WO2022257968A1 - 点云编码方法、点云解码方法及终端 - Google Patents
点云编码方法、点云解码方法及终端 Download PDFInfo
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- G—PHYSICS
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T9/00—Image coding
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
- H04N19/134—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or criterion affecting or controlling the adaptive coding
- H04N19/157—Assigned coding mode, i.e. the coding mode being predefined or preselected to be further used for selection of another element or parameter
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
- H04N19/134—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or criterion affecting or controlling the adaptive coding
- H04N19/157—Assigned coding mode, i.e. the coding mode being predefined or preselected to be further used for selection of another element or parameter
- H04N19/159—Prediction type, e.g. intra-frame, inter-frame or bidirectional frame prediction
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/50—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding
- H04N19/597—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding specially adapted for multi-view video sequence encoding
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- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/90—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using coding techniques not provided for in groups H04N19/10-H04N19/85, e.g. fractals
- H04N19/96—Tree coding, e.g. quad-tree coding
Definitions
- the present application belongs to the technical field of point cloud processing, and in particular relates to a point cloud encoding method, a point cloud 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.
- Each point in the point cloud usually includes geometric information and attribute information.
- the above-mentioned geometric information is, for example, three-dimensional coordinates (x, y, z), and the above-mentioned attribute information is, for example, red, green, and blue colors (R, G, B) and reflectivity.
- the geometric information of the point cloud is encoded first. After the geometric encoding is completed and the point cloud is geometrically reconstructed, the attribute information of the point cloud is Perform attribute encoding, which causes a large delay in the attribute encoding of the point cloud.
- the above-mentioned multi-fork tree encoding includes but not limited to octree encoding, quadtree encoding and binary tree encoding;
- the geometric information of the point cloud can only be obtained after the cloud is divided into a complete multi-fork tree, which causes a large delay in the geometric encoding of the point cloud.
- the decoding process of the point cloud is consistent with the encoding process of the point cloud, and there is also a large delay.
- the embodiment of the present application provides a point cloud encoding method, a point cloud decoding method, and a terminal, which can solve the problem that there is a high time delay in the encoding and decoding process of the point cloud, thereby reducing the encoding and decoding efficiency of the point cloud.
- a point cloud encoding method comprising:
- the encoding operation includes at least one of the following:
- the first identification parameter is used to characterize parallel encoding, performing geometric encoding and attribute predictive encoding on the first target point cloud in parallel to obtain an encoding result of the first target point cloud;
- a point cloud decoding method comprising:
- the decoding operation includes at least one of the following:
- the fifth identification parameter is used to represent parallel decoding, perform geometry decoding and attribute prediction decoding on the second target point cloud in parallel to obtain a decoding result of the second target point cloud;
- an encoder including:
- the first obtaining module is used to obtain the first identification parameter of the first target point cloud to be encoded
- An encoding module configured to perform an encoding operation on the first target point cloud based on the first identification parameter
- the encoding operation includes at least one of the following:
- the first identification parameter is used to characterize parallel encoding, performing geometric encoding and attribute predictive encoding on the first target point cloud in parallel to obtain an encoding result of the first target point cloud;
- a decoder including:
- the second acquisition module is used to acquire the fifth identification parameter of the second target point cloud to be decoded
- a decoding module configured to perform a decoding operation on the second target point cloud based on the fifth identification parameter
- the decoding operation includes at least one of the following:
- the fifth identification parameter is used to characterize parallel decoding, perform geometric decoding and attribute prediction decoding on the second target point cloud in parallel to obtain the decoding result of the second target point cloud;
- a terminal includes a processor, a memory, and a program or instruction stored in the memory and operable on the processor.
- the program or instruction is executed by the processor Implement the steps of the method described in the first aspect, or implement the steps of the method described in the second aspect.
- 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 method as described in the first aspect are realized, or the steps of the method as described in the first aspect are realized, or the steps as described in The steps of the method described in the second 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 implement the method as described in the first aspect , or implement the steps of the method described in the second aspect.
- a computer program product is provided, the computer program product is stored in a non-volatile storage medium, and the computer program product is executed by at least one processor to implement the method described in the first aspect steps, or implement the steps of the method as described in the second aspect.
- a communication device configured to execute the steps of the method described in the first aspect, or execute the steps of the method described in the second aspect.
- the geometry encoding and attribute prediction encoding are performed on the first target point cloud in parallel, so as to reduce the time delay of the first target point cloud in the attribute encoding process .
- the cost of the first target point cloud in the geometric coding process is further reduced. delay. In this way, the encoding efficiency of the first target point cloud is improved by reducing the time delay in the encoding process of the first target point cloud.
- Figure 1 is a schematic diagram of the point cloud AVS encoder framework
- Figure 2 is a schematic diagram of the point cloud AVS decoder framework
- Fig. 3 is the flow chart of the point cloud encoding method provided by the embodiment of the present application.
- Fig. 4 is a schematic flow chart of the parallel encoding provided by the embodiment of the present application.
- FIG. 5 is a schematic flow chart of low-latency geometric predictive coding provided by an embodiment of the present application.
- Fig. 6 is a schematic flow diagram of the hybrid geometric encoding provided by the embodiment of the present application.
- Fig. 7 is a flow chart of the point cloud decoding method provided by the embodiment of the present application.
- FIG. 8 is a structural diagram of an encoder provided in an embodiment of the present application.
- FIG. 9 is a structural diagram of a decoder provided in an embodiment of the present application.
- FIG. 10 is a structural diagram of a communication device provided by an embodiment of the present application.
