Transform coefficient encoding method, transform coefficient decoding method, and terminal

Abstract

The embodiment of the application provides a transform coefficient coding method, a transform coefficient decoding method and a terminal, and belongs to the technical field of coding and decoding. The transform coefficient coding method comprises the following steps: obtaining transform coefficients corresponding to a point cloud; the transform coefficients comprise a direct current (DC) coefficient and at least two alternating current (AC) coefficients; encoding the DC coefficient according to a first order corresponding to the DC coefficient and a context probability model corresponding to the DC coefficient; encoding each AC coefficient according to a second order corresponding to each AC coefficient and a context probability model corresponding to each AC coefficient; the second order is determined based on the first order and an index of a coding point corresponding to the AC coefficient; and generating a target code stream based on the encoding result of the DC coefficient and the encoding result of each AC coefficient.

Description

Technical Field

[0001] This application belongs to the field of encoding and decoding technology, specifically relating to a transform coefficient encoding method, a transform coefficient decoding method, and a terminal. Background Technology

[0002] A point cloud is a set of discrete points in space that are randomly distributed and represent the spatial structure and surface attributes of a three-dimensional object or scene. The encoding process of a point cloud involves attribute information encoding. After encoding the attribute information to obtain transform coefficients, these transform coefficients are quantized, and then the quantized transform coefficients are encoded to obtain the bitstream.

[0003] In related technologies, a large number of context probability models are required to encode the transform coefficients, which results in a large number of bits in the encoded bitstream. Summary of the Invention

[0004] This application provides a transform coefficient encoding method, a transform coefficient decoding method, and a terminal, which can solve the problem of a large number of bits in the encoded bitstream.

[0005] Firstly, a transform coefficient encoding method is provided, including:

[0006] The encoding end acquires the transformation coefficients corresponding to the point cloud; the transformation coefficients include DC coefficients and at least two AC coefficients.

[0007] The encoding end encodes the DC coefficients according to the first order corresponding to the DC coefficients and the context probability model corresponding to the DC coefficients;

[0008] The encoding end encodes each AC coefficient according to the second order corresponding to each AC coefficient and the context probability model corresponding to each AC coefficient; the second order is determined based on the first order and the index of the encoding point corresponding to the AC coefficient;

[0009] The encoding end generates the target bitstream based on the encoding results of the DC coefficients and the encoding results of each AC coefficient.

[0010] Secondly, a method for decoding transform coefficients is provided, including:

[0011] The decoding end acquires the target bitstream; the target bitstream includes the encoding result of the DC coefficient in the transform coefficient and the encoding result of at least two AC coefficients in the transform coefficient;

[0012] The decoding end decodes the encoding result of the DC coefficient according to the first order corresponding to the DC coefficient and the context probability model corresponding to the DC coefficient, and obtains the DC coefficient.

[0013] The decoding end decodes the encoding result of each AC coefficient according to the second order corresponding to each AC coefficient and the context probability model corresponding to each AC coefficient, to obtain at least two AC coefficients; the second order is determined based on the first order and the index of the encoding point corresponding to the AC coefficient, or based on the proportion information included in the attribute information parameter set of the target bitstream;

[0014] The decoding end determines the transformation coefficients based on the DC coefficients and the at least two AC coefficients.

[0015] Thirdly, a transform coefficient encoding device is provided, comprising:

[0016] The acquisition module is used to acquire the transformation coefficients corresponding to the point cloud; the transformation coefficients include DC coefficients and at least two AC coefficients;

[0017] The first encoding module is used to encode the DC coefficients according to the first order corresponding to the DC coefficients and the context probability model corresponding to the DC coefficients;

[0018] The second encoding module is used to encode each AC coefficient according to the second order corresponding to each AC coefficient and the context probability model corresponding to each AC coefficient; the second order is determined based on the first order and the index of the encoding point corresponding to the AC coefficient;

[0019] The generation module is used to generate a target bitstream based on the encoding results of the DC coefficients and the encoding results of each AC coefficient.

[0020] Fourthly, a transform coefficient decoding device is provided, comprising:

[0021] The first acquisition module is used to acquire the target code stream; the target code stream includes the encoding result of the DC coefficient in the transform coefficient and the encoding result of at least two AC coefficients in the transform coefficient;

[0022] The first decoding module is used to decode the encoding result of the DC coefficient according to the first order corresponding to the DC coefficient and the context probability model corresponding to the DC coefficient, so as to obtain the DC coefficient.

[0023] The second decoding module is used to decode the encoding result of each AC coefficient according to the second order corresponding to each AC coefficient and the context probability model corresponding to each AC coefficient, to obtain at least two AC coefficients; the second order is determined based on the first order and the index of the encoding point corresponding to the AC coefficient, or based on the proportion information included in the attribute information parameter set of the target bitstream;

[0024] The first determining module is used to determine the transformation coefficients based on the DC coefficients and the at least two AC coefficients.

[0025] Fifthly, a terminal is provided, the terminal including a processor and a memory, the memory storing a program or instructions executable on the processor, the program or instructions, when executed by the processor, implementing the steps of the method as described in the first aspect, or implementing the steps of the method as described in the second aspect.

[0026] In a sixth aspect, a readable storage medium is provided, on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect, or implement the steps of the method described in the second aspect.

[0027] In a seventh aspect, a chip is provided, the chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being configured to run programs or instructions to implement the method as described in the first aspect, or to implement the method as described in the second aspect.

[0028] Eighthly, a computer program / program product is provided, which is stored in a storage medium and is executed by at least one processor to implement the steps of the method as described in the first aspect, or to implement the steps of the method as described in the second aspect.

