionally, the at least one target attribute transformation coefficient is any one of the following:

[0376] A plurality of encoded attribute transformation coefficients of the layer where the node to be encoded is located;

[0377] Attribute transform coefficients of multiple coded nodes.

[0378] Optionally, the target parameter corresponding to the node to be encoded is a ratio of the first value to the second value.

[0379] Optionally, the reconstructed value of the target attribute transform coefficient includes a reconstructed value of a third attribute transform coefficient corresponding to the first channel and a reconstructed value of a fourth attribute transform coefficient corresponding to the second channel, and the target parameter is:

[0380] Wherein, L is the number of levels where the node to be coded is located, k indicates that the node to be coded is the kth node of the Lth level, and s L,k is the target parameter corresponding to the node to be encoded; is the reconstructed value of the third attribute transformation coefficient corresponding to the target attribute transformation coefficient of the j-th node of the L layer; It is the reconstructed value of the fourth attribute transformation coefficient corresponding to the target attribute transformation coefficient of the j-th node of the L layer, where j is a positive integer greater than 0 and less than or equal to k.

[0381] Optionally, both the first channel and the second channel are chroma channels;

[0382] Or, the first channel is a chroma channel, and the second channel is a luminance channel;

[0383] Alternatively, the first channel is a luminance channel, and the second channel is a chrominance channel.

[0384] When the terminal is a decoding terminal:

[0385] The processor 710 is configured to:

[0386] Decoding the first encoding result in the code stream to obtain a reconstructed value of a residual attribute transform coefficient, where the residual attribute transform coefficient is a residual attribute transform coefficient corresponding to the second attribute transform coefficient corresponding to the second channel;

[0387] performing predictive decoding processing on the reconstructed value of the residual attribute transform coefficient based on the reconstructed value of the first attribute transform coefficient corresponding to the first channel to obtain a reconstructed value of the second attribute transform coefficient, wherein the first channel and the second channel are different channels of the same color space;

[0388] Based on the reconstructed value of the second attribute transformation coefficient, the attribute transformation coefficient of the node to be decoded is obtained.

[0389] Optionally, the processor 710 is specifically configured to:

[0390] Decoding the second encoding result in the code stream to obtain a reconstructed value of a first attribute transform coefficient corresponding to the first channel;

[0391] Based on the reconstructed value of the first attribute transform coefficient, a prediction decoding process is performed on the reconstructed value of the residual attribute transform coefficient to obtain the reconstructed value of the second attribute transform coefficient.

[0392] Optionally, the processor 710 is specifically configured to:

[0393] determining a predicted value of the second attribute transform coefficient based on a reconstructed value of the first attribute transform coefficient and a target parameter, wherein the target parameter is a correlation parameter between the first channel and the second channel;

[0394] The reconstructed value of the second attribute transform coefficient is obtained based on the predicted value of the second attribute transform coefficient and the reconstructed value of the residual attribute transform coefficient.

[0395] Optionally, the processor 710 is further configured to:

[0396] The encoding result of the target parameter in the code stream is decoded to obtain the target parameter.

[0397] Optionally, the transform tree structure includes multiple layers of nodes, and the target parameters corresponding to multiple nodes in the layer where the node to be decoded is located are the same.

[0398] Optionally, the processor 710 is further configured to:

[0399] A target parameter corresponding to the node to be decoded is determined based on a reconstructed value of at least one target attribute transform coefficient, where the target attribute transform coefficient is a decoded attribute transform coefficient.

[0400] Optionally, the reconstructed value of the target attribute transform coefficient includes a reconstructed value of a third attribute transform coefficient corresponding to the first channel and a reconstructed value of a fourth attribute transform coefficient corresponding to the second channel;

[0401] The target parameter corresponding to the node to be decoded is positively correlated with a first value, the first value being the sum of first products corresponding to each target attribute transform coefficient in the at least one target attribute transform coefficient, the first product being the product of a reconstructed value of the third attribute transform coefficient corresponding to the target attribute transform coefficient and a reconstructed value of the corresponding fourth attribute transform coefficient; or

[0402] The target parameter corresponding to the node to be decoded is negatively correlated with a second value, the second value being the sum of the first square values ​​corresponding to each target attribute transformation coefficient in the at least one target attribute transformation coefficient, and the first square value being the square value of the reconstructed value of the third attribute transformation coefficient corresponding to the target attribute transformation coefficient.