- FIG. 11 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 encoder corresponding to the point cloud encoding method and the decoder corresponding to the point cloud decoding method in the embodiments of the present application can be terminals, and the terminal can also be called terminal equipment or user equipment (User Equipment, UE), and the terminal can be a mobile phone , Tablet Personal Computer, Laptop Computer or Notebook Computer, Personal Digital Assistant (PDA), PDA, Netbook, 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) and other terminal-side devices, wearable devices include: smart watches, bracelets, earphones, glasses, etc. It should be noted that, the embodiment of the present application does not limit the specific type of the terminal.
- Figure 1 As shown in Figure 1, currently, in the technical standard of digital audio and video encoding and decoding, geometric information and attribute information of the point cloud are encoded separately by using the point cloud AVS encoder.
- 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 node 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 Red Green Blue (RGB) color space to 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 reconstructed attribute value of the found neighbor is used to predict the point to be predicted to obtain the predicted attribute value, and then the The real attribute value and the predicted attribute value are differentiated to obtain the prediction residual, and finally the prediction residual is quantized and encoded 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 AVS decoder framework is shown in FIG. 2 .
- the attribute encoding of the point cloud needs to be completed after the geometric encoding of the point cloud is completed, resulting in a long time delay for the attribute encoding of the point cloud.
- the geometric information corresponding to the encoded points in the point cloud can only be obtained after the point cloud is divided into a complete multi-fork tree, resulting in a long delay in the geometric encoding of the point cloud.
- the points to be encoded need to store the occupancy information of neighbor nodes, which takes up a lot of memory space.
- the present application provides a point cloud encoding method and a point cloud decoding method.
- FIG. 3 is a flow chart of the point cloud encoding method provided by the present application.
- the point cloud coding method provided in this embodiment includes the following steps:
- the point cloud to be encoded is referred to as the first target point cloud.
- the first target point cloud can be It is understood as a frame of point cloud; and 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, that is to say, a point cloud includes multiple coded points.
- the above-mentioned first identification parameter is a parameter in the sequence parameter set (Sequence Parameter Set, SPS) corresponding to the first target point cloud.
- the above-mentioned first flag parameter may be a synchronous geometric attribute enabling flag (geometry_attribute_simultaneous_enable_flag).
- the sequence parameter set refers to the parameter set corresponding to the point cloud sequence
- the point cloud sequence refers to the sequence formed by multi-frame point clouds.
- the first identification can be obtained from the sequence parameter set corresponding to the point cloud sequence to which the first target point cloud belongs. parameter.
- the geometry encoding and attribute prediction encoding are performed in parallel on the first target point cloud to obtain the encoding result of the first target point cloud.
- the above geometric coding refers to performing multi-tree coding on the first target point cloud
- the above attribute prediction coding refers to performing attribute coding on the first target point cloud by determining the attribute prediction mode corresponding to the first target point cloud, wherein, the first target point cloud
- the above encoding results include geometric entropy encoding and attribute entropy encoding corresponding to the first target point cloud.
- the geometry coding and attribute prediction coding are performed in parallel on the first target point cloud, wherein the geometry prediction coding is performed on at least part of the points to be coded in the first target point cloud.
- traditional octree encoding can be performed on some of the points to be encoded in the first target point cloud, and geometric prediction encoding can be performed on the other part of the points to be encoded; or, all the points to be encoded in the first target point cloud Dot performs geometric predictive coding.
- FIG. 4 is a schematic flowchart of parallel encoding provided by the embodiment of the present application.
- it is determined whether there is a first identification parameter in the sequence parameter set, and if there is a first identification parameter in the sequence parameter set, the geometric encoding and attribute prediction encoding are performed on the first target point cloud in parallel; if there is no first identification parameter in the sequence parameter set If an identification parameter is used, geometric encoding is first performed on the first target point cloud, and then attribute encoding is performed on the first target point cloud.
- geometric predictive coding is first performed on at least part of the points to be encoded in the first target point cloud, and then attribute coding is performed on the first target point cloud.
- the above-mentioned geometric prediction coding refers to performing geometric coding on the first target point cloud by determining the geometric prediction mode corresponding to the first target point cloud, wherein, for the specific implementation manner of geometric prediction coding on the first target point cloud, please refer to the follow-up implementation example.
- the points to be encoded need to store the occupancy information of neighbor nodes, which reduces the memory occupied by geometric encoding.
- the geometry encoding and attribute prediction encoding are performed on the first target point cloud in parallel, so as to reduce the time delay of the first target point cloud in the attribute encoding process .
- the cost of the first target point cloud in the geometric coding process is further reduced. delay. In this way, the encoding efficiency of the first target point cloud is improved by reducing the time delay in the encoding process of the first target point cloud.
- performing geometric predictive coding on at least some of the points to be coded in the first target point cloud includes:
- the second identification parameter corresponding to the first target point cloud is used to represent the execution of geometric predictive coding for all points to be encoded, based on the encoding sequence corresponding to the points to be encoded in the first target point cloud, determine N Geometric predictive value;
- Entropy coding is performed on the quantized first prediction residual.
- sequence parameter set includes a geometry parameter set (Geometry Parameters Set, GPS) and an attribute parameter set (Attributes Parameter Set, APS), where the parameters in the geometry parameter set are related to the geometric encoding process of the point cloud, and the parameters in the attribute parameter set It is related to the attribute encoding process of point cloud.
- geometry parameter set Geometry Parameters Set, GPS
- attribute parameter set Attributes Parameter Set, APS
- the above-mentioned second flag parameter is a parameter in the geometry parameter set.
- the above-mentioned second flag parameter can be set as a low latency geometry enable flag (low_latency_geometry_enable_flag), wherein the second flag parameter is also called Low latency parameter.
- low_latency_geometry_enable_flag the second flag parameter is also called Low latency parameter.