[0029] In this embodiment, transform coefficients corresponding to the point cloud are obtained; the transform coefficients include DC coefficients and at least two AC coefficients; the DC coefficients are encoded according to the first order corresponding to the DC coefficients and the context probability model corresponding to the DC coefficients; each AC coefficient is encoded according to the second order corresponding to each AC coefficient and the context probability model corresponding to each AC coefficient; the second order is determined based on the first order and the index of the encoding point corresponding to the AC coefficient; a target bitstream is generated based on the encoding results of the DC coefficients and the encoding results of each AC coefficient. In related technologies, the transform coefficients need to be encoded according to the numerical value of the transform coefficients through the context probability model before being encoded again; compared with related technologies, the transform coefficient encoding method provided in this embodiment directly encodes the DC and AC coefficients included in the transform coefficients through the context probability model after obtaining the transform coefficients, thereby achieving transform coefficient encoding with fewer context probability models and reducing the number of bits in the bitstream. Attached Figure Description

[0030] Figure 1 This is a partial schematic diagram of the G-PCC point cloud encoding device;

[0031] Figure 2 This is a partial schematic diagram of the G-PCC point cloud decoding device;

[0032] Figure 3 This is a flowchart illustrating the transform coefficient encoding method provided in an embodiment of this application;

[0033] Figure 4 This is a flowchart illustrating the transform coefficient encoding method provided in an embodiment of this application;

[0034] Figure 5 This is a structural diagram of the transform coefficient encoding device provided in the embodiments of this application;

[0035] Figure 6 This is a structural diagram of the transform coefficient decoding device provided in the embodiments of this application;

[0036] Figure 7 This is a structural diagram of the communication device provided in the embodiments of this application;

[0037] Figure 8 This is a schematic diagram of the hardware structure of the terminal provided in the embodiments of this application. Detailed Implementation

[0038] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0039] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first" and "second" are generally of the same class, not limited in number; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0040] The list building device corresponding to the list building method in this application embodiment can be a terminal, which can also be called a terminal device or user equipment (UE). The terminal can be a mobile phone, tablet computer, laptop computer, personal digital assistant (PDA), handheld computer, netbook, ultra-mobile personal computer (UMPC), mobile internet device (MID), augmented reality (AR) / virtual reality (VR) device, robot, wearable device or vehicle-mounted device (VUE), pedestrian terminal (PUE), smart home (home devices with wireless communication functions, such as refrigerators, televisions, washing machines or furniture, etc.), game console, personal computer (personal Terminal devices such as computers (PCs), ATMs, or self-service machines; wearable devices include smartwatches, smart bracelets, smart earphones, smart glasses, smart jewelry (smart bracelets, smart necklaces, smart anklets, smart ankle chains, etc.), smart wristbands, smart clothing, etc. It should be noted that the embodiments in this application do not limit the specific type of terminal.

[0041] For ease of understanding, the following describes some aspects of the embodiments of this application:

[0042] Please see Figure 1 ,like Figure 1 As shown, in current digital audio and video codec technology standards, Geometry Point Cloud Compression (G-PCC) point cloud encoding devices can be used to encode the attribute information of point clouds. Specifically, the attribute information of the point cloud can be color-converted and recolored. Then, based on the reconstructed geometric information, the recolored attribute information can be subjected to region adaptive transformation based on upsampling prediction, or lifting transformation based on hierarchical structure partitioning, to obtain transformation coefficients. The transformation coefficients are then quantized to obtain quantization coefficients. Finally, the quantization coefficients are arithmetically encoded to obtain the attribute bitstream.

[0043] The above-mentioned operation steps for region adaptive transformation based on upsampling prediction include: constructing a transformation tree structure for the point cloud; then, performing upsampling prediction and Region Adaptive Harm Transform (RAHT) layer by layer from the root node of the transformation tree structure. If the current node is the root node, the attribute information of the node is directly subjected to RAHT transformation to obtain DC coefficients and AC coefficients; if the current node is not the root node, it is determined whether to predict the current node based on the grandparent and parent nodes of the current node. If prediction is required for the current node, the attribute information of the current node is predicted to obtain attribute prediction values, and then RAHT transformation is performed on the attribute prediction values ​​and the original attribute values ​​of the current node respectively to calculate the AC coefficient residuals; if prediction is not required for the current node, the original attribute values ​​of the current node are directly subjected to RAHT transformation to obtain AC coefficients.

[0044] The operation steps of the lifting transformation based on hierarchical structure partitioning mentioned above include: First, the point cloud is hierarchically partitioned by Level of Detail (LoD) to establish the hierarchical structure of the point cloud;

[0045] Then, the bottom-level nodes and nodes at the same level as the current node are used as reference points. The current node searches within the reference points and selects the nearest multiple reference points as prediction reference points. The reconstructed attribute values ​​of these multiple prediction reference points are used for linear interpolation prediction. The predicted values ​​are then subjected to lifting transformation.

[0046] It should be understood that the above arithmetic coding includes zero-run coding and transform coefficient coding. First, zero-run coding is performed on the quantized transform coefficients, and then transform coefficient coding is performed on the transform coefficients to generate the bitstream. The implementation process of zero-run coding is briefly described below: (1) The context probability model 1 is used to determine whether the run (length) value of the transform coefficient is 0. If it is 0, a 0 is encoded and the current coding ends; if it is not 0, a 1 is encoded and the following judgment is performed.

[0047] (2) Determine whether the run value of the transformation coefficient is 1 by using the context probability model 2. If it is 1, encode one bit 0 and end the current encoding; if it is not 1, encode one bit 1 and continue to make the following judgment.

[0048] (3) Determine whether the run value of the transformation coefficient is 2 by using the context probability model 3. If it is 2, encode one bit 0 and end the current encoding; if it is not 2, encode one bit 1 and continue to make the following judgment.

[0049] (4) Update the run value by subtracting 3 from the run value to obtain the updated run value.

[0050] (5) Divide the run value by 2 to obtain the prefix value;

[0051] (6) Encode the prefix value using a unary code using context probability model 4;

[0052] Check if the prefix value is 0. If it is 0, encode one bit of 0 and use bypass encoding to encode the remainder of the run value taken as 2. If it is not 0, encode one bit of 1 and continue to perform the following checks.

[0053] Check if the prefix value is 1. If it is 1, encode one bit of 0 and use bypass encoding to encode the remainder of the run value taken as 2. If it is not 1, encode one bit of 1 and continue to perform the following checks.

[0054] Determine if the prefix value is 2. If it is 2, encode one bit of 0 and use bypass encoding to encode the remainder of the run value taken from 2. If it is not 2, encode one bit of 1 and continue with the following judgment.

[0055] Determine if the prefix value is 3. If it is 3, encode one bit of 0 and use bypass encoding to encode the remainder of the run value taken as 2. If it is not 3, encode one bit of 1 and end the current encoding.

[0056] (7) Update the run value by subtracting 8 from the run value to obtain the updated run value.

[0057] (8) Update the run value using second-order exponential Golomb coding through context probability model 5.

[0058] The following is a brief description of the implementation process of transform coefficient encoding:

[0059] (1) Determine whether the value of the transformation coefficient is equal to 0 by using context probability model 1. If it is 0, end the encoding; if it is not 0, encode 1 and continue the following encoding.

[0060] (2) Determine whether the value of the transformation coefficient is equal to 1 by using context probability model 1. If it is 1, end the encoding; if it is not 1, encode 1 and continue the following encoding.

