WO2023098802A1 - Procédé de codage d'attribut de nuage de points, procédé de décodage d'attribut de nuage de points et terminal - Google Patents

Procédé de codage d'attribut de nuage de points, procédé de décodage d'attribut de nuage de points et terminal Download PDF

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WO2023098802A1
WO2023098802A1 PCT/CN2022/135874 CN2022135874W WO2023098802A1 WO 2023098802 A1 WO2023098802 A1 WO 2023098802A1 CN 2022135874 W CN2022135874 W CN 2022135874W WO 2023098802 A1 WO2023098802 A1 WO 2023098802A1
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point
attribute
encoded
decoded
prediction
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PCT/CN2022/135874
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Chinese (zh)
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张伟
杨付正
张可
吕卓逸
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维沃移动通信有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T9/00—Image coding
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N13/00—Stereoscopic video systems; Multi-view video systems; Details thereof
    • H04N13/10—Processing, recording or transmission of stereoscopic or multi-view image signals
    • H04N13/106—Processing image signals
    • H04N13/161—Encoding, multiplexing or demultiplexing different image signal components
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/102—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or selection affected or controlled by the adaptive coding
    • H04N19/124—Quantisation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/102—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or selection affected or controlled by the adaptive coding
    • H04N19/13—Adaptive entropy coding, e.g. adaptive variable length coding [AVLC] or context adaptive binary arithmetic coding [CABAC]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/50—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding
    • H04N19/503—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding involving temporal prediction
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/50—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding
    • H04N19/597—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding specially adapted for multi-view video sequence encoding
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/70—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by syntax aspects related to video coding, e.g. related to compression standards
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/85—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using pre-processing or post-processing specially adapted for video compression
    • H04N19/88—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using pre-processing or post-processing specially adapted for video compression involving rearrangement of data among different coding units, e.g. shuffling, interleaving, scrambling or permutation of pixel data or permutation of transform coefficient data among different blocks

Definitions

  • the present application belongs to the technical field of point cloud processing, and in particular relates to a point cloud attribute encoding method, a point cloud attribute decoding method and a terminal.
  • a point cloud is a set of discrete point sets randomly distributed in space that express the spatial structure and surface properties of a three-dimensional object or scene.
  • attribute encoding is performed on the point cloud, wherein the attribute encoding of the point cloud includes attribute predictive encoding.
  • the geometric information of the point cloud needs to be reordered, which disrupts the geometric coding order of the point cloud after geometric coding. It is necessary to determine the nearest neighbor point corresponding to the point to be coded in a more complicated way. Attribute predictive coding reduces coding efficiency.
  • the attribute decoding process of point cloud corresponds to the attribute encoding process of point cloud, and the reordering of point cloud during the attribute decoding process also reduces the attribute decoding efficiency of point cloud.
  • the embodiment of the present application provides a point cloud attribute encoding method, a point cloud attribute decoding method and a terminal, which can solve the problem that in the process of attribute prediction encoding, the operation of reordering the geometric information of the point cloud reduces the attribute encoding and decoding efficiency of the point cloud technical issues.
  • a method for encoding point cloud attributes comprising:
  • the encoding end obtains the point cloud to be encoded
  • the encoding end determines at least one prediction point corresponding to the point to be encoded based on the position of the point to be encoded in the point cloud to be encoded;
  • the encoding end determines the predicted attribute value of the point cloud to be encoded based on the reconstructed attribute value of the predicted point and the weight value corresponding to the predicted point;
  • the coding end performs quantization and entropy coding on the first attribute prediction residual to obtain a target code stream, and the first attribute prediction residual is determined based on the real attribute value of the point to be encoded and the predicted attribute value.
  • a device for encoding point cloud attributes including:
  • the first obtaining module is used to obtain the point cloud to be coded
  • a first determination module configured to determine at least one prediction point corresponding to the point to be encoded based on the position of each point to be encoded in the point cloud to be encoded;
  • the second determination module is configured to determine the predicted attribute value of the point cloud to be encoded based on the reconstructed attribute value of the predicted point and the weight value corresponding to the predicted point;
  • An encoding module configured to perform quantization and entropy encoding on a first attribute prediction residual to obtain a target code stream, where the first attribute prediction residual is determined based on the actual attribute value of the point to be encoded and the predicted attribute value.
  • a method for decoding point cloud attributes comprising:
  • the decoding end obtains the target code stream, and the target code stream includes the point cloud to be decoded;
  • the decoding end determines at least one prediction point corresponding to the point to be decoded based on the position of the point to be decoded in the point cloud to be decoded;
  • the decoding end obtains the reconstructed attribute value of the point to be decoded based on the second attribute prediction residual of the point to be decoded, the reconstructed attribute value of the predicted point, and the weight value corresponding to the predicted point.
  • a point cloud attribute decoding device including:
  • the second acquisition module is used to acquire a target code stream, the target code stream includes a point cloud to be decoded;
  • a third determining module configured to determine at least one prediction point corresponding to the point to be decoded based on the position of the point to be decoded in the point cloud to be decoded;
  • the decoding module is configured to obtain the reconstructed attribute value of the point to be decoded based on the second attribute prediction residual of the point to be decoded, the reconstructed attribute value of the predicted point, and the weight value corresponding to the predicted point.
  • a communication device in a fifth aspect, includes a processor and a memory, the memory stores programs or instructions that can run on the processor, and the programs or instructions are implemented when executed by the processor.
  • a sixth aspect provides a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps of the point cloud attribute encoding method as described in the first aspect are implemented , or realize the steps of the point cloud attribute decoding method as described in the third aspect.