- the second identification parameter exists in the geometric parameter set corresponding to the first target point cloud, perform geometric predictive coding on all points to be encoded in the first target point cloud, and perform geometric predictive coding on all points to be encoded The process is called low-latency geometric predictive coding.
- a first list is established in advance, and the first list includes N geometric prediction values, wherein the N geometric prediction values correspond to the N geometric prediction modes one by one, and N is a positive integer greater than 1.
- the determining the N geometric prediction values based on the encoding sequence corresponding to the points to be encoded in the first target point cloud includes at least one of the following:
- the coding sequence corresponding to the point to be coded is less than or equal to a preset value, preset the N geometric prediction values;
- the N geometric prediction values are associated with coded points in the first target point cloud.
- N geometric prediction values are preset in the first list, wherein each geometric prediction value is different.
- the geometric prediction value in the first list is set according to the geometric information of the coded point.
- the number of N is 4, that is, the first list includes 4 geometric predictors, and the coding order of the points to be encoded is 5, then the 4 to-be-coded points whose encoding orders are 1 to 4 before the points to be encoded can be used. Encodes the geometric information of the point, and determines the geometric prediction value.
- the determination rule of the geometric prediction value can be that the first geometric prediction value is the sum of the geometric information of the 4 points to be encoded; the second geometric prediction value is the minimum geometric information of the 4 points to be encoded; the third geometric The predicted value is the average value of the geometric information of the four points to be encoded; the fourth predicted geometric value is the difference between the geometric information of the fourth point to be encoded and the geometric information of the third point to be encoded.
- the geometric information of the point to be encoded can be characterized as the three-dimensional coordinates (x, y, z) of the point to be encoded.
- the N geometric prediction values correspond to the N geometric prediction modes one by one, that is, each geometric prediction value is used to represent a geometric prediction mode.
- the predicted geometric information corresponding to the points to be encoded is obtained.
- the predicted geometric information can be understood as a three-dimensional coordinate; the above predicted geometric information is used as the input of the rate-distortion cost algorithm , calculate the rate-distortion cost of the point to be encoded in the geometric prediction mode.
- the geometric prediction mode with the smallest rate-distortion cost is determined as the target geometric prediction mode.
- the geometric parameter set may have a third identification parameter and a first parameter value associated with the third identification parameter.
- the third flag parameter may be represented as a geometry quantization enabled flag (geometry_enable_quantized_flag), and the first parameter value may be represented as GeomQP[3].
- the third identification parameter is used to characterize lossy coding, that is to say, if there is a third identification parameter in the geometric parameter set, it means that the geometric quantization in the loop is introduced to the point to be coded.
- the intra-loop geometric quantization can be understood as quantizing the prediction residual generated by geometrically encoding the point to be coded.
- the first parameter value is used to quantize the first prediction residual corresponding to the target geometric prediction mode, and the quantized first prediction residual is entropy Encoding, get the geometric entropy encoding.
- the first prediction residual can be understood as the difference between the geometric prediction coding point and the point to be coded, and the geometric prediction coding point is the code point obtained by performing geometric prediction coding on the to-be-coded point by using the target geometric prediction mode.
- entropy coding is directly performed on the first prediction residual corresponding to the target geometric prediction mode to obtain geometric entropy coding.
- geometric predictive coding is performed on all points to be coded in the first target point cloud, since geometric predictive coding does not involve multi-tree division of the points to be coded , so the encoding delay of the first target point cloud can be reduced.
- FIG. 5 is a schematic flowchart of low-latency geometric predictive encoding provided by an embodiment of the present application.
- multi-tree coding is performed on the first target point cloud, and entropy coding is performed on the coding result of the multi-tree coding to obtain geometric entropy coding.
- the geometric prediction coding is performed on the first target point cloud, and if there is a third identification parameter in the geometric parameter set, the first identification parameter associated with the third identification parameter is used.
- a parameter value quantizes the prediction residual obtained by the geometric prediction coding to obtain the quantized prediction residual, and performs entropy coding on the quantized prediction residual to obtain the geometric entropy coding. If the third identification parameter does not exist in the geometric parameter set, entropy encoding is directly performed on the prediction residual to obtain geometric entropy encoding.
- the points to be encoded in the first target point cloud can be preset to be sorted, the encoding order of the points to be encoded is determined, and then each point to be encoded is performed Geometric predictive coding.
- Morton code sorting For example, Morton code sorting, Hilbert sorting or azimuth order sorting can be performed on the points to be coded in advance to determine the coding order of the points to be coded.
- performing geometric predictive coding on at least some of the points to be coded in the first target point cloud includes:
- the fourth identification parameter corresponding to the first target point cloud is used to characterize the hybrid encoding, acquire a second parameter value associated with the fourth identification parameter;
- the first to-be-encoded point and the second to-be-encoded point are encoded by using different encoding methods.
- the fourth identification parameter may be represented as a geometry_enable_predict_flag enable flag (geometry_enable_predict_flag), the fourth identification parameter is also called a hybrid coding parameter, and the fourth identification parameter is used to represent a hybrid coding, that is, if there is a first Four identification parameters, perform multi-tree encoding on part of the points to be encoded in the first target point cloud, and perform geometric prediction encoding on the other part of the points to be encoded.
- the second parameter value may be expressed as an octree division end node (octree_division_end_node) SizeLog2[3].
- the dividing the first target point cloud into first points to be encoded and second points to be encoded based on the second parameter value includes:
- the points to be encoded corresponding to the Mth encoding layer to the Lth encoding layer of the first target point cloud are determined as the second points to be encoded.
- the first target point cloud includes L coding layers
- the second parameter value is used to indicate the Mth coding layer
- L is a positive integer greater than 1
- M is a positive integer smaller than L.