[0061] (3) The prefix code of the bitstream is generated by encoding the first set of context probability models (including context probability model 3, context probability model 4 and context probability model 5), and the suffix code of the bitstream is generated by encoding the second set of context probability models (including context probability model 6, context probability model 7 and context probability model 8).

[0062] Please see Figure 2 ,like Figure 2As shown, in current digital audio and video codec technology standards, Geometry Point Cloud Compression (G-PCC) point cloud decoding devices can be used to decode the attribute information of point clouds. It should be understood that the implementation process of decoding the attribute information of point clouds using the aforementioned G-PCC point cloud decoding device is the reverse of its encoding process, and will not be repeated here. From the above, it can be seen that in the process of transform coefficient encoding, it is necessary to first encode the transform coefficients based on their values ​​using context probability model 1 and context probability model 2, and then encode the transform coefficients using two sets of context probability models. This requires using a large number of context probability models to encode the transform coefficients, resulting in a large number of bits in the generated bitstream.

[0063] To address the aforementioned technical problems, this application provides a transform coefficient encoding method that can be applied to the encoding end. The transform coefficient encoding method provided in this application will be described in detail below with reference to the accompanying drawings and through some embodiments and application scenarios.

[0064] Please see Figure 3 , Figure 3 This is a flowchart of the transform coefficient encoding method in this embodiment. The transform coefficient encoding method provided in this embodiment includes the following steps:

[0065] S301, the encoder obtains the transformation coefficients corresponding to the point cloud.

[0066] The transformation coefficients mentioned above are the transformation coefficients corresponding to the point cloud, that is, the quantized transformation coefficients obtained by performing region adaptive transformation based on upsampling prediction or lifting transformation based on hierarchical structure partitioning on the attribute information of the point cloud. Among them, the transformation coefficients include DC coefficients and AC coefficients.

[0067] It should be understood that attribute information includes color information and reflectance information. If the transformation coefficients are obtained by performing an adaptive region transformation based on upsampling prediction or a lifting transformation based on hierarchical structure partitioning, then the transformation coefficients are determined based on color information. The aforementioned transformation coefficients include Y, U, and V components.

[0068] If the transformation coefficients are obtained by performing an adaptive regional transformation based on upsampling prediction using reflectance information, or by performing an uplift transformation based on hierarchical structure partitioning, then the transformation coefficients are determined based on reflectance information.

[0069] In this step, the encoder obtains the transformation coefficients corresponding to the point cloud, wherein the transformation coefficients include DC coefficients and at least two AC coefficients.

[0070] S302, the encoding end encodes the DC coefficients according to the first order corresponding to the DC coefficients and the context probability model corresponding to the DC coefficients.

[0071] In this step, the encoder encodes the DC coefficients according to the context probability model corresponding to the first order and the DC coefficients. For specific implementation details, please refer to the following embodiments.

[0072] S303, the encoding end encodes each AC coefficient according to the second order corresponding to each AC coefficient and the context probability model corresponding to each AC coefficient.

[0073] In this step, the second order can be determined based on the index of the encoding point corresponding to the first order and the AC coefficient, and each AC coefficient can be encoded according to the second order and the context probability model corresponding to each AC coefficient.

[0074] S304, the encoding end generates the target bitstream based on the encoding results of the DC coefficients and the encoding results of each AC coefficient.

[0075] After encoding the DC coefficients, the encoder obtains a binary array, which can be understood as the encoding result of the DC coefficients. Similarly, after encoding each AC coefficient, the encoder obtains a binary array, which can also be understood as the encoding result of the AC coefficients. In other words, the encoder generates the target bitstream by encoding both the DC and AC coefficients in a single encoding process.

[0076] In related technologies, the transform coefficients need to be encoded after being encoded according to the numerical value of the transform coefficients through a context probability model. Compared with related technologies, the transform coefficient encoding method provided in this application directly encodes the DC coefficients and AC coefficients included in the transform coefficients through a context probability model after obtaining the transform coefficients. In this way, the transform coefficients can be encoded with fewer context probability models, thereby reducing the number of bits in the bitstream.

[0077] Optionally, the DC coefficient is determined based on color information, and before encoding the DC coefficient according to the first order corresponding to the DC coefficient and the context probability model corresponding to the DC coefficient, the method further includes:

[0078] The first order corresponding to the DC coefficient is determined based on the Columbus order corresponding to at least some of the components of the DC coefficient.

[0079] In this embodiment, if the transformation coefficients are obtained by performing region adaptive transformation based on upsampling prediction of color information, or by performing lifting transformation based on hierarchical structure partitioning, the first order corresponding to the DC coefficients can be determined based on the Columbus order corresponding to at least some components of the DC coefficients.

[0080] Optionally, the Columbus orders corresponding to the Y, U, and V components of the DC coefficients are obtained, and the Columbus order corresponding to any component is determined as the first order.

[0081] Optionally, determining the first order corresponding to the DC coefficients based on the Columbus order corresponding to at least some components of the DC coefficients includes:

[0082] Obtain the Columbus order corresponding to each component of the DC coefficient;

[0083] The Columbus order with the highest frequency among the Columbus orders corresponding to each component is determined as the first order corresponding to the DC coefficient.

[0084] In this embodiment, the Columbus orders corresponding to the Y, U, and V components of the DC coefficients can be obtained. For example, the logarithm of log2 of the Y component of the DC coefficients can be taken to obtain the Columbus order corresponding to the Y component. Then, the Columbus order with the highest frequency among the three Columbus orders is determined as the first order.

[0085] In one embodiment, if the Columbus order corresponding to each component has the same frequency of occurrence, then the Columbus order corresponding to any component can be determined as the first order.

[0086] Optionally, determining the first order corresponding to the DC coefficients based on the Columbus order corresponding to at least some components of the DC coefficients includes:

[0087] Obtain the Columbus order corresponding to each component of the DC coefficient;

[0088] The average value of the Columbus order corresponding to each component of the DC coefficient is taken as the integer part to obtain the first order corresponding to the DC coefficient.

[0089] In this embodiment, after obtaining the Columbus orders corresponding to the Y, U, and V components of the DC coefficients, the average of these three Columbus orders is rounded down to obtain the first order.

[0090] Optionally, encoding the DC coefficients based on the first order corresponding to the DC coefficients and the context probability model corresponding to the DC coefficients includes:

[0091] The DC coefficients are Columbus encoded using the first set of context probability models and the second set of context probability models.

[0092] In this embodiment, if the DC coefficient is obtained by performing a region adaptive transformation based on upsampling prediction or a lifting transformation based on hierarchical structure partitioning from reflectance information, then the DC coefficient is Columbus encoded using the first set of context probability models and the second set of context probability models, wherein the encoding order of the DC coefficient is the same as the first order.