  • a chip in a seventh aspect, includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is used to run programs or instructions to achieve the points described in the first aspect A cloud attribute encoding method, or the steps for realizing the point cloud attribute decoding method as described in the third aspect.
  • a computer program product is provided, the computer program product is stored in a storage medium, and the computer program product is executed by at least one processor to implement the point cloud attribute encoding method as described in the first aspect steps, or realize the steps of the point cloud attribute decoding method as described in the third aspect.
  • a ninth aspect provides an electronic device configured to implement the steps of the method described in the first aspect.
  • an electronic device configured to implement the steps of the method described in the third aspect.
  • the point cloud to be encoded is obtained, and at least one predicted point corresponding to the point to be encoded is determined based on the position of the point to be encoded in the point cloud to be encoded; based on the reconstruction attribute value of the predicted point and the weight value corresponding to the predicted point, Determine the predicted attribute value of the point cloud to be encoded; perform quantization and entropy encoding on the first attribute prediction residual to obtain the target code stream.
  • point cloud attribute encoding process no reordering of the point cloud to be encoded is involved, so the geometric encoding order of the point cloud after geometric encoding will not be disrupted, and the nearest neighbor point of the point to be encoded can be determined based on the geometric encoding order. Attribute predictive coding is performed to improve coding efficiency.
  • Fig. 1 is a schematic diagram of the framework of a point cloud AVS point cloud attribute encoding device
  • Fig. 2 is a schematic diagram of the framework of a point cloud AVS point cloud attribute decoding device
  • Fig. 3 is the flow chart of the point cloud attribute encoding method that the embodiment of the present application provides;
  • FIG. 4 is an application scenario diagram of the point cloud attribute encoding method provided by the embodiment of the present application.
  • Fig. 5 is a flow chart of the point cloud attribute decoding method provided by the embodiment of the present application.
  • FIG. 6 is a structural diagram of a point cloud attribute encoding device provided by an embodiment of the present application.
  • FIG. 7 is a structural diagram of a point cloud attribute decoding device provided by an embodiment of the present application.
  • FIG. 8 is a structural diagram of a communication device provided by an embodiment of the present application.
  • FIG. 9 is a schematic diagram of a hardware structure of a terminal provided by an embodiment of the present application.
  • first, second and the like in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific sequence or sequence. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the application are capable of operation in sequences other than those illustrated or described herein and that "first" and “second” distinguish objects. It is usually one category, and the number of objects is not limited. For example, there may be one or more first objects.
  • “and/or” in the description and claims means at least one of the connected objects, and the character “/” generally means that the related objects are an "or” relationship.
  • Both the point cloud attribute encoding device corresponding to the point cloud attribute encoding method in the embodiment of the present application and the point cloud attribute decoding device corresponding to the point cloud attribute decoding method can be terminals, and the terminal can also be called terminal equipment or user equipment (User Equipment).
  • the terminal can be a mobile phone, a tablet computer (Tablet Personal Computer), a laptop computer (Laptop Computer) or a notebook computer, a personal digital assistant (Personal Digital Assistant, PDA), a handheld computer, a netbook, a super mobile personal Computer (ultra-mobile personal computer, UMPC), mobile Internet device (Mobile Internet Device, MID), augmented reality (augmented reality, AR) / virtual reality (virtual reality, VR) equipment, robot, wearable device (Wearable Device ) or vehicle equipment (Vehicle User Equipment, VUE), pedestrian terminal (Pedestrian User Equipment, PUE), smart home (home equipment with wireless communication functions, such as refrigerators, TVs, washing machines or furniture, etc.), game consoles, personal computers ( personal computer, PC), teller machines or self-service machines 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 anklet
  • Fig. 1 As shown in Fig. 1, at present, in the technical standard of digital audio and video codec, the geometric information and Attribute information is encoded separately.
  • coordinate transformation is performed on the geometric information so that all point clouds are contained in a bounding box, and then the coordinates are quantized.
  • Quantization mainly plays the role of scaling. Since quantization will round the geometric coordinates, the geometric information of some points will be the same, which is called duplicate points. It is determined whether to remove duplicate points according to the parameters. Quantization and removal of duplicate points are two steps. Also known as the voxelization process.
  • the bounding box is divided into 8 sub-cubes, and the non-empty sub-cubes continue to be divided until the unit cube with leaf nodes of 1x1x1 is obtained.
  • the number of points in the point is encoded to generate a binary code stream.
  • the points to be encoded need to store the occupancy information of neighbor nodes to perform predictive coding for the occupancy information of the points to be encoded. In this way, for the points to be encoded that are close to the leaf nodes , need to store a large amount of occupancy information, occupying a large amount of memory space.
  • Attribute coding is mainly aimed at color and reflectance information. First, judge whether to perform color space conversion according to the parameters. If color space conversion is performed, the color information is converted from the red green blue (RGB) color space to the brightness color (YUV) color space. Then, the geometrically reconstructed point cloud is recolored with the original point cloud so that the unencoded attribute information corresponds to the reconstructed geometric information.
  • RGB red green blue
  • YUV brightness color
  • the nearest neighbor of the point to be predicted is searched using the geometric spatial relationship, and the predicted point is predicted by using the reconstruction attribute value of the found neighbor to obtain Predict the attribute value, then make a difference between the real attribute value and the predicted attribute value to obtain the prediction residual, and finally quantize and encode the prediction residual to generate a binary code stream.
  • the decoding process in the digital audio and video codec technical standard corresponds to the above encoding process.
  • the frame of the AVS point cloud attribute decoding device is shown in FIG. 2 .
  • an embodiment of the present application provides a point cloud attribute encoding method.
  • the point cloud attribute encoding method provided by the embodiment of the present application will be described in detail below through some embodiments and application scenarios with reference to the accompanying drawings.