[0403] Optionally, the at least one target attribute transformation coefficient is any one of the following:

[0404] A plurality of decoded attribute transformation coefficients of the layer where the node to be decoded is located;

[0405] Attribute transform coefficients of multiple decoded nodes.

[0406] Optionally, the target parameter corresponding to the node to be decoded is a ratio of the first value to the second value.

[0407] Optionally, the reconstructed value of the target attribute transform coefficient includes a reconstructed value of a third attribute transform coefficient corresponding to the first channel and a reconstructed value of a fourth attribute transform coefficient corresponding to the second channel, and the target parameter is:

[0408] Wherein, L is the number of layers where the node to be decoded is located, k indicates that the node to be decoded is the kth node in the L layer, s L,k is the target parameter corresponding to the node to be decoded; is the reconstructed value of the third attribute transformation coefficient corresponding to the target attribute transformation coefficient of the j-th node of the L layer; It is the reconstructed value of the fourth attribute transformation coefficient corresponding to the target attribute transformation coefficient of the j-th node of the L layer, where j is a positive integer greater than 0 and less than or equal to k.

[0409] Optionally, both the first channel and the second channel are chroma channels;

[0410] Or, the first channel is a chroma channel, and the second channel is a luminance channel;

[0411] Alternatively, the first channel is a luminance channel, and the second channel is a chrominance channel.

[0412] It can be understood that the implementation process of each implementation method mentioned in this embodiment can refer to the relevant description of the point cloud encoding processing method or the point cloud decoding processing method of the method embodiment, and achieve the same or corresponding technical effect. To avoid repetition, it will not be repeated here.

[0413] An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the various processes of the above-mentioned point cloud encoding processing method embodiment are implemented, or when the program or instruction is executed by a processor, the various processes of the above-mentioned point cloud decoding processing method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0414] The processor is the processor in the terminal described in the above embodiment. The readable storage medium includes a computer-readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk. In some examples, the readable storage medium may be a non-transitory readable storage medium.

[0415] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned point cloud encoding processing method embodiment, or to implement the various processes of the above-mentioned point cloud decoding processing method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0416] It should be understood that the chip mentioned in the embodiments of the present application may include a system-level chip (also referred to as a system chip, a chip system or a system-on-chip chip), and may also include an independent display chip, etc.

[0417] An embodiment of the present application further provides a computer program / program product, which is stored in a storage medium. The computer program / program product is executed by at least one processor to implement the various processes of the above-mentioned point cloud encoding processing method or point cloud decoding processing method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0418] An embodiment of the present application also provides a coding and decoding system, including: an encoding end device and a decoding end device, wherein the encoding end device can be used to execute the steps of the point cloud encoding processing method as described above, and the decoding end device can be used to execute the steps of the point cloud decoding processing method as described above.

[0419] It should be noted that, in this article, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the opposite order according to the functions involved. For example, the described method may be performed in an order different from that described, and various steps may also be added, omitted or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0420] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of a computer software product plus a necessary general-purpose hardware platform, or of course, by hardware. The computer software product is stored in a storage medium (such as ROM, RAM, magnetic disk, optical disk, etc.) and includes a number of instructions for enabling a terminal or network-side device to execute the methods described in each embodiment of the present application.

[0421] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of this application, ordinary technicians in this field can also make many forms of implementation methods without departing from the purpose of this application and the scope of protection of the claims. These implementation methods are all within the protection of this application.

Claims

1. A point cloud coding processing method, comprising: The encoding end performs a transformation process based on a transformation tree structure corresponding to the to-be-encoded point cloud to obtain an attribute transformation coefficient of a to-be-encoded node in the transformation tree structure, wherein the attribute transformation coefficient of the to-be-encoded node includes a first attribute transformation coefficient corresponding to a first channel and a second attribute transformation coefficient corresponding to a second channel, wherein the first channel and the second channel are different channels of the same color space; The encoding end performs predictive encoding processing on the second attribute transform coefficient based on the first attribute transform coefficient to obtain a residual attribute transform coefficient corresponding to the second attribute transform coefficient; The encoding end performs encoding processing on the residual attribute transformation coefficient to obtain a first encoding result; The code stream of the point cloud to be encoded includes the first encoding result.