[0093] If the DC coefficients are obtained by performing region adaptive transformation based on upsampling prediction of color information, or by performing lifting transformation based on hierarchical structure partitioning, then the U and V components in the DC coefficients are Golomb encoded using the first set of context probability models, and the Y component in the DC coefficients is Golomb encoded using the second set of context probability models. The encoding order of the DC coefficients using Golomb encoding is the same as the first order.

[0094] It should be noted that the first group of context probability models mentioned above includes multiple context probability models, and optionally, the first group of context probability models includes three context probability models; the second group of context probability models mentioned above includes multiple context probability models, and optionally, the second group of context probability models includes three context probability models.

[0095] It should be noted that the multiple context probability models included in the second set of context probability models are different from those included in the first set of context probability models. The DC coefficients are encoded using the first set of context probability models to generate the prefix code corresponding to the DC coefficients, and the DC coefficients are encoded using the second set of context probability models to generate the suffix code corresponding to the DC coefficients.

[0096] In this embodiment, the DC coefficients are subjected to high-order exponential Golomb coding based on the first order, the first set of context probability models, and the second set of context probability models, thereby reducing the number of bits in the generated bitstream.

[0097] Optionally, before encoding each AC coefficient based on the second order corresponding to each AC coefficient and the context probability model corresponding to each AC coefficient, the method further includes:

[0098] The difference between the first order and the first value is determined as the second order;

[0099] In this embodiment, the second order can be determined using the following formula:

[0100]

[0101] in, Indicates the second order. This represents the first order, and n represents the index of the encoding point. Indicates the total number of coding points. This indicates the preset amplitude control parameters.

[0102] Optionally, the DC coefficient is determined based on color information, and the DC coefficient includes Y, U, and V components. Encoding the DC coefficient according to the first order corresponding to the DC coefficient and the context probability model corresponding to the DC coefficient includes:

[0103] Based on the first order corresponding to the DC coefficient, determine the coding order for each component of the DC coefficient to be Columbus encoded;

[0104] Based on the encoding order, each component of the DC coefficient is Columbus encoded using the first set of context probability models and the second set of context probability models.

[0105] In this embodiment, if the DC coefficient is obtained by performing a region adaptive transformation based on upsampling prediction of color information, or by performing a lifting transformation based on hierarchical structure partitioning, then after determining the first order corresponding to the DC coefficient, each component of the DC coefficient can be Columbus encoded using the first set of context probability models and the second set of context probability models.

[0106] One possible scenario is that the first order of the DC coefficients is greater than the third preset value, but less than or equal to the fourth preset value. In this case, the coding order of the Y component in the DC coefficients is the first order, and the coding order of the U and V components in the DC coefficients is 1.

[0107] Another possible scenario is that the first order of the DC coefficients is greater than the fourth preset value. In this case, the coding order of the U and V components in the DC coefficients using Golomb coding is the difference between the first order and the fourth preset value, and the coding order of the Y component in the DC coefficients using Golomb coding is 1.

[0108] Another possible scenario is that the first order corresponding to the DC coefficient is less than or equal to the third preset value. In this case, the coding order of Golomb coding for each component of the DC coefficient is 1.

[0109] Optionally, the third preset value is 1 and the fourth preset value is 2.

[0110] In this embodiment, at least some components in the DC coefficients are subjected to high-order exponential Golomb coding based on the first order, the first set of context probability models, and the second set of context probability models, thereby reducing the number of bits in the encoded bitstream.

[0111] Optionally, the AC coefficients are determined based on color information, and the AC coefficients include Y, U, and V components. Encoding each AC coefficient according to its second-order coefficient and the context probability model includes:

[0112] Based on the second order corresponding to each AC coefficient, determine the coding order for each component of the AC coefficient to be Columbus encoded;

[0113] Based on the encoding order, each component of the AC coefficients is Columbus encoded using the first set of context probability models and the second set of context probability models.

[0114] As described above, the first set of context probability models includes multiple context probability models, and optionally, the context probability models include three context probability models; the second set of context probability models includes multiple context probability models, and optionally, the second set of context probability models includes three context probability models.

[0115] It should be noted that the multiple context probability models included in the second set of context probability models are different from those included in the first set of context probability models. The AC coefficients are encoded using the first set of context probability models to generate the prefix code corresponding to the AC coefficients, and the AC coefficients are encoded using the second set of context probability models to generate the suffix code corresponding to the AC coefficients.

[0116] In this embodiment, if the AC coefficients are obtained by performing region adaptive transformation based on upsampling prediction of color information, or by performing lifting transformation based on hierarchical structure partitioning, then each component of the AC coefficients can be Columbus encoded using the first set of context probability models and the second set of context probability models.

[0117] One possible scenario is that the second order of the AC coefficients is greater than the third preset value, but less than or equal to the fourth preset value. In this case, the coding order of the Y component in the AC coefficients using Golomb coding is the second order, and the coding order of the U and V components in the AC coefficients using Golomb coding is 1.

[0118] Another possible scenario is that the second order of the AC coefficients is greater than the fourth preset value. In this case, the coding order of the U and V components in the AC coefficients using Golomb coding is the difference between the second order and the fourth preset value, and the coding order of the Y component in the AC coefficients using Golomb coding is 1.

[0119] Another possible scenario is that the second order corresponding to the AC coefficients is less than or equal to the third preset value. In this case, the coding order of Golomb coding for each component of the AC coefficients is 1.

[0120] Optionally, the third preset value is 1 and the fourth preset value is 2.

[0121] In this embodiment, at least some components of the AC coefficients are subjected to high-order exponential Golomb coding based on the second order, the first set of context probability models, and the second set of context probability models, thereby reducing the number of bits in the encoded bitstream.

[0122] Optionally, the AC coefficients are determined based on color information, and the AC coefficients include Y, U, and V components. Encoding each AC coefficient according to its second-order coefficient and the context probability model includes:

[0123] The U and V components in the AC coefficients are Golombian encoded using the first set of context probability models.

[0124] The Y component in the AC coefficients is Golombian-coded using the second set of contextual probability models.

[0125] In this embodiment, the U and V components in the AC coefficients are Golomb encoded using the first set of context probability models, and the Y component in the AC coefficients is Golomb encoded using the second set of context probability models. The encoding order of the AC coefficients using Golomb encoding is the same as the second order.

[0126] It should be noted that the multiple context probability models included in the second set of context probability models are different from those included in the first set of context probability models. The AC coefficients are encoded using the first set of context probability models to generate the prefix code corresponding to the AC coefficients, and the AC coefficients are encoded using the second set of context probability models to generate the suffix code corresponding to the AC coefficients.