  • FIG. 3 is a flow chart of the point cloud attribute encoding method provided by the present application.
  • the point cloud attribute encoding method provided in this embodiment includes the following steps:
  • the point cloud to be encoded in this step is a point cloud that has undergone geometric encoding and has a geometric encoding order; the point cloud to be encoded in this step is a point cloud that has not been reordered, and the point cloud to be encoded in this step is a point cloud that has undergone Point cloud recoloring and color space conversion.
  • the above-mentioned recoloring refers to recoloring the geometrically reconstructed point cloud by using the original point cloud, so that the unencoded attribute information corresponds to the reconstructed geometric information, and the recolored point cloud is obtained;
  • the above-mentioned color space conversion refers to the transformation of the point cloud Color information is converted from RGB space to YUV space.
  • this step when most of the coded blocks in the point cloud to be coded are coded using predictive tree coding, since in predictive tree coding, the front and back nodes in the same coded block are usually the nearest neighbor points, it can be based on The characteristics of the above-mentioned prediction tree coding, according to the position of the point to be coded in the point cloud to be coded, determine the prediction point corresponding to the point to be coded, the above-mentioned prediction point is the nearest neighbor point, and the prediction point is the coded point.
  • the number of prediction points corresponding to a point to be encoded is greater than or equal to 1.
  • the preset attribute information corresponding to the point to be encoded may be directly encoded.
  • the above reconstructed attribute values are attribute values corresponding to encoded prediction points.
  • the reconstructed attribute value of the predicted point can be read directly, and the weight value corresponding to the predicted point can be calculated.
  • the weight value corresponding to the prediction point can be calculated.
  • correlation calculation is performed on the above-mentioned reconstructed attribute value and weight value to obtain the predicted predicted value corresponding to the point cloud to be encoded.
  • the first attribute prediction residual is determined based on the real attribute value of the point to be encoded and the predicted attribute value
  • entropy coding is a coding method that does not lose any information according to the principle of entropy during the coding process.
  • the above-mentioned entropy coding may be Shannon coding, Huffman coding or other types of coding, which are not specifically limited in this embodiment.
  • the above-mentioned target code stream is a binary code stream.
  • the point cloud to be encoded is obtained, and at least one predicted point corresponding to the point to be encoded is determined based on the position of the point to be encoded in the point cloud to be encoded; based on the reconstruction attribute value of the predicted point and the weight value corresponding to the predicted point, Determine the predicted attribute value corresponding to the point cloud to be encoded; perform quantization and entropy encoding on the first attribute prediction residual to obtain the target code stream.
  • point cloud attribute encoding process no reordering of the point cloud to be encoded is involved, so the geometric encoding order of the point cloud after geometric encoding will not be disrupted, and the nearest neighbor point of the point to be encoded can be determined based on the geometric encoding order. Attribute predictive coding is performed to improve coding efficiency.
  • the attribute compression efficiency of the point cloud attribute encoding provided by the embodiment of the present application is higher than the attribute compression efficiency of the point cloud attribute encoding of the traditional point cloud attribute encoding.
  • Table 1 For easy understanding of the technical effects produced by this application, please refer to Table 1.
  • “bpip ratio” in Table 1 is also called the average bit ratio of coding points. It should be understood that under the condition of lossless coding, the lower the value of the average bit ratio of coding points, it shows the attribute compression efficiency of the coding method. The higher the value, the better the encoding performance.
  • the average bit ratio of code points includes overall ratio, geometric information ratio and attribute information ratio, where "Total” in Table 1 can also be expressed as overall ratio, “Geometry” can also be expressed as geometric information ratio, and “Colour” can also be expressed as can be expressed as an attribute information ratio.
  • Code stream 1 and code stream 2 shown in Table 1 are code streams obtained by applying the point cloud attribute encoding provided by the embodiment of the present application, code stream 3, code stream 4, code stream 5, code stream 6 and code stream shown in Table 1
  • Code stream 7 is a code stream obtained by traditional point cloud attribute encoding.
  • the attribute information ratio corresponding to the code stream obtained by the point cloud attribute encoding provided by the embodiment of the present application is lower than the attribute information ratio of the code stream obtained by the traditional point cloud attribute encoding.
  • the method includes:
  • the target parameter in the attribute header information of the point cloud to be encoded is set as a preset value.
  • the point cloud will be divided into octrees to obtain multiple encoding blocks, and it is judged whether the octree level corresponding to the point cloud adopts macroblock encoding.
  • macroblock encoding is adopted, Macroblock coding is adopted for the point cloud, and the number of coding blocks corresponding to the point cloud is determined as the number of macroblocks using the macroblock coding method for the point cloud to be coded.
  • the number of macroblocks for which the above-mentioned point cloud to be encoded adopts the macroblock encoding method may be determined as the second value.
  • the point cloud includes the parameter lcu_node_depth.
  • the octree level octree_node_depth corresponding to the point cloud is obtained.
  • the macro block is used to determine the octree level corresponding to the point cloud encoding method.
  • the point cloud is coded by macroblocks, it is judged whether each coded block continues to use octree coding or predictive tree coding, and the number of macroblocks in the point cloud coded by the predictive tree method is determined as the first a value. That is to say, the number of coded blocks in the point cloud coded by the prediction tree method is determined as the first value.
  • the point cloud includes the high density coefficient geomTreeDensityHigh, the low density coefficient geomTreeDensityLow and the minimum point limit parameter geomPointTh.