2. The method according to claim 1, wherein: The encoder performs predictive coding processing on the second attribute transform coefficient based on the first attribute transform coefficient to obtain a residual attribute transform coefficient corresponding to the second attribute transform coefficient, including: The encoding end performs encoding processing on the first attribute transformation coefficient to obtain a second encoding result; The encoding end reconstructs the first attribute transform coefficient based on the second encoding result to obtain a reconstructed value of the first attribute transform coefficient; The encoding end performs predictive encoding processing on the second attribute transform coefficient based on the reconstructed value of the first attribute transform coefficient to obtain a residual attribute transform coefficient corresponding to the second attribute transform coefficient.

3. The method according to claim 2, wherein: The encoder performs predictive coding processing on the second attribute transform coefficient based on the reconstructed value of the first attribute transform coefficient to obtain a residual attribute transform coefficient corresponding to the second attribute transform coefficient, including: The encoder determines, based on the reconstructed value of the first attribute transform coefficient and a target parameter, a predicted value of the second attribute transform coefficient, wherein the target parameter is a correlation parameter between the first channel and the second channel; The encoding end obtains a residual attribute transform coefficient corresponding to the second attribute transform coefficient based on the predicted value of the second attribute transform coefficient and the second attribute transform coefficient.

4. The method according to claim 3, wherein: The transform tree structure includes multiple layers of nodes. Before the encoder determines the predicted value of the second attribute transform coefficient based on the reconstructed value of the first attribute transform coefficient and the target parameter, the method further includes: The encoding end determines the target parameter based on original attribute values ​​of multiple nodes in the layer where the node to be encoded is located; Among them, the target parameters corresponding to the multiple nodes in the layer where the node to be encoded is located are the same.

5. The method according to claim 4, wherein: The code stream of the point cloud to be encoded also includes the encoding result of the target parameter.

6. The method according to claim 3, wherein: Before the encoder determines the predicted value of the second attribute transform coefficient based on the reconstructed value of the first attribute transform coefficient and the target parameter, the method further includes: The encoding end reconstructs at least one target attribute transform coefficient to obtain the at least one target attribute a reconstructed value of a transform coefficient, wherein the target attribute transform coefficient is an encoded attribute transform coefficient; The encoding end determines the target parameter corresponding to the node to be encoded based on the reconstructed value of the at least one target attribute transformation coefficient.

7. The method according to claim 6, wherein: The reconstructed value of the target attribute transform coefficient includes a reconstructed value of a third attribute transform coefficient corresponding to the first channel and a reconstructed value of a fourth attribute transform coefficient corresponding to the second channel; The target parameter corresponding to the node to be encoded is positively correlated with a first value, the first value being the sum of first products corresponding to each target attribute transform coefficient in the at least one target attribute transform coefficient, the first product being the product of a reconstructed value of the third attribute transform coefficient corresponding to the target attribute transform coefficient and a reconstructed value of the corresponding fourth attribute transform coefficient; or The target parameter corresponding to the node to be encoded is negatively correlated with a second value, the second value is the sum of the first square values ​​corresponding to each target attribute transformation coefficient in the at least one target attribute transformation coefficient, and the first square value is the square value of the reconstructed value of the third attribute transformation coefficient corresponding to the target attribute transformation coefficient.

8. The method according to claim 6 or 7, wherein: The at least one target attribute transformation coefficient is any one of the following: A plurality of encoded attribute transformation coefficients of the layer where the node to be encoded is located; Attribute transform coefficients of multiple encoded nodes.

9. The method according to claim 7, wherein: The target parameter corresponding to the node to be encoded is the ratio of the first value to the second value.