[0127] In this embodiment, the AC coefficients are subjected to high-order exponential Golomb coding based on the second order, the first set of context probability models, and the second set of context probability models, thereby reducing the number of bits in the generated bitstream.

[0128] Optionally, the AC coefficients are determined based on reflectivity information, and encoding each AC coefficient according to the second order corresponding to each AC coefficient and the context probability model corresponding to each AC coefficient includes:

[0129] Each AC coefficient is Columbus encoded using the first set of context probability models and the second set of context probability models.

[0130] As described above, the first set of context probability models includes multiple context probability models, optionally including three context probability models; the second set of context probability models includes multiple context probability models, optionally including three context probability models. The multiple context probability models included in the second set of context probability models are different from the multiple context probability models included in the first set of context probability models.

[0131] In this embodiment, if the AC coefficients are obtained by performing region adaptive transformation based on upsampling prediction or by performing lifting transformation based on hierarchical structure partitioning on reflectance information, then the AC coefficients can be Columbus encoded using the first set of context probability models and the second set of context probability models.

[0132] One possible scenario is that the second order corresponding to the AC coefficients is greater than the fifth preset value. In this case, the coding order of the AC coefficients using Golomb coding is the same as the second order.

[0133] Another possible scenario is that the second order corresponding to the AC coefficients is less than or equal to the fifth preset value. In this case, the coding order of the AC coefficients using Golomb coding is 1.

[0134] Optionally, the fifth preset value mentioned above is 1.

[0135] In this embodiment, the AC coefficients are subjected to high-order exponential Golomb coding based on the second order, the first set of context probability models, and the second set of context probability models, thereby reducing the number of bits in the generated bitstream.

[0136] Optionally, the initial probability of the context probability model is a preset probability value.

[0137] It should be understood that in related technologies, the initial probability of the context probability model is 0.5, that is, the number of bits required to encode 1 and the number of bits required to encode 0 are the same. This makes the convergence speed of the arithmetic coding process slow and the coding efficiency low.

[0138] In this embodiment, the user can customize the initial probability of the context probability model by modifying the relevant parameters of the encoder. This allows the user to set an initial probability that conforms to the data distribution characteristics based on the data distribution pattern of the point cloud, which facilitates faster convergence of arithmetic coding and improves coding efficiency.

[0139] Optionally, the attribute information parameter set of the target bitstream includes the first order and the scale information, wherein the scale information is used to determine the second order.

[0140] Optionally, the first order mentioned above can be adopted. This parameter represents the target bitstream's attribute information parameter set. In this embodiment, this parameter is passed into the set of attribute information parameters to facilitate the decoding end in decoding the transform coefficients based on this parameter. Simultaneously, proportional information can also be passed into the attribute information parameter set. This proportional information represents the range of the Columbus order, and the decoding end can determine the second order using this proportional information.

[0141] Optionally, encoding the DC coefficients based on the first order corresponding to the DC coefficients and the context probability model corresponding to the DC coefficients includes:

[0142] Obtain the target identifier;

[0143] When the target identifier is the target value, the DC coefficient is encoded according to the first order corresponding to the DC coefficient and the context probability model corresponding to the DC coefficient.

[0144] In this embodiment, the target identifier can also be obtained from the attribute information parameter set. If the target identifier is a target value, it indicates that the encoding end uses the transform coefficient encoding method provided in this embodiment, that is, it executes the first order corresponding to the DC coefficient and the context probability model corresponding to the DC coefficient to encode the DC coefficient. If the target identifier is not a target value, it indicates that the encoding end uses the transform coefficient encoding method in related technologies.

[0145] Optionally, the target value is 1, and the target identifier can be represented as adaptiveExpGolombFlag. For a more intuitive understanding of the technical effects of the transform coefficient encoding provided in the embodiments of this application, please refer to Tables 1 and 2.

[0146] Table 1:

[0147]

[0148] Table 2:

[0149]

[0150] The values ​​in Tables 1 and 2 are used to characterize the bitrate ratio between the bitstream generated by the transform coefficient encoding provided in the embodiments of this application and the bitstream generated using related technologies, under the condition of the same peak signal-to-noise ratio. It should be understood that the lower the value, the greater the reduction in bitrate.

[0151] For example, "-0.1%" in the second row and second column of Table 1 indicates that the bitstream corresponding to the Y component generated by the transform coefficient encoding provided in this application embodiment has a reduced bitrate of 0.1% compared to the bitstream corresponding to the Y component generated using related technologies.

[0152] Please see Figure 4 , Figure 4This is a flowchart illustrating the transform coefficient decoding method provided in this embodiment. The transform coefficient decoding method provided in this embodiment includes the following steps:

[0153] S401, the decoding end obtains the target bitstream.

[0154] The target bitstream mentioned above includes the encoding results of the DC coefficients in the transform coefficients and the encoding results of at least two AC coefficients in the transform coefficients.

[0155] S402, the decoding end decodes the encoding result of the DC coefficient according to the first order corresponding to the DC coefficient and the context probability model corresponding to the DC coefficient to obtain the DC coefficient.

[0156] In this step, the decoding end can decode the encoding result of the DC coefficient using the context probability model corresponding to the first order and the DC coefficient.

[0157] S403, the decoding end decodes the encoding result of each AC coefficient according to the second order corresponding to each AC coefficient and the context probability model corresponding to each AC coefficient, and obtains at least two AC coefficients.

[0158] In this step, the decoding end can determine the second order based on the index of the coding point corresponding to the first order and AC coefficients, or based on the proportion information included in the attribute information parameter set of the target bitstream. For specific implementation methods, please refer to the following embodiments.

[0159] The decoding end decodes the encoding result of each AC coefficient based on the second order and the context probability model corresponding to each AC coefficient.

[0160] S404, the decoding end determines the transformation coefficients based on the DC coefficients and the at least two AC coefficients.

[0161] It should be understood that the transform coefficient decoding method provided in this embodiment is the inverse process of the transform coefficient encoding method described above, and will not be repeated here.

[0162] Optionally, before decoding the encoding result of the DC coefficients based on the first order corresponding to the DC coefficients and the context probability model corresponding to the DC coefficients, the method further includes:

[0163] The first order is obtained from the attribute information parameter set of the target bitstream.

[0164] As mentioned above, since the encoding end passes the first order into the attribute information parameter, the decoding end can obtain the first order from the attribute information parameter set.