- the first target point cloud includes 10 coding layers, that is, L is 10; the second parameter value is used to indicate the fifth coding layer, that is, M is 5.
- the points to be encoded corresponding to the first encoding layer to the fourth encoding layer of the first target point cloud are determined as the first points to be encoded;
- the point to be encoded corresponding to the tenth encoding layer is determined as the second point to be encoded.
- the first to-be-encoded point is also called a high-bit to-be-encoded point
- the second to-be-encoded point is also called a low-bit to-be-encoded point.
- the points to be encoded in the first target point cloud are divided into first points to be encoded and second points to be encoded, different encoding methods are used to encode the first points to be encoded and the second points to be encoded .
- encoding the first point to be encoded and the second point to be encoded by using different encoding methods includes:
- multi-tree encoding is performed on the first point to be encoded, wherein the above-mentioned multi-tree encoding includes but not limited to octree encoding, quadtree encoding and binary tree encoding.
- the geometric predictive coding is performed on the second point to be coded.
- the specific content of the geometric predictive coding please refer to the above-mentioned embodiments, which will not be repeated here.
- geometric predictive encoding may be performed on the first point to be encoded, and multi-tree encoding may be performed on the second point to be encoded.
- geometric predictive coding is performed on some of the points to be encoded in the first target point cloud. For this part of the points to be encoded , does not need to divide the multi-tree, which reduces the coding time delay of some points to be coded in the geometric coding process, thereby improving the coding efficiency.
- FIG. 6 is a schematic flowchart of the hybrid geometric encoding provided by the embodiment of the present application.
- the fourth identification parameter does not exist in the geometric parameter set
- multi-tree coding is performed on the first target point cloud
- entropy coding is performed on the coding result of the multi-tree coding to obtain geometric entropy coding.
- the second parameter value associated with the fourth identification parameter in the geometric parameter set is obtained, and the point to be encoded of the first target point cloud is divided into the first point to be encoded and the first point to be encoded using the second parameter value.
- multi-tree encoding is performed on the first point to be encoded; geometric predictive encoding is performed on the second point to be encoded, and entropy encoding is performed on the prediction residual obtained by geometric predictive encoding to obtain geometric entropy encoding.
- multi-tree encoding and attribute predictive encoding are performed synchronously on the point to be encoded.
- performing attribute predictive coding on the first target point cloud includes:
- Entropy encoding is performed on the second prediction residual corresponding to the target attribute prediction mode, where the target attribute prediction mode is an attribute prediction mode corresponding to the smallest rate-distortion cost.
- the second list is established in advance, and the second list includes I attribute prediction values, wherein, the I attribute prediction values correspond to the I attribute prediction modes one by one, and I is a positive integer greater than 1.
- the above-mentioned I attribute predictive values may be determined based on the encoding sequence corresponding to the points to be encoded.
- one attribute prediction value is preset.
- the above preset value may be 1.
- the I attribute predictive values corresponding to the points to be encoded whose encoding order is 1 are all preset, and the preset I attribute predictive values are different from each other.
- one attribute prediction value may be determined based on the attribute information of the encoded points in the first target point cloud.
- the above-mentioned preset value is 1, and the number of I is 4, that is, the second list includes 4 attribute prediction values, and the coding order of the points to be encoded is 5;
- the attribute information of the 4 to-be-encoded points from 4 to 4 is used to determine the attribute prediction value.
- the determination rules of the attribute prediction value are the same as the determination rules of the above geometric prediction value, which will not be repeated here, and the specific determination rules of the attribute prediction value can be flexibly set, and are not specifically limited here.
- one attribute prediction value is in one-to-one correspondence with one attribute prediction mode, that is, each attribute prediction value is used to represent an attribute prediction mode.
- Use I attribute prediction mode to perform attribute prediction encoding on the code point to be coded, and determine the rate-distortion cost corresponding to each attribute prediction mode. It should be understood that the specific manner of performing attribute predictive coding on the to-be-coded points is the same as the above-mentioned manner of performing geometric predictive coding on the to-be-coded points, and will not be repeated here.
- the attribute prediction mode with the smallest rate-distortion cost is determined as the target attribute prediction mode, and entropy coding is performed on the second prediction residual corresponding to the target attribute prediction mode to obtain attribute entropy coding.
- the second prediction residual can be understood as the difference between the attribute predicted code point and the point to be coded, and the above attribute predicted code point is the code point obtained by performing attribute predictive coding on the code point to be coded using the target attribute prediction mode.
- attribute predictive encoding is used to encode the point to be encoded to obtain attribute entropy encoding corresponding to the point to be encoded.
- attribute information corresponding to the point to be encoded can be obtained without using geometric information, which greatly reduces the delay in the attribute encoding process and improves the encoding efficiency of the point cloud.
- attribute predictive encoding may also be performed on the first target point cloud in the following manner.
- performing attribute predictive coding on the first target point cloud includes:
- the target encoding point is an encoded point in the first target point cloud
- the attribute information corresponding to the target encoding point determine one attribute prediction value corresponding to the to-be-encoded point
- Entropy encoding is performed on the second prediction residual corresponding to the target attribute prediction mode, where the target attribute prediction mode is an attribute prediction mode corresponding to the smallest rate-distortion cost.
- geometric encoding is performed on some encoding points in advance to obtain the geometric information of the partial encoding points.
- attribute predictive encoding is performed on the first target point cloud. It should be understood that during the process of performing attribute prediction encoding on the first target point cloud, geometric encoding is performed on the first target point cloud in parallel. That is to say, before performing geometry encoding and attribute prediction encoding on the first target point cloud in parallel, the geometric information of some encoded points has been obtained.