  • the method of judging whether the coding block is encoded by the prediction tree method is to calculate the density of the coding block, if the density of the coding block is greater than the value corresponding to the low density coefficient and smaller than the value corresponding to the high density coefficient, and the coding point included in the coding block is If the number is greater than the value corresponding to the minimum number of points limit parameter, it can be determined that the coding block is coded using the prediction tree method.
  • the quotient of the first value and the second value is determined as the first proportional value.
  • the condition for enabling macroblock coding is met. Since the point cloud is divided into 8 coding blocks at the second level of octree division, the second value can be determined as 8. If 4 of the above-mentioned 8 coding blocks adopt predictive tree coding, then it can be determined that the first value is 4, and the first ratio value is 0.5.
  • a threshold is set, and when the first ratio value is greater than or equal to the threshold, it means that most of the coding blocks in the point cloud use prediction tree coding, and the target in the attribute header information of the point cloud to be coded Parameters are set to default values.
  • the aforementioned threshold may be an empirical threshold, or may be a threshold set by a user.
  • the above preset value is used to indicate that the point cloud to be encoded satisfies the first condition, that is, most of the encoded blocks in the point cloud to be encoded use prediction tree encoding.
  • the predicted point corresponding to the point to be encoded is determined based on the position of the point to be encoded in the point cloud to be encoded.
  • a target parameter can be set in the Attribute Brick Header (ABH) of the point cloud to be encoded, and the target parameter can be expressed as use_pred_tree_code_attr, wherein the above preset value can be 1. It should be understood that when the assignment of the target parameter is not a preset value, for example, when the assignment of the target parameter is 0, it means that most of the coding blocks in the point cloud to be coded do not use prediction tree coding.
  • ABS Attribute Brick Header
  • the determining at least one prediction point corresponding to the point to be encoded based on the position of each point to be encoded in the point cloud to be encoded includes:
  • the preceding and following coding points in the same coding block are usually the nearest neighbor points.
  • the position of the point to be encoded can be used as the search center, the encoded point can be searched within the preset range of the point to be encoded, and the searched encoded point can be determined as the predicted point.
  • the aforementioned preset range refers to the coded range corresponding to the point to be coded.
  • the coding points in Figure 4 form a geometric coding sequence based on the single-chain structure of the KD tree, where the P0 point is the point to be coded, and P1, P2 and P3 3 coded points, determine the above 3 coded points as prediction points.
  • the coding points in the coding block are not reordered based on the coding block, but based on the geometric coding order of the coding block to determine the prediction points to improve coding efficiency.
  • the determining the predicted attribute value of the point to be encoded according to the reconstructed attribute value of the predicted point and the weight value corresponding to the predicted point includes:
  • the product result of the reconstruction attribute value of the prediction point and the weight value corresponding to other prediction points is determined as the first target value corresponding to the prediction point;
  • the sum of the first target values corresponding to each predicted point is determined as the predicted attribute value of the point to be encoded.
  • the weight value corresponding to each prediction point may be determined based on the geometric spatial positional relationship between the point to be encoded and each prediction point.
  • the weight value corresponding to each prediction point may be determined based on the geometric spatial positional relationship between the point to be encoded and each prediction point.
  • the reconstruction attribute value of the prediction point is multiplied by the weight values corresponding to other prediction points, and the obtained multiplication result is used as the first target value corresponding to the prediction point. Further, the first target values of all predicted points corresponding to the point to be encoded are accumulated to obtain the predicted attribute value of the point to be encoded.
  • the implementation scenario shown in FIG. 4 is taken as an example for description.
  • the point P0 to be encoded corresponds to three prediction points P1, P2 and P3, please refer to the following formula:
  • predAttr W2*W3*Attr1+W1*W3*Attr2+W1*W2*Attr3
  • predAttr is the predicted attribute value of the point to be encoded, that is, the predicted attribute value of P0;
  • W1, W2, and W3 are the weight values corresponding to the predicted point, where W1 is the weight value corresponding to P1, and W2 is the weight value corresponding to P2.
  • W3 is the weight value corresponding to P3;
  • Attr1, Attr2 and Attr3 are the reconstructed attribute values corresponding to the predicted points, among which, Attr1 is the reconstructed attribute value corresponding to P1, Attr 2 is the reconstructed attribute value corresponding to P2, and Attr 3 is the reconstructed attribute value corresponding to P3 attribute value.
  • the predicted attribute value corresponding to the point to be encoded P0 can be obtained through the above formula.
  • the determining the weight value corresponding to each prediction point based on the geometric space position relationship between the point to be encoded and each prediction point includes:
  • Numerical conversion is performed on the distance value to obtain a weight value corresponding to the predicted point.
  • the distance value between the predicted point and the point to be coded is calculated, and in an optional embodiment, the distance value between the predicted point and the point to be coded may be obtained directly.
  • the length of a space vector between the prediction point and the point to be encoded may be calculated, and then based on the length of the space vector, the weight value corresponding to the prediction point is obtained.
  • the essence of performing numerical conversion on the distance value above is to determine the first norm corresponding to the distance value as the weight value corresponding to the prediction point, or determine the second norm corresponding to the distance value as the weight value corresponding to the prediction point, Alternatively, the weight value corresponding to the prediction point may be determined in other numerical representations corresponding to the distance value.
  • FIG. 5 is a flow chart of the point cloud attribute decoding method provided by the present application.
  • the point cloud attribute decoding method provided in this embodiment includes the following steps:
  • the target code stream is directly sent to the point cloud attribute decoding device, and the above target code stream includes the point cloud to be decoded.
  • the number of prediction points corresponding to a point to be decoded is greater than or equal to 1.
  • the preset attribute information corresponding to the point to be decoded may be directly decoded.
  • the decoder performs inverse quantization processing on the prediction residual of the first attribute in the target code stream to obtain the prediction residual of the second attribute.