[0165] Optionally, decoding the encoding result of the DC coefficients based on the first order corresponding to the DC coefficients and the context probability model corresponding to the DC coefficients includes:

[0166] The encoding results of the DC coefficients are Columbus decoded using the first set of context probability models and the second set of context probability models.

[0167] It should be understood that the decoding order of the Columbus decoding of the encoded DC coefficients is the same as that of the first order. As mentioned above, the first set of context probability models includes multiple context probability models, optionally including three context probability models; the second set of context probability models also includes multiple context probability models, optionally including three context probability models. The multiple context probability models included in the second set of context probability models are different from the multiple context probability models included in the first set of context probability models.

[0168] Optionally, before decoding the encoding result of each AC coefficient based on the second order corresponding to each AC coefficient and the context probability model corresponding to each AC coefficient, the method further includes:

[0169] The difference between the first order and the first value is determined as the second order.

[0170] In one alternative implementation, the decoding end can substitute the first order into the calculation formula involved in the encoding end to determine the second order. The method by which the decoding end calculates the second order is the same as that of the encoding end, and will not be repeated here.

[0171] Optionally, before decoding the encoding result of each AC coefficient based on the second order corresponding to each AC coefficient and the context probability model corresponding to each AC coefficient, the method further includes:

[0172] Obtain the ratio information from the attribute information parameter set of the target bitstream;

[0173] The second order is determined based on the ratio information and the preset total number of coding points.

[0174] Another alternative implementation is that the decoding end obtains the ratio information from the attribute information parameter set. As mentioned above, the ratio information is used to characterize the range of the Columbus order, and then the second order can be determined according to the relationship between the ratio information and the total number of preset coding points.

[0175] Optionally, before decoding the encoding result of each AC coefficient based on the second order corresponding to each AC coefficient and the context probability model corresponding to each AC coefficient, the method further includes:

[0176] The ratio information is determined based on the ratio between the index of the coding point corresponding to the AC coefficient and the preset total number of coding points;

[0177] The second order is determined based on the ratio information and the total number of coding points.

[0178] In this embodiment, the encoding end does not need to transmit ratio information. The decoding end determines the ratio information based on the ratio between the index of the encoding point corresponding to the AC coefficient and the preset total number of encoding points. Then, based on the ratio information and the total number of encoding points, the second order is determined.

[0179] Optionally, the DC coefficient is determined based on color information, and the DC coefficient includes Y, U, and V components. Decoding the encoding result of the DC coefficient according to the first order corresponding to the DC coefficient and the context probability model corresponding to the DC coefficient includes:

[0180] Based on the first order corresponding to each DC coefficient, determine the decoding order for Columbus decoding of each component of the DC coefficient;

[0181] Based on the decoding order, the encoding results of the DC coefficients are subjected to Columbus decoding through the first context probability model and the second context probability model.

[0182] In this embodiment, if the DC coefficient is obtained by performing a region adaptive transformation based on upsampling prediction of color information, or by performing a lifting transformation based on hierarchical structure partitioning, then the encoding result of the DC coefficient can be Columbus decoded through the first set of context probability models and the second set of context probability models.

[0183] One possible scenario is that the first order of the DC coefficients is greater than the third preset value, but less than or equal to the fourth preset value. In this case, the decoding order of the Y component in the DC coefficients is the first order, and the decoding order of the U and V components in the DC coefficients is 1.

[0184] Another possible scenario is that the first order of the DC coefficients is greater than the fourth preset value. In this case, the decoding order of the U and V components in the DC coefficients is the difference between the first order and the fourth preset value, and the decoding order of the Y component in the DC coefficients is 1.

[0185] Another possible scenario is that the first order corresponding to the DC coefficients is less than or equal to the third preset value. In this case, the decoding order for each component of the DC coefficients using Columbus decoding is 1.

[0186] Optionally, the AC coefficients are determined based on color information, and the AC coefficients include Y, U, and V components. Decoding the encoding result of each AC coefficient according to the second order corresponding to each AC coefficient and the context probability model corresponding to each AC coefficient includes:

[0187] Based on the second order corresponding to each AC coefficient, determine the decoding order for Columbus decoding of the encoded result of the AC coefficient;

[0188] Based on the decoding order, the encoding results of the AC coefficients are subjected to Columbus decoding through the first set of context probability models and the second set of context probability models.

[0189] In this embodiment, if the AC coefficients are obtained by performing region adaptive transformation based on upsampling prediction of color information, or by performing lifting transformation based on hierarchical structure partitioning, then each component of the AC coefficients can be Columbus decoded using the first set of context probability models and the second set of context probability models.

[0190] One possible scenario is that the second order of the AC coefficients is greater than the third preset value, but less than or equal to the fourth preset value. In this case, the decoding order of the Y component in the AC coefficients is the second order, and the decoding order of the U and V components in the AC coefficients is 1.

[0191] Another possible scenario is that the second order of the AC coefficients is greater than the fourth preset value. In this case, the decoding order of the U and V components in the AC coefficients is the difference between the second order and the fourth preset value, and the decoding order of the Y component in the AC coefficients is 1.

[0192] Another possible scenario is that the second order corresponding to the AC coefficients is less than or equal to the third preset value. In this case, the decoding order for each component of the AC coefficients using Columbus decoding is 1.

[0193] Optionally, the third preset value is 1 and the fourth preset value is 2.

[0194] Optionally, the AC coefficients are determined based on color information, and the AC coefficients include Y, U, and V components. Decoding the encoding result of each AC coefficient according to the second order corresponding to each AC coefficient and the context probability model corresponding to each AC coefficient includes:

[0195] The encoding results of the AC coefficients are Columbus decoded using the first set of context probability models and the second set of context probability models.

[0196] It should be understood that the decoding order of the Columbus decoding of the encoded AC coefficients is the same as that of the second order.

[0197] Optionally, the AC coefficients are determined based on reflectivity information, and the decoding of the encoding result of each AC coefficient according to the second order corresponding to each AC coefficient and the context probability model corresponding to each AC coefficient includes:

[0198] Columbus decoding is performed on the encoding results of each AC coefficient using the first set of context probability models and the second set of context probability models.

[0199] In this embodiment, if the AC coefficients are obtained by performing a region adaptive transformation based on upsampling prediction or a lifting transformation based on hierarchical structure partitioning from reflectance information, then the encoding results of the AC coefficients can be Columbus decoded using the first set of context probability models and the second set of context probability models.

[0200] One possible scenario is that the second order corresponding to the AC coefficients is greater than the fifth preset value. In this case, the decoding order of the Columbus decoding of the encoded AC coefficients is the same as the second order.