10. The method according to any one of claims 6 to 9, wherein: The reconstructed value of the target attribute transform coefficient includes the reconstructed value of the third attribute transform coefficient corresponding to the first channel and the reconstructed value of the fourth attribute transform coefficient corresponding to the second channel, and the target parameter is: Wherein, L is the number of levels where the node to be encoded is located, k indicates that the node to be encoded is the kth node of the Lth layer, and s L,k is the target parameter corresponding to the node to be encoded; is the reconstructed value of the third attribute transformation coefficient corresponding to the target attribute transformation coefficient of the j-th node of the L layer; It is the reconstructed value of the fourth attribute transformation coefficient corresponding to the target attribute transformation coefficient of the j-th node of the L layer, where j is a positive integer greater than 0 and less than or equal to k.

11. The method according to any one of claims 1 to 10, wherein: The first channel and the second channel are both chrominance channels; Or, the first channel is a chroma channel, and the second channel is a luminance channel; Or, the first channel is a brightness channel, and the second channel is a chrominance channel.

12. A point cloud decoding processing method, comprising: The decoding end performs decoding processing on the first encoding result in the bitstream to obtain a reconstructed value of a residual attribute transformation coefficient, where the residual attribute transformation coefficient is a residual attribute transformation coefficient corresponding to the second attribute transformation coefficient corresponding to the second channel; The decoding end reconstructs the residual attribute transform coefficient based on the reconstructed value of the first attribute transform coefficient corresponding to the first channel. The reconstructed value of the first channel and the second channel are different channels of the same color space; The decoding end obtains the attribute transformation coefficient of the node to be decoded based on the reconstructed value of the second attribute transformation coefficient.

13. The method according to claim 12, wherein: The decoding end performs predictive decoding processing on the reconstructed value of the residual attribute transform coefficient based on the reconstructed value of the first attribute transform coefficient corresponding to the first channel to obtain the reconstructed value of the second attribute transform coefficient, including: The decoding end performs decoding processing on the second encoding result in the bitstream to obtain a reconstructed value of a first attribute transformation coefficient corresponding to the first channel; The decoding end performs predictive decoding processing on the reconstructed value of the residual attribute transform coefficient based on the reconstructed value of the first attribute transform coefficient to obtain the reconstructed value of the second attribute transform coefficient.

14. The method according to claim 13, wherein: The decoding end performs predictive decoding processing on the reconstructed value of the residual attribute transform coefficient based on the reconstructed value of the first attribute transform coefficient to obtain the reconstructed value of the second attribute transform coefficient, including: The decoding end determines a predicted value of the second attribute transform coefficient based on a reconstructed value of the first attribute transform coefficient and a target parameter, wherein the target parameter is a correlation parameter between the first channel and the second channel; The decoding end obtains the reconstructed value of the second attribute transform coefficient based on the predicted value of the second attribute transform coefficient and the reconstructed value of the residual attribute transform coefficient.

15. The method according to claim 14, wherein: Before the decoding end determines the predicted value of the second attribute transform coefficient based on the reconstructed value of the first attribute transform coefficient and the target parameter, the method further includes: The decoding end decodes the encoding result of the target parameter in the bitstream to obtain the target parameter.

16. The method according to claim 15, wherein: The transform tree structure includes multiple layers of nodes, and the target parameters corresponding to multiple nodes in the layer where the node to be decoded is located are the same.

17. The method according to claim 14, wherein: Before the decoding end determines the predicted value of the second attribute transform coefficient based on the reconstructed value of the first attribute transform coefficient and the target parameter, the method further includes: The decoding end determines a target parameter corresponding to the node to be decoded based on a reconstructed value of at least one target attribute transform coefficient, where the target attribute transform coefficient is a decoded attribute transform coefficient.