  • the first attribute prediction residual of the point to be decoded and the reconstructed attribute value of the prediction point may be read directly, and the weight value corresponding to the prediction point is calculated.
  • the weight value corresponding to the prediction point please refer to the subsequent embodiments.
  • a correlation calculation is performed on the prediction residual of the second attribute, the reconstructed attribute value and the weight value to obtain the reconstructed attribute value corresponding to the point cloud to be decoded.
  • the target code stream is obtained, and the target code stream includes the point cloud to be decoded, and at least one prediction point corresponding to the point to be decoded is determined based on the position of the point to be decoded in the point cloud to be decoded; based on the second
  • the attribute prediction residual, the reconstructed attribute value of the predicted point, and the weight value corresponding to the predicted point are used to obtain the reconstructed attribute value of the point to be decoded.
  • the method includes:
  • the point cloud to be decoded satisfies a first condition.
  • the point cloud attribute encoding device if the point cloud to be encoded satisfies the first condition, the point cloud attribute encoding device will set the target parameter in the attribute header information of the point cloud to be encoded as a preset value. In this way, during the decoding process, the point cloud attribute decoding device can directly read the target parameter in the attribute header information of the point cloud to be decoded, and if the value corresponding to the target parameter is a preset value, it can directly determine that the point cloud to be decoded satisfies the first One condition.
  • the determining at least one prediction point corresponding to the point to be decoded based on the position of each point to be decoded in the point cloud to be decoded includes:
  • the preceding and following decoding points in the same decoding block are usually the nearest neighbor points.
  • the position of the point to be decoded can be used as the search center, the decoded point can be searched within the preset range of the point to be decoded, and the searched decoded point can be determined as the prediction point.
  • the aforementioned preset range refers to the decoded range corresponding to the point to be decoded.
  • the reconstruction attribute value corresponding to the point cloud to be decoded is obtained based on the second attribute prediction residual of the point to be decoded, the reconstruction attribute value of the prediction point and the weight value corresponding to the prediction point include:
  • a sum value result between the second attribute prediction residual and the predicted attribute value is determined as the reconstructed attribute value.
  • the reconstructed attribute value of the above prediction point is the attribute value corresponding to the decoded prediction point.
  • the weight value of the above prediction point is related to the geometric space position relationship between the prediction point and the point to be decoded. Specifically, how to determine the weight value corresponding to the prediction point For the technical solution, please refer to the following examples.
  • the predicted attribute value of the point to be decoded is determined according to the reconstructed attribute value and weight value, and the predicted attribute value is the attribute value obtained by performing attribute prediction decoding on the point to be decoded using the predicted point.
  • An optional implementation manner is to read the second attribute prediction residual of the point to be decoded, and use the sum of the second attribute prediction residual and the predicted attribute value as the reconstructed attribute value.
  • the determining the predicted attribute value of the point to be decoded according to the reconstructed attribute value of the predicted point and the weight value corresponding to the predicted point includes:
  • the product result of the reconstruction attribute value of the prediction point and the weight value corresponding to other prediction points is determined as the second target value corresponding to the prediction point;
  • the sum of the second target values corresponding to each predicted point is determined as the predicted attribute value of the point to be decoded.
  • the weight value corresponding to each prediction point may be determined based on the geometric spatial position relationship between the point to be decoded and each prediction point.
  • the weight value corresponding to each prediction point may be determined based on the geometric spatial position relationship between the point to be decoded and each prediction point.
  • the reconstruction attribute value of the prediction point is multiplied by the weight values corresponding to other prediction points, and the obtained multiplication result is used as the second target value corresponding to the prediction point. Further, the second target values of all prediction points corresponding to the point to be decoded are accumulated to obtain the predicted attribute value of the point to be decoded.
  • the determining the weight value corresponding to each prediction point based on the geometric spatial position relationship between the point to be decoded and each prediction point includes:
  • Numerical conversion is performed on the distance value to obtain a weight value corresponding to the predicted point.
  • For a predicted point calculate the distance between the predicted point and the point to be decoded. After obtaining the distance value between the prediction point and the point to be decoded, the distance value is numerically converted, and then the first norm corresponding to the distance value can be determined as the weight value corresponding to the prediction point, or the second norm corresponding to the distance value The number is determined as the weight value corresponding to the prediction point, or other numerical forms corresponding to the distance value are determined as the weight value corresponding to the prediction point.
  • the length of the space vector between the prediction point and the point to be decoded may be calculated, and based on the length, the weight value corresponding to the prediction point is obtained.
  • the point cloud attribute encoding method provided in the embodiment of the present application may be executed by a point cloud attribute encoding device.
  • the point cloud attribute encoding device provided in the embodiment of the present application is described by taking the point cloud attribute encoding device executing the point cloud attribute encoding method as an example.
  • the point cloud attribute encoding device 600 includes:
  • the first obtaining module 601 is used to obtain the point cloud to be coded
  • the first determination module 602 is configured to determine at least one prediction point corresponding to the point to be encoded based on the position of the point to be encoded in the point cloud to be encoded;
  • the second determination module 603 is configured to determine the predicted attribute value of the point to be encoded based on the reconstructed attribute value of the predicted point and the weight value corresponding to the predicted point;
  • the encoding module 604 is configured to perform quantization and entropy encoding on the prediction residual of the first attribute to obtain a target code stream.
  • the point cloud attribute encoding device 600 also includes:
  • a first determining unit configured to determine a first proportional value
  • the second determining unit is configured to set the target parameter in the attribute header information of the point cloud to be encoded as a preset value when the first ratio value is greater than or equal to a preset threshold.