[0201] Another possible scenario is that the second order corresponding to the AC coefficients is less than or equal to the fifth preset value. In this case, the decoding order of the Columbus decoding of the encoded AC coefficients is 1.

[0202] Optionally, the fifth preset value mentioned above is 1.

[0203] Optionally, the initial probability of the context probability model is a preset probability value.

[0204] In this embodiment, the initial probability of the context probability model can be customized. This allows for setting an initial probability that conforms to the data distribution characteristics based on the data distribution patterns of the point cloud, facilitating faster convergence of arithmetic decoding and improving decoding efficiency.

[0205] Optionally, the step of decoding the encoding result of the DC coefficients based on the first order corresponding to the DC coefficients and the context probability model corresponding to the DC coefficients to obtain the DC coefficients includes:

[0206] Obtain the target identifier;

[0207] When the target identifier is the target value, the encoding result of the DC coefficient is decoded according to the first order corresponding to the DC coefficient and the context probability model corresponding to the DC coefficient to obtain the DC coefficient.

[0208] In this embodiment, if the decoding end obtains a target identifier in the attribute information parameter set, and the target identifier is a target value, it indicates that the decoding end uses the transform coefficient decoding method provided in this application embodiment, that is, it executes the step of decoding the encoding result of the DC coefficient according to the first order corresponding to the DC coefficient and the context probability model corresponding to the DC coefficient. If the target identifier is not a target value, it indicates that the decoding end uses the transform coefficient decoding method in related technologies.

[0209] Optionally, the target value is 1, and the target identifier can be represented as adaptiveExpGolombFlag.

[0210] The transform coefficient encoding method provided in this application can be executed by a transform coefficient encoding device. This application uses an example of a transform coefficient encoding device executing the transform coefficient encoding method to illustrate the transform coefficient encoding device provided in this application.

[0211] like Figure 5 As shown in the figure, this application embodiment also provides a transform coefficient encoding device 500, including:

[0212] The acquisition module 501 is used to acquire the transformation coefficients corresponding to the point cloud; the transformation coefficients include DC coefficients and at least two AC coefficients.

[0213] The first encoding module 502 is used to encode the DC coefficients according to the first order corresponding to the DC coefficients and the context probability model corresponding to the DC coefficients;

[0214] The second encoding module 503 is used to encode each AC coefficient according to the second order corresponding to each AC coefficient and the context probability model corresponding to each AC coefficient; the second order is determined based on the first order and the index of the encoding point corresponding to the AC coefficient.

[0215] The generation module 504 is used to generate a target bitstream based on the encoding results of the DC coefficients and the encoding results of each AC coefficient.

[0216] Optionally, the DC coefficient is determined based on color information, and the transform coefficient encoding device 500 further includes:

[0217] The first determining module is used to determine the first order corresponding to the DC coefficient based on the Columbus order corresponding to at least some components of the DC coefficient.

[0218] Optionally, the first determining module is specifically used for:

[0219] Obtain the Columbus order corresponding to each component of the DC coefficient;

[0220] The Columbus order with the highest frequency among the Columbus orders corresponding to each component is determined as the first order corresponding to the DC coefficient.

[0221] Optionally, the first determining module is further specifically used for:

[0222] Obtain the Columbus order corresponding to each component of the DC coefficient;

[0223] The average value of the Columbus order corresponding to each component of the DC coefficient is taken as the integer part to obtain the first order corresponding to the DC coefficient.

[0224] Optionally, the first encoding module 502 is specifically used for:

[0225] The DC coefficients are encoded using the first set of context probability models and the second set of context probability models;

[0226] Wherein, the coding order of the DC coefficients using Golomb coding is the same as the first order;

[0227] The first group of context probability models includes multiple context probability models, the second group of context probability models includes multiple context probability models, and the multiple context probability models included in the second group of context probability models are different from the multiple context probability models included in the first group of context probability models.

[0228] Optionally, the transform coefficient encoding device 500 further includes:

[0229] The second determining module is used to determine the difference between the first order and the first value as the second order;

[0230] Wherein, the first value is the product of the second value and the preset amplitude control parameter, the second value is the result of performing a logarithmic calculation on the third value, the third value is the maximum value between the first preset value and the fourth value, the fourth value is the result of multiplying the fifth value and the second preset value, and the fifth value is the result of dividing the index of the coding point by the total number of coding points.

[0231] Optionally, the DC coefficient is determined based on color information, and the DC coefficient includes Y, U, and V components. The first encoding module 502 is further specifically used for:

[0232] Based on the first order corresponding to the DC coefficient, determine the coding order for each component of the DC coefficient to be Columbus encoded;

[0233] Based on the encoding order, each component of the DC coefficient is Columbus encoded using the first set of context probability models and the second set of context probability models.

[0234] Wherein, if the first order corresponding to the DC coefficient is greater than a third preset value and less than or equal to a fourth preset value, the coding order of the Y component in the DC coefficient using Golomb coding is the first order, and the coding order of the U and V components in the DC coefficient using Golomb coding is 1; or,

[0235] When the first order corresponding to the DC coefficient is greater than the fourth preset value, the coding order of the U and V components in the DC coefficient using Golomb coding is the difference between the first order and the fourth preset value, and the coding order of the Y component in the DC coefficient using Golomb coding is 1; or,

[0236] When the first order corresponding to the DC coefficient is less than or equal to the third preset value, the coding order of each component of the DC coefficient using Columbus coding is 1.

[0237] The first group of context probability models includes multiple context probability models, the second group of context probability models includes multiple context probability models, and the multiple context probability models included in the second group of context probability models are different from the multiple context probability models included in the first group of context probability models.

[0238] Optionally, the AC coefficients are determined based on color information, and the AC coefficients include Y, U, and V components. The second encoding module 503 is specifically used for:

[0239] Based on the second order corresponding to each AC coefficient, determine the coding order for each component of the AC coefficient to be Columbus encoded;

[0240] Based on the encoding order, each component of the AC coefficient is Columbus encoded using the first set of context probability models and the second set of context probability models.

[0241] Wherein, if the second order corresponding to the AC coefficients is greater than a third preset value and less than or equal to a fourth preset value, the coding order of the Y component in the AC coefficients using Golomb coding is the second order, and the coding order of the U and V components in the AC coefficients using Golomb coding is 1; or,

[0242] When the second order corresponding to the AC coefficients is greater than the fourth preset value, the coding order of the U and V components in the AC coefficients using Golomb coding is the difference between the second order and the fourth preset value, and the coding order of the Y component in the AC coefficients using Golomb coding is 1; or,

[0243] When the second order corresponding to the AC coefficient is less than or equal to the third preset value, the coding order of each component of the AC coefficient using Columbus coding is 1.