- the geometric information corresponding to the points to be encoded and the geometric information corresponding to the encoded points can be obtained, and the encoded points that match the geometric information corresponding to the points to be encoded are determined as target encoding points.
- geometric information can be understood as three-dimensional coordinates.
- An optional implementation is to use the three-dimensional coordinates corresponding to the points to be encoded as the search center, and search for encoded points within the preset range of the search center. If the number of coded points is 1, the coded point is determined as the target coded point; if there are multiple coded points, the Euclidean distance between the three-dimensional coordinates corresponding to each coded point and the search center is calculated, and the The coded point with the shortest Euclidean distance is determined as the target coded point.
- target code point may also be determined in other ways, and the above is only an example.
- the second list is pre-established, and the second list includes one attribute prediction value.
- the above one attribute prediction value can be determined based on the attribute information corresponding to the target coding point. value.
- the first attribute prediction value can be set as the color information corresponding to the target code point
- the second attribute prediction value can be set as the color information corresponding to the target code point.
- Reflectance set the predicted value of the third attribute as the product of the color information corresponding to the target code point and the reflectance.
- each attribute prediction value is used to represent an attribute prediction mode.
- Use I attribute prediction mode to perform attribute prediction encoding on the code point to be coded, and determine the rate-distortion cost corresponding to each attribute prediction mode. Then, the attribute prediction mode with the smallest rate-distortion cost is determined as the target attribute prediction mode, and entropy coding is performed on the second prediction residual corresponding to the target attribute prediction mode to obtain attribute entropy coding.
- the geometric information corresponding to the point to be encoded, and the geometric information and attribute information corresponding to the encoded point can be obtained.
- the target encoding point corresponding to the point to be encoded is determined. It should be understood that the three-dimensional coordinate point represented by the point to be encoded is relatively close to the three-dimensional coordinate point represented by the target encoding point.
- the attribute information of the code point is subjected to attribute predictive coding of the code point to be coded, so as to improve the coding efficiency of the attribute predictive coding.
- FIG. 7 is a flow chart of the point cloud decoding method provided by the present application.
- the point cloud decoding method provided in this embodiment includes the following steps:
- the point cloud to be decoded is referred to as the second target point cloud
- the above-mentioned fifth identification parameter can be the same identification parameter as the first identification parameter
- the fifth identification parameter is obtained from the sequence parameter set corresponding to the second target point cloud .
- geometry decoding and attribute prediction decoding are performed on the second target point cloud in parallel to obtain the encoding result of the second target point cloud.
- the above-mentioned geometric decoding refers to performing multi-tree decoding on the second target point cloud.
- the above attribute prediction decoding refers to performing attribute decoding on the second target point cloud by determining the attribute prediction mode corresponding to the second target point cloud. It should be understood that the method of determining the attribute prediction mode corresponding to the second target point cloud is the same as determining the first The attribute prediction mode corresponding to the target point cloud is the same way.
- the above decoding result includes geometry information and attribute information.
- geometric prediction decoding is first performed on at least part of the points to be decoded in the second target point cloud, and then attribute decoding is performed on the second target point cloud.
- the above-mentioned geometric prediction decoding refers to performing geometric decoding on the second target point cloud by determining the geometric prediction mode corresponding to the second target point cloud. It should be understood that the method of determining the geometric prediction mode corresponding to the second target point cloud is different from determining The geometric prediction mode corresponding to the first target point cloud is in the same way.
- the geometry decoding and the attribute prediction decoding are performed in parallel on the second target point cloud, wherein the geometry prediction decoding is performed on at least part of the points to be decoded in the second target point cloud.
- geometry decoding and attribute prediction decoding are performed on the second target point cloud in parallel, so as to reduce the time delay of the second target point cloud in the attribute decoding process.
- Performing geometric prediction decoding on at least part of the points to be decoded of the second target point cloud further reducing the delay in the geometric decoding process of the second target point cloud.
- geometric prediction decoding may be performed on all points to be decoded in the second target point cloud.
- the point to be decoded can be performed using the first parameter value.
- the specific implementation of lossy decoding is the same as that of performing lossy encoding on the first target point cloud, and will not be repeated here.
- hybrid decoding is performed on the points to be decoded in the second target point cloud, that is, geometric prediction decoding is performed on some points to be decoded, and the other part is to be decoded. Points perform multi-tree decoding.
- the point cloud encoding method provided in the embodiment of the present application may be executed by an encoder, or a control module in the encoder for executing the point cloud encoding method.
- an encoder implementing a point cloud encoding method is taken as an example to illustrate the encoder provided in this embodiment of the present application.
- the encoder 300 includes:
- the first acquisition module 301 is configured to acquire the first identification parameter of the first target point cloud to be encoded
- An encoding module 302 configured to perform an encoding operation on the first target point cloud based on the first identification parameter.
- the encoding module 302 includes:
- the first determining unit is configured to, in the case that the second identification parameter corresponding to the first target point cloud is used to represent the execution of geometric predictive coding for all points to be coded, corresponding to the points to be coded based on the first target point cloud
- the coding order of N geometric prediction values is determined;
- a second determining unit configured to determine a rate-distortion cost corresponding to each of the geometric prediction modes
- a quantization unit configured to quantize the first prediction residual corresponding to the target geometric prediction mode by using the first parameter value
- the first coding unit is configured to perform entropy coding on the quantized first prediction residual.
- the first determination unit is specifically configured to:
- the coding sequence corresponding to the point to be coded is less than or equal to a preset value, preset the N geometric prediction values;
- the N geometric prediction values are associated with coded points in the first target point cloud.