  • the first determining module 602 is specifically configured to:
  • the second determining module 603 is also specifically configured to:
  • the product result of the reconstruction attribute value of the prediction point and the weight value corresponding to other prediction points is determined as the first target value corresponding to the prediction point;
  • the sum of the first target values corresponding to each predicted point is determined as the predicted attribute value of the point to be encoded.
  • the second determining module 603 is also specifically configured to:
  • Numerical conversion is performed on the distance value to obtain a weight value corresponding to the predicted point.
  • the point cloud to be encoded is obtained, and at least one predicted point corresponding to the point to be encoded is determined based on the position of the point to be encoded in the point cloud to be encoded; based on the reconstruction attribute value of the predicted point and the weight value corresponding to the predicted point, Determine the predicted attribute value of the point cloud to be encoded; perform quantization and entropy encoding on the first attribute prediction residual to obtain the target code stream.
  • point cloud attribute encoding process no reordering of the point cloud to be encoded is involved, so the geometric encoding order of the point cloud after geometric encoding will not be disrupted, and the nearest neighbor point of the point to be encoded can be determined based on the geometric encoding order. Attribute predictive coding is performed to improve coding efficiency.
  • the point cloud attribute encoding device provided by the embodiment of the present application can realize each process realized by the method embodiment in FIG. 3 and achieve the same technical effect. To avoid repetition, details are not repeated here.
  • the point cloud attribute decoding method provided in the embodiment of the present application may be executed by a point cloud attribute decoding device.
  • the point cloud attribute decoding device provided in the embodiment of the present application is described by taking the point cloud attribute decoding device executing the point cloud attribute decoding method as an example.
  • the point cloud attribute decoding device 700 includes:
  • the second obtaining module 701 is used to obtain the target code stream
  • the third determination module 702 is configured to determine at least one prediction point corresponding to the point to be decoded based on the position of the point to be decoded in the point cloud to be decoded;
  • the decoding module 703 is configured to obtain the reconstructed attribute value of the point to be encoded based on the second attribute prediction residual of the point to be decoded, the reconstructed attribute value of the predicted point, and the weight value corresponding to the predicted point.
  • the point cloud attribute decoding device 700 also includes:
  • An acquisition unit configured to acquire the target parameters in the attribute header information of the point cloud to be decoded
  • the third determination unit is configured to determine that the point cloud to be decoded satisfies the first condition when the value corresponding to the target parameter is a preset value.
  • the third determining module 702 is specifically configured to:
  • the decoding module 703 is specifically configured to:
  • a sum value result between the second attribute prediction residual and the predicted attribute value is determined as the reconstructed attribute value.
  • the decoding module 703 is specifically configured to:
  • the product result of the reconstruction attribute value of the prediction point and the weight value corresponding to other prediction points is determined as the second target value corresponding to the prediction point;
  • the sum of the second target values corresponding to each predicted point is determined as the predicted attribute value of the point to be decoded.
  • the decoding module 703 is specifically configured to:
  • Numerical conversion is performed on the distance value to obtain a weight value corresponding to the predicted point.
  • the target code stream is obtained, and the target code stream includes the point cloud to be decoded, and at least one prediction point corresponding to the point to be decoded is determined based on the position of the point to be decoded in the point cloud to be decoded; based on the second
  • the attribute prediction residual, the reconstructed attribute value of the predicted point, and the weight value corresponding to the predicted point are used to obtain the reconstructed attribute value of the point to be decoded.
  • the device for encoding point cloud attributes and the device for decoding point cloud attributes in the embodiments of the present application may be electronic equipment, or components in electronic equipment, such as integrated circuits or chips.
  • the electronic device may be a terminal, or other devices other than the terminal.
  • the terminal may include but not limited to the types of terminals listed above, and other devices may be servers, network attached storage (Network Attached Storage, NAS), etc., which are not specifically limited in this embodiment of the present application.
  • NAS Network Attached Storage
  • the point cloud attribute encoding device provided by the embodiment of the present application can realize each process realized by the method embodiment in FIG. 3 and achieve the same technical effect. To avoid repetition, details are not repeated here.
  • the point cloud attribute decoding device provided by the embodiment of the present application can realize each process realized by the method embodiment in FIG. 5 and achieve the same technical effect. To avoid repetition, details are not repeated here.
  • the embodiment of the present application also provides a communication device 800, including a processor 801 and a memory 802, and the memory 802 stores programs or instructions that can run on the processor 801, such as , when the communication device 800 is a terminal, when the program or instruction is executed by the processor 801, each step of the above-mentioned point cloud attribute encoding method embodiment can be realized, and the same technical effect can be achieved, or the above-mentioned point cloud attribute decoding method embodiment can be realized each step, and can achieve the same technical effect.
  • the embodiment of the present application also provides a terminal, including a processor and a communication interface, and the processor is configured to perform the following operations:
  • Quantization and entropy coding are performed on the prediction residual of the first attribute to obtain a target code stream.
  • the processor is used to:
  • the reconstructed attribute value of the predicted point, and the weight value corresponding to the predicted point is obtained.
  • FIG. 9 is a schematic diagram of a hardware structure of a terminal implementing an embodiment of the present application.
  • the terminal 900 includes, but is not limited to: a radio frequency unit 901, a network module 902, an audio output unit 903, an input unit 904, a sensor 905, a display unit 906, a user input unit 907, an interface unit 908, a memory 909, and a processor 910.
  • the terminal 900 can also include a power supply (such as a battery) for supplying power to various components, and the power supply can be logically connected to the processor 910 through the power management system, so as to manage charging, discharging, and power consumption through the power management system. Management and other functions.
  • a power supply such as a battery
  • the terminal structure shown in FIG. 9 does not constitute a limitation on the terminal, and the terminal may include more or fewer components than shown in the figure, or combine some components, or arrange different components, which will not be repeated here.