18. The method according to claim 17, wherein: The reconstructed value of the target attribute transform coefficient includes a reconstructed value of a third attribute transform coefficient corresponding to the first channel and a reconstructed value of a fourth attribute transform coefficient corresponding to the second channel; The target parameter corresponding to the node to be decoded is positively correlated with a first value, the first value being the sum of first products corresponding to each target attribute transform coefficient in the at least one target attribute transform coefficient, the first product being the product of a reconstructed value of the third attribute transform coefficient corresponding to the target attribute transform coefficient and a reconstructed value of the corresponding fourth attribute transform coefficient; or The target parameter corresponding to the node to be decoded is negatively correlated with a second value, the second value is the sum of the first square values ​​corresponding to each target attribute transformation coefficient in the at least one target attribute transformation coefficient, and the first square value is the target attribute transformation coefficient. The square value of the reconstructed value of the third attribute transformation coefficient corresponding to the target attribute transformation coefficient.

19. The method according to claim 17 or 18, wherein: The at least one target attribute transformation coefficient is any one of the following: A plurality of decoded attribute transformation coefficients of the layer where the node to be decoded is located; Attribute transform coefficients of multiple decoded nodes.

20. The method according to claim 18, wherein: The target parameter corresponding to the node to be decoded is the ratio of the first value to the second value.

21. The method according to any one of claims 17 to 20, wherein: The reconstructed value of the target attribute transform coefficient includes the reconstructed value of the third attribute transform coefficient corresponding to the first channel and the reconstructed value of the fourth attribute transform coefficient corresponding to the second channel, and the target parameter is: Where L is the number of levels where the node to be decoded is located, k indicates that the node to be decoded is the kth node of the Lth layer, and s L,k is the target parameter corresponding to the node to be decoded; is the reconstructed value of the third attribute transformation coefficient corresponding to the target attribute transformation coefficient of the j-th node of the L layer; It is the reconstructed value of the fourth attribute transformation coefficient corresponding to the target attribute transformation coefficient of the j-th node of the L layer, where j is a positive integer greater than 0 and less than or equal to k.

22. The method according to any one of claims 12 to 21, wherein: The first channel and the second channel are both chrominance channels; Or, the first channel is a chroma channel, and the second channel is a luminance channel; Or, the first channel is a brightness channel, and the second channel is a chrominance channel.

23. A point cloud coding processing device, comprising: A transformation module, configured to perform transformation processing based on a transformation tree structure corresponding to a point cloud to be encoded, and obtain attribute transformation coefficients of nodes to be encoded in the transformation tree structure, wherein the attribute transformation coefficients of nodes to be encoded include first attribute transformation coefficients corresponding to a first channel and second attribute transformation coefficients corresponding to a second channel, wherein the first channel and the second channel are different channels of the same color space; A first encoding module, configured to perform predictive encoding processing on the second attribute transform coefficient based on the first attribute transform coefficient to obtain a residual attribute transform coefficient corresponding to the second attribute transform coefficient; A second encoding module, used for encoding the residual attribute transformation coefficient to obtain a first encoding result; The code stream of the point cloud to be encoded includes the first encoding result.

24. A point cloud decoding processing device, comprising: A first decoding module is used to decode the first encoding result in the bitstream to obtain a reconstructed value of a residual attribute transformation coefficient, where the residual attribute transformation coefficient is a residual attribute transformation coefficient corresponding to a second attribute transformation coefficient corresponding to the second channel; The second decoding module is used to perform predictive decoding processing on the reconstructed value of the residual attribute transformation coefficient based on the reconstructed value of the first attribute transformation coefficient corresponding to the first channel to obtain the reconstructed value of the second attribute transformation coefficient. The channel and the second channel are different channels of the same color space; The acquisition module is used to acquire the attribute transformation coefficient of the node to be decoded based on the reconstructed value of the second attribute transformation coefficient.

25. An electronic device, comprising a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the steps of the point cloud encoding processing method as described in any one of claims 1 to 11; or, the program or instruction, when executed by the processor, implements the steps of the point cloud decoding processing method as described in any one of claims 12 to 22.

26. A chip, comprising a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement the steps of the point cloud encoding processing method as described in any one of claims 1 to 11, or to implement the steps of the point cloud decoding processing method as described in any one of claims 12 to 22.

27. A readable storage medium storing a program or instruction, wherein the program or instruction, when executed by a processor, implements the steps of the point cloud encoding processing method as described in any one of claims 1 to 11, or, when executed by a processor, implements the steps of the point cloud decoding processing method as described in any one of claims 12 to 22.

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