- the encoding module 302 includes:
- An acquisition unit configured to acquire a second parameter value associated with the fourth identification parameter when the fourth identification parameter corresponding to the first target point cloud is used to represent a hybrid encoding
- a division unit configured to divide the first target point cloud into first points to be encoded and second points to be encoded based on the second parameter value
- the second coding unit is configured to use different coding methods to code the first point to be coded and the second point to be coded.
- the second encoding unit is specifically configured to:
- the division unit is specifically used for:
- the points to be encoded corresponding to the Mth encoding layer to the Lth encoding layer of the first target point cloud are determined as the second points to be encoded.
- the encoding module 302 is specifically configured to:
- Entropy encoding is performed on the second prediction residual corresponding to the target attribute prediction mode.
- the encoding module 302 is specifically configured to:
- the attribute information corresponding to the target encoding point determine one attribute prediction value corresponding to the to-be-encoded point
- Entropy coding is performed on the second prediction residual corresponding to the target attribute prediction mode.
- the encoder 300 provided in the embodiment of the present application can implement various processes implemented in the method embodiment in FIG. 3 and achieve the same technical effect. To avoid repetition, details are not repeated here.
- the point cloud decoding method provided in the embodiment of the present application may be executed by a decoder, or a control module in the decoder for executing the point cloud decoding method.
- the decoder provided in the embodiment of the present application is described by taking the point cloud decoding method performed by the decoder as an example.
- the decoder 400 includes:
- the second obtaining module 401 is used to obtain the fifth identification parameter of the second target point cloud to be decoded
- a decoding module 402 configured to perform a decoding operation on the second target point cloud based on the fifth identification parameter.
- the geometry encoding and attribute prediction encoding are performed on the first target point cloud in parallel, so as to reduce the time delay of the first target point cloud in the attribute encoding process .
- the cost of the first target point cloud in the geometric coding process is further reduced. delay. In this way, the encoding efficiency of the first target point cloud is improved by reducing the time delay in the encoding process of the first target point cloud.
- the encoder and decoder in the embodiment of the present application may be a device, a device with an operating system or an electronic device, or a component, an integrated circuit, or a chip in a terminal.
- the apparatus or electronic equipment may be a mobile terminal or a non-mobile terminal.
- a mobile terminal may include but not limited to the types of terminals listed above, and a non-mobile terminal may be a server, a network attached storage (Network Attached Storage, NAS), a personal computer (personal computer, PC), a television (television , TV), teller machines or self-service machines, etc., are not specifically limited in this embodiment of the present application.
- the encoder 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 decoder provided in the embodiment of the present application can implement each process implemented in the method embodiment in FIG. 7 and achieve the same technical effect. To avoid repetition, details are not repeated here.
- this embodiment of the present application further provides a communication device 500, including a processor 501, a memory 502, and programs or instructions stored in the memory 502 and operable on the processor 501,
- a communication device 500 including a processor 501, a memory 502, and programs or instructions stored in the memory 502 and operable on the processor 501
- the communication device 500 is a terminal
- the program or instruction is executed by the processor 501
- each process of the above-mentioned point cloud encoding method embodiment can be achieved, and the same technical effect can be achieved, or the above-mentioned point cloud decoding method embodiment can be realized.
- Each process 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:
- the encoding operation includes at least one of the following:
- the first identification parameter is used to characterize parallel encoding, performing geometric encoding and attribute predictive encoding on the first target point cloud in parallel to obtain an encoding result of the first target point cloud;
- the processor is used to:
- the decoding operation includes at least one of the following:
- the fifth identification parameter is used to represent parallel decoding, perform geometry decoding and attribute prediction decoding on the second target point cloud in parallel to obtain a decoding result of the second target point cloud;
- FIG. 11 is a schematic diagram of a hardware structure of a terminal implementing an embodiment of the present application.
- the terminal 1000 includes but not limited to: a radio frequency unit 1001, a network module 1002, an audio output unit 1003, an input unit 1004, a sensor 1005, a display unit 1006, a user input unit 1007, an interface unit 1008, a memory 1009, and a processor 1010, etc. .
- the terminal 1000 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 1010 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. 11 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 1004 may include a graphics processor (Graphics Processing Unit, GPU) 10041 and a microphone 10042, and the graphics processor 10041 is used for the image capture device (such as the image data of the still picture or video obtained by the camera) for processing.
- the display unit 1006 may include a display panel 10061, and the display panel 10071 may be configured in the form of a liquid crystal display, an organic light emitting diode, or the like.
- the user input unit 1007 includes a touch panel 10071 and other input devices 10072 .
- the touch panel 10071 is also called a touch screen.
- the touch panel 10071 may include two parts, a touch detection device and a touch controller.
- Other input devices 10072 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 1001 receives the downlink data from the network side device, and processes it to the processor 1010; in addition, sends the uplink data to the network side device.
- the radio frequency unit 1001 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like.
- the memory 1009 can be used to store software programs or instructions as well as various data.
- the memory 1009 may mainly include a program or instruction storage area and a data storage area, wherein the program or instruction storage area may store an operating system, at least one application program or instruction required by a function (such as a sound playback function, an image playback function, etc.) and the like.
- the memory 1009 may include a high-speed random access memory, and may also include a nonvolatile memory, wherein the nonvolatile memory may be a read-only memory (Read-Only Memory, ROM), a programmable read-only memory (Programmable ROM) , PROM), erasable programmable read-only memory (Erasable PROM, EPROM), electrically erasable programmable read-only memory (Electrically EPROM, EEPROM) or flash memory.
- ROM Read-Only Memory
- PROM programmable read-only memory
- PROM erasable programmable read-only memory
- Erasable PROM Erasable PROM
- EPROM electrically erasable programmable read-only memory
- EEPROM electrically erasable programmable read-only memory
- flash memory for example at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device.