  • the input unit 904 may include a graphics processing unit (Graphics Processing Unit, GPU) 9041 and a microphone 9042, and the graphics processor 9041 is used in a video capture mode or an image capture mode by an image capture device (such as the image data of the still picture or video obtained by the camera) for processing.
  • the display unit 906 may include a display panel 9061, and the display panel 9061 may be configured in the form of a liquid crystal display, an organic light emitting diode, or the like.
  • the user input unit 907 includes at least one of a touch panel 9071 and other input devices 9072 .
  • the touch panel 9071 is also called a touch screen.
  • the touch panel 9071 may include two parts, a touch detection device and a touch controller.
  • Other input devices 9072 may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, switch buttons, etc.), trackballs, mice, and joysticks, which will not be repeated here.
  • the radio frequency unit 901 may transmit the downlink data from the network side device to the processor 910 for processing after receiving the downlink data; in addition, the radio frequency unit 901 may send the uplink data to the network side device.
  • the radio frequency unit 901 includes, but is not limited to, an antenna, an amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like.
  • the memory 909 can be used to store software programs or instructions as well as various data.
  • the memory 909 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, an application program or instructions required by at least one function (such as a sound playing function, image playback function, etc.), etc.
  • memory 909 may include volatile memory or nonvolatile memory, or memory x09 may include both volatile and nonvolatile memory.
  • the non-volatile memory can be read-only memory (Read-Only Memory, ROM), programmable read-only memory (Programmable ROM, PROM), erasable programmable read-only memory (Erasable PROM, EPROM), electronically programmable Erase Programmable Read-Only Memory (Electrically EPROM, EEPROM) or Flash.
  • ROM Read-Only Memory
  • PROM programmable read-only memory
  • Erasable PROM Erasable PROM
  • EPROM erasable programmable read-only memory
  • Electrical EPROM Electrical EPROM
  • EEPROM electronically programmable Erase Programmable Read-Only Memory
  • Volatile memory can be random access memory (Random Access Memory, RAM), static random access memory (Static RAM, SRAM), dynamic random access memory (Dynamic RAM, DRAM), synchronous dynamic random access memory (Synchronous DRAM, SDRAM), double data rate synchronous dynamic random access memory (Double Data Rate SDRAM, DDRSDRAM), enhanced synchronous dynamic random access memory (Enhanced SDRAM, ESDRAM), synchronous connection dynamic random access memory (Synch link DRAM , SLDRAM) and Direct Memory Bus Random Access Memory (Direct Rambus RAM, DRRAM).
  • RAM Random Access Memory
  • SRAM static random access memory
  • DRAM dynamic random access memory
  • DRAM synchronous dynamic random access memory
  • SDRAM double data rate synchronous dynamic random access memory
  • Double Data Rate SDRAM Double Data Rate SDRAM
  • DDRSDRAM double data rate synchronous dynamic random access memory
  • Enhanced SDRAM, ESDRAM enhanced synchronous dynamic random access memory
  • Synch link DRAM , SLDRAM
  • Direct Memory Bus Random Access Memory Direct Rambus
  • the processor 910 may include one or more processing units; optionally, the processor 910 integrates an application processor and a modem processor, wherein the application processor is mainly involved in the operation of the operating system, user interface, and application programs, and the modulation and demodulation processor
  • the demodulation processor mainly processes wireless communication signals, such as a baseband processor. It can be understood that the foregoing modem processor may not be integrated into the processor 910 .
  • the processor is used to perform the following operations:
  • Quantization and entropy coding are performed on the prediction residual of the first attribute to obtain a target code stream.
  • the point cloud to be encoded is obtained, and at least one predicted point corresponding to the point to be encoded is determined based on the position of the point to be encoded in the point cloud to be encoded; based on the reconstruction attribute value of the predicted point and the weight value corresponding to the predicted point, Determine the predicted attribute value of the point cloud to be encoded; perform quantization and entropy encoding on the first attribute prediction residual to obtain the target code stream.
  • point cloud attribute encoding process no reordering of the point cloud to be encoded is involved, so the geometric encoding order of the point cloud after geometric encoding will not be disrupted, and the nearest neighbor point of the point to be encoded can be determined based on the geometric encoding order. Attribute predictive coding is performed to improve coding efficiency.
  • processor 910 is configured to perform the following operations:
  • the reconstructed attribute value of the predicted point, and the weight value corresponding to the predicted point is obtained.
  • the target code stream is obtained, and the target code stream includes the point cloud to be decoded, and at least one prediction point corresponding to the point to be decoded is determined based on the position of the point to be decoded in the point cloud to be decoded; based on the second
  • the attribute prediction residual, the reconstructed attribute value of the predicted point, and the weight value corresponding to the predicted point are used to obtain the reconstructed attribute value of the point to be decoded.
  • the embodiment of the present application also provides a readable storage medium, the readable storage medium stores a program or an instruction, and when the program or instruction is executed by a processor, each process of the above-mentioned point cloud attribute encoding method embodiment is realized, or,
  • the various processes of the above-mentioned embodiment of the point cloud attribute decoding method can achieve the same technical effect, so in order to avoid repetition, details are not repeated here.
  • the processor is the processor in the terminal described in the foregoing embodiments.
  • 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, and the like.
  • the embodiment of the present application further provides a chip, the chip includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is used to run programs or instructions to realize the implementation of the above point cloud attribute encoding method
  • the chip includes a processor and a communication interface
  • the communication interface is coupled to the processor
  • the processor is used to run programs or instructions to realize the implementation of the above point cloud attribute encoding method
  • the chip mentioned in the embodiment of the present application may also be called a system-on-chip, a system-on-chip, a system-on-a-chip, or a system-on-a-chip.