- the processor 1010 may include one or more processing units; optionally, the processor 1010 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, application programs or instructions, etc., Modem processors mainly handle wireless communications, such as baseband processors. It can be understood that the foregoing modem processor may not be integrated into the processor 1010 .
- the processor is used to perform the following operations:
- the encoding operation includes at least one of the following:
- the first identification parameter is used to characterize parallel encoding, performing geometric encoding and attribute predictive encoding on the first target point cloud in parallel to obtain an encoding result of the first target point cloud;
- the processor is used to:
- the decoding operation includes at least one of the following:
- the fifth identification parameter is used to represent parallel decoding, perform geometry decoding and attribute prediction decoding on the second target point cloud in parallel to obtain a decoding result of the second target point cloud;
- the embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, each process of the above-mentioned point cloud encoding method embodiment is realized, or the above-mentioned
- Each process of the embodiment of the point cloud 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 computer readable storage medium, such as computer read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disk, etc.
- the embodiment of the present application also provides a computer program product, the computer program product is stored in a non-transitory storage medium, and the computer program product is executed by at least one processor to implement each of the above-mentioned point cloud coding method embodiments. process, or realize the various processes of the above-mentioned point cloud decoding method embodiment, and can achieve the same technical effect, in order to avoid repetition, it will not be repeated here.
- 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 implement the above embodiment of the point cloud 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 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
Description
Claims (23)
- 一种点云编码方法,包括:获取待编码的第一目标点云的第一标识参数;基于所述第一标识参数对所述第一目标点云执行编码操作;其中,所述编码操作包括以下至少一项:在所述第一标识参数用于表征并行编码的情况下,对所述第一目标点云并行执行几何编码和属性预测编码,得到所述第一目标点云的编码结果;对所述第一目标点云的至少部分待编码点执行几何预测编码。
- 根据权利要求1所述的方法,其中,所述对所述第一目标点云的至少部分待编码点执行几何预测编码包括:在所述第一目标点云对应的第二标识参数用于表征对全部待编码点执行几何预测编码的情况下,基于所述第一目标点云的待编码点对应的编码顺序,确定N个几何预测值;所述编码顺序基于对所述待编码点进行预设排序确定,所述N个几何预测值与N个几何预测模式一一对应,N为大于1的正整数;确定每一所述几何预测模式对应的率失真代价;使用第一参数值对目标几何预测模式对应的第一预测残差进行量化;所述第一参数值与所述第一目标点云的第三标识参数关联,所述第三标识参数用于表征有损编码,所述目标几何预测模式为最小的率失真代价对应的几何预测模式;对量化后的第一预测残差进行熵编码。
- 根据权利要求2所述的方法,其中,所述基于所述第一目标点云的待编码点对应的编码顺序,确定N个几何预测值包括以下至少一项:在所述待编码点对应的编码顺序小于等于预设值的情况下,预先设置所述N个几何预测值;在所述待编码点对应的编码顺序大于所述预设值的情况下,确定所述N个几何预测值与所述第一目标点云中的已编码点相关联。
- 根据权利要求1所述的方法,其中,所述对所述第一目标点云的至少部分待编码点执行几何预测编码包括:在所述第一目标点云对应的第四标识参数用于表征混合编码的情况下,获取所述第四标识参数关联的第二参数值;基于所述第二参数值,将所述第一目标点云划分为第一待编码点和第二待编码点;使用不同的编码方式对所述第一待编码点和所述第二待编码点进行编码。
- 根据权利要求4所述的方法,其中,所述使用不同的编码方式对所述第一待编码点和所述第二待编码点进行编码包括:对所述第一待编码点进行多叉树编码,以及对所述第二待编码点进行几何预测编码,或者;对所述第一待编码点进行几何预测编码,以及对所述第二待编码点进行多叉树编码。
- 根据权利要求4所述的方法,其中,所述第一目标点云包括L个编码层,所述第二参数值用于指示第M个编码层,L大于1的正整数,M为小于L的正整数;所述基于所述第二参数值,将所述第一目标点云划分为第一待编码点和第二待编码点包括:将所述第一目标点云的第1个编码层至第M-1个编码层对应的待编码点,确定为所述第一待编码点;将所述第一目标点云的第M个编码层至第L个编码层对应的待编码点,确定为所述第二待编码点。
- 根据权利要求1所述的方法,其中,对所述第一目标点云执行属性预测编码包括:基于所述第一目标点云的待编码点对应的编码顺序,确定I个属性预测值;所述I个属性预测值与I个属性预测模式一一对应,I为大于1的正整数;确定每一所述属性预测模式对应的率失真代价;对目标属性预测模式对应的第二预测残差进行熵编码,所述目标属性预测模式为最小的率失真代价对应的属性预测模式。
- 根据权利要求1所述的方法,其中,对所述第一目标点云执行属性预测编码包括:基于所述第一目标点云的待编码点对应的几何信息,确定所述待编码点对应的目标编码点;所述目标编码点为所述第一目标点云中的已编码点;根据所述目标编码点对应的属性信息,确定所述待编码点对应的I个属性预测值;所述I个属性预测值与I个属性预测模式一一对应,I为大于1的正整数;确定每一所述属性预测模式对应的率失真代价;对目标属性预测模式对应的第二预测残差进行熵编码,所述目标属性预测模式为最小的率失真代价对应的属性预测模式。
- 一种点云解码方法,包括:获取待解码的第二目标点云的第五标识参数;基于所述第五标识参数对所述第二目标点云执行解码操作;其中,所述解码操作包括以下至少一项:在所述第五标识参数用于表征并行解码的情况下,对所述第二目标点云并行执行几何解码和属性预测解码,得到所述第二目标点云的解码结果;对所述第二目标点云的至少部分待解码点执行几何预测解码。
- 一种编码器,包括:第一获取模块,用于获取待编码的第一目标点云的第一标识参数;编码模块,用于基于所述第一标识参数对所述第一目标点云执行编码操作;其中,所述编码操作包括以下至少一项:在所述第一标识参数用于表征并行编码的情况下,对所述第一目标点云并行执行几何编码和属性预测编码,得到所述第一目标点云的编码结果;对所述第一目标点云的至少部分待编码点执行几何预测编码。
- 根据权利要求10所述的编码器,其中,所述编码模块包括:第一确定单元,用于在所述第一目标点云对应的第二标识参数用于表征对全部待编码点执行几何预测编码的情况下,基于所述第一目标点云的待编码点对应的编码顺序,确定N个几何预测值;所述编码顺序基于对所述待编码点进行预设排序确定,所述N个几何预测值与N个几何预测模式一一对应,N为大于1的正整数;第二确定单元,用于确定每一所述几何预测模式对应的率失真代价;量化单元,用于使用第一参数值对目标几何预测模式对应的第一预测残差进行量化;所述第一参数值与所述第一目标点云的第三标识参数关联,所述第三标识参数用于表征有损编码,所述目标几何预测模式为最小的率失真代价对应的几何预测模式;第一编码单元,用于对量化后的第一预测残差进行熵编码。
- 根据权利要求11所述的编码器,其中,所述第一确定单元,具体用于:在所述待编码点对应的编码顺序小于等于预设值的情况下,预先设置所述N个几何预测值;在所述待编码点对应的编码顺序大于所述预设值的情况下,确定所述N个几何预测值与所述第一目标点云中的已编码点相关联。
- 根据权利要求10所述的编码器,其中,所述编码模块包括:获取单元,用于在所述第一目标点云对应的第四标识参数用于表征混合编码的情况下,获取所述第四标识参数关联的第二参数值;划分单元,用于基于所述第二参数值,将所述第一目标点云划分为第一待编码点和第二待编码点;第二编码单元,用于使用不同的编码方式对所述第一待编码点和所述第二待编码点进行编码。
- 根据权利要求13所述的编码器,其中,所述第二编码单元具体用于:对所述第一待编码点进行多叉树编码,以及对所述第二待编码点进行几何预测编码,或者;对所述第一待编码点进行几何预测编码,以及对所述第二待编码点进行多叉树编码。
- 根据权利要求13所述的编码器,其中,所述第一目标点云包括L个编码层,所述第二参数值用于指示第M个编码层,L大于1的正整数,M为小于L的正整数;所述划分单元,具体用于:将所述第一目标点云的第1个编码层至第M-1个编码层对应的待编码点, 确定为所述第一待编码点;将所述第一目标点云的第M个编码层至第L个编码层对应的待编码点,确定为所述第二待编码点。
- 根据权利要求10所述的编码器,其中,所述编码模块,具体用于:基于所述第一目标点云的待编码点对应的编码顺序,确定I个属性预测值;所述I个属性预测值与I个属性预测模式一一对应,I为大于1的正整数;确定每一所述属性预测模式对应的率失真代价;对目标属性预测模式对应的第二预测残差进行熵编码,所述目标属性预测模式为最小的率失真代价对应的属性预测模式。
- 根据权利要求10所述的编码器,其中,所述编码模块,具体用于:基于所述第一目标点云的待编码点对应的几何信息,确定所述待编码点对应的目标编码点;所述目标编码点为所述第一目标点云中的已编码点;根据所述目标编码点对应的属性信息,确定所述待编码点对应的I个属性预测值;所述I个属性预测值与I个属性预测模式一一对应,I为大于1的正整数;确定每一所述属性预测模式对应的率失真代价;对目标属性预测模式对应的第二预测残差进行熵编码,所述目标属性预测模式为最小的率失真代价对应的属性预测模式。
- 一种解码器,包括:第二获取模块,用于获取待解码的第二目标点云的第五标识参数;解码模块,用于基于所述第五标识参数对所述第二目标点云执行解码操作;其中,所述解码操作包括以下至少一项:在所述第五标识参数用于表征并行解码的情况下,对所述第二目标点云并行执行几何解码和属性预测解码,得到所述第二目标点云的解码结果;对所述第二目标点云的至少部分待解码点执行几何预测解码。
- 一种终端,包括处理器,存储器及存储在所述存储器上并可在所述处理器上运行的程序或指令,其中,所述程序或指令被所述处理器执行时实现如权利要求1-8任一项所述的点云编码方法的步骤,或者实现如权利要求9 所述的点云解码方法的步骤。
- 一种可读存储介质,所述可读存储介质上存储程序或指令,其中,所述程序或指令被处理器执行时实现如权利要求1-8任一项所述的点云编码方法的步骤,或者实现如权利要求9所述的点云解码方法的步骤。
- 一种芯片,包括处理器和通信接口,所述通信接口和所述处理器耦合,其中,所述处理器用于运行程序或指令,实现如权利要求1-8任一项所述的点云编码方法的步骤,或者实现如权利要求9所述的点云解码方法的步骤。
- 一种计算机程序产品,所述计算机程序产品被存储在非瞬态的可读存储介质中,其中,所述计算机程序产品被至少一个处理器执行以实现如权利要求1-8任一项所述的点云编码方法的步骤,或者实现如权利要求9所述的点云解码方法的步骤。
- 一种通信设备,被配置为执行如权利要求1-8任一项所述的点云编码方法的步骤,或者执行如权利要求9所述的点云解码方法的步骤。
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