  • the embodiment of the present application further provides a computer program product, the computer program product is stored in a storage medium, and the computer program product is executed by at least one processor to implement the various processes in the above embodiment of the point cloud attribute encoding method, Or realize each process of the above-mentioned point cloud attribute decoding method embodiment, and can achieve the same technical effect, in order to avoid repetition, no more details here.
  • the term “comprising”, “comprising” or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article or apparatus comprising a set of elements includes not only those elements, It also includes other elements not expressly listed, or elements inherent in the process, method, article, or device. Without further limitations, an element defined by the phrase “comprising a " does not preclude the presence of additional identical elements in the process, method, article, or apparatus comprising that element.
  • 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 reverse order according to the functions involved. Functions are performed, for example, the described methods may be performed in an order different from that described, and various steps may also be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.
  • the methods of the above embodiments can be implemented by means of software plus a necessary general-purpose hardware platform, and of course also by hardware, but in many cases the former is better implementation.
  • the technical solution of the present application can be embodied in the form of computer software products, which are stored in a storage medium (such as ROM/RAM, magnetic disk, etc.) , CD-ROM), including several instructions to make a terminal (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) execute the methods described in the various embodiments of the present application.

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Abstract

La présente demande se rapporte au domaine technique du traitement de nuage de points et concerne un procédé de codage d'attribut de nuage de points, un procédé de décodage d'attribut de nuage de points et un terminal. Le procédé de codage d'attribut de nuage de points décrit dans des modes de réalisation de la présente invention consiste à : obtenir un nuage de points à coder (S101) ; sur la base de l'emplacement d'un point à coder dans le nuage de points à coder, déterminer au moins un point prédit correspondant au point à coder (S102) ; sur la base d'une valeur d'attribut reconstruite du point prédit et d'une valeur pondérale correspondant à la valeur prédite, déterminer une valeur d'attribut prédite du point à coder (S103) ; et effectuer la quantification et le codage entropique d'un premier résidu de prédiction d'attribut pour obtenir un flux de code cible (S104), le premier résidu de prédiction d'attribut étant déterminé sur la base d'une valeur d'attribut réelle et de la valeur d'attribut prédite du point à coder.
PCT/CN2022/135874 2021-12-03 2022-12-01 Procédé de codage d'attribut de nuage de points, procédé de décodage d'attribut de nuage de points et terminal WO2023098802A1 (fr)

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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112449754A (zh) * 2019-07-04 2021-03-05 深圳市大疆创新科技有限公司 一种数据编码、数据解码方法、设备及存储介质
CN112789657A (zh) * 2018-10-02 2021-05-11 索尼公司 图像处理装置和方法
WO2021141117A1 (fr) * 2020-01-09 2021-07-15 パナソニック インテレクチュアル プロパティ コーポレーション オブ アメリカ Procédé de codage de données tridimensionnelles, procédé de décodage de données tridimensionnelles, dispositif de codage de données tridimensionnelles et dispositif de décodage de données tridimensionnelles
WO2021201384A1 (fr) * 2020-04-03 2021-10-07 엘지전자 주식회사 Appareil et procédé permettant de traiter des données de nuage de points
WO2021210550A1 (fr) * 2020-04-14 2021-10-21 パナソニック インテレクチュアル プロパティ コーポレーション オブ アメリカ Procédé de codage de données tridimensionnelles, procédé de décodage de données tridimensionnelles, dispositif de codage de données tridimensionnelles et dispositif de décodage de données tridimensionnelles
CN113646807A (zh) * 2019-03-25 2021-11-12 松下电器（美国）知识产权公司 三维数据编码方法、三维数据解码方法、三维数据编码装置、以及三维数据解码装置

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
MX2021003502A (es) * 2018-10-09 2021-05-27 Panasonic Ip Corp America Metodo de codificacion de datos tridimensionales, metodo de decodificacion de datos tridimensionales, dispositivo de codificacion de datos tridimensionales y dispositivo de decodificacion de datos tridimensionales.
CN112565734B (zh) * 2020-12-03 2022-04-19 西安电子科技大学 基于混合编码的点云属性编解码方法及装置
CN113179410B (zh) * 2021-06-10 2022-08-23 上海交通大学 一种点云属性的编解码方法、装置和系统

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112789657A (zh) * 2018-10-02 2021-05-11 索尼公司 图像处理装置和方法
CN113646807A (zh) * 2019-03-25 2021-11-12 松下电器（美国）知识产权公司 三维数据编码方法、三维数据解码方法、三维数据编码装置、以及三维数据解码装置
CN112449754A (zh) * 2019-07-04 2021-03-05 深圳市大疆创新科技有限公司 一种数据编码、数据解码方法、设备及存储介质
WO2021141117A1 (fr) * 2020-01-09 2021-07-15 パナソニック インテレクチュアル プロパティ コーポレーション オブ アメリカ Procédé de codage de données tridimensionnelles, procédé de décodage de données tridimensionnelles, dispositif de codage de données tridimensionnelles et dispositif de décodage de données tridimensionnelles
WO2021201384A1 (fr) * 2020-04-03 2021-10-07 엘지전자 주식회사 Appareil et procédé permettant de traiter des données de nuage de points
WO2021210550A1 (fr) * 2020-04-14 2021-10-21 パナソニック インテレクチュアル プロパティ コーポレーション オブ アメリカ Procédé de codage de données tridimensionnelles, procédé de décodage de données tridimensionnelles, dispositif de codage de données tridimensionnelles et dispositif de décodage de données tridimensionnelles

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