[0244] The first group of context probability models includes multiple context probability models, the second group of context probability models includes multiple context probability models, and the multiple context probability models included in the second group of context probability models are different from the multiple context probability models included in the first group of context probability models.

[0245] Optionally, the AC coefficients are determined based on color information, and the AC coefficients include Y, U, and V components. The second encoding module 503 is further specifically used for:

[0246] The U and V components in the AC coefficients are Columbus encoded using the first set of context probability models; the coding order of the U and V components is the same as the second order, and the first set of context probability models includes multiple context probability models.

[0247] The Y component in the AC coefficients is Golomb encoded using the second set of context probability models. The encoding order of the Y component is the same as the second order. The second set of context probability models includes multiple context probability models, and the multiple context probability models included in the second set of context probability models are different from the multiple context probability models included in the first set of context probability models.

[0248] Optionally, the AC coefficient is determined based on reflectivity information, and the second encoding module 503 is further specifically used for:

[0249] Each AC coefficient is Columbus encoded using the first set of context probability models and the second set of context probability models.

[0250] Wherein, if the second order corresponding to the AC coefficient is greater than the fifth preset value, the coding order of the AC coefficient using Golomb coding is the same as the second order; or,

[0251] When the second order corresponding to the AC coefficient is less than or equal to the fifth preset value, the coding order of the AC coefficient using Columbus coding is 1.

[0252] The first group of context probability models includes multiple context probability models, the second group of context probability models includes multiple context probability models, and the multiple context probability models included in the second group of context probability models are different from the multiple context probability models included in the first group of context probability models.

[0253] Optionally, the initial probability of the context probability model is a preset probability value.

[0254] Optionally, the attribute information parameter set of the target bitstream includes the first order and the scale information, wherein the scale information is used to determine the second order.

[0255] Optionally, the first encoding module 502 is further specifically used for:

[0256] Obtain the target identifier;

[0257] When the target identifier is the target value, the DC coefficient is encoded according to the first order corresponding to the DC coefficient and the context probability model corresponding to the DC coefficient.

[0258] In related technologies, the transform coefficients need to be encoded after being encoded according to the numerical value of the transform coefficients through a context probability model. Compared with related technologies, the transform coefficient encoding method provided in this application directly encodes the DC coefficients and AC coefficients included in the transform coefficients through a context probability model after obtaining the transform coefficients. In this way, the transform coefficients can be encoded with fewer context probability models, thereby reducing the number of bits in the bitstream.

[0259] This device embodiment is the same as the one described above. Figure 3 The transformation coefficient encoding method embodiment shown above corresponds to the various implementation processes and methods of the encoding end in the above method embodiment, which can be applied to this device embodiment and achieve the same technical effect.

[0260] The transform coefficient decoding method provided in this application can be executed by a transform coefficient decoding device. This application uses an example of a transform coefficient decoding device executing the transform coefficient decoding method to illustrate the transform coefficient decoding device provided in this application.

[0261] like Figure 6 As shown in the figure, this application embodiment also provides a transform coefficient decoding device 600, including:

[0262] The first acquisition module 601 is used to acquire the target code stream; the target code stream includes the encoding result of the DC coefficient in the transform coefficient and the encoding result of at least two AC coefficients in the transform coefficient;

[0263] The first decoding module 602 is used to decode the encoding result of the DC coefficient according to the first order corresponding to the DC coefficient and the context probability model corresponding to the DC coefficient, so as to obtain the DC coefficient.

[0264] The second decoding module 603 is used to decode the encoding result of each AC coefficient according to the second order corresponding to each AC coefficient and the context probability model corresponding to each AC coefficient, to obtain at least two AC coefficients; the second order is determined based on the first order and the index of the encoding point corresponding to the AC coefficient, or based on the proportion information included in the attribute information parameter set of the target bitstream.

[0265] The first determining module 604 is used to determine the transformation coefficients based on the DC coefficients and the at least two AC coefficients.

[0266] Optionally, the transform coefficient decoding device 600 further includes:

[0267] The second acquisition module is used to acquire the first order from the attribute information parameter set of the target code stream.

[0268] Optionally, the first decoding module 602 is specifically used for:

[0269] The encoding results of the DC coefficients are Columbus decoded using the first set of context probability models and the second set of context probability models.

[0270] Wherein, the decoding order of the Columbus decoding of the encoding result of the DC coefficient is the same as the first order, the first group of context probability models includes multiple context probability models, the second group of context probability models includes multiple context probability models, and the multiple context probability models included in the second group of context probability models are different from the multiple context probability models included in the first group of context probability models.

[0271] Optionally, the transform coefficient decoding device 600 further includes:

[0272] The second determining module is used to determine the difference between the first order and the first value as the second order;

[0273] Wherein, the first value is the product of the second value and the preset amplitude control parameter, the second value is the result of performing a logarithmic calculation on the third value, the third value is the maximum value between the first preset value and the fourth value, the fourth value is the result of multiplying the fifth value and the second preset value, and the fifth value is the result of dividing the index of the current coding point corresponding to the AC coefficient by the total number of coding points.

[0274] Optionally, the DC coefficient is determined based on color information, and the DC coefficient includes Y, U, and V components. The first decoding module 602 is further specifically used for:

[0275] Based on the first order corresponding to each DC coefficient, determine the decoding order for Columbus decoding of each component of the DC coefficient;

[0276] Based on the decoding order, the encoding result of the DC coefficients is subjected to Columbus decoding through the first context probability model and the second context probability model.

[0277] Wherein, if the first order corresponding to the DC coefficient is greater than a third preset value and less than or equal to a fourth preset value, the decoding order of the Y component in the DC coefficient using Columbus decoding is the first order, and the decoding order of the U and V components in the DC coefficient using Columbus decoding is 1; or,

[0278] When the first order corresponding to the DC coefficient is greater than the fourth preset value, the decoding order of the U and V components in the DC coefficient for Columbus decoding is the difference between the first order and the fourth preset value, and the decoding order of the Y component in the DC coefficient for Columbus decoding is 1; or,

[0279] When the first order corresponding to the DC coefficient is less than or equal to the third preset value, the decoding order of the Columbus decoding of the encoding result of the DC coefficient is 1.

[0280] Optionally, the transform coefficient decoding device 600 further includes:

[0281] The third acquisition module is used to acquire ratio information from the attribute information parameter set of the target bitstream;

[0282] The third determining module is used to determine the second order based on the ratio information and the preset total number of coding points.

[0283] Optionally, the transform coefficient decoding device 600 further includes:

[0284] The fourth determining module is used to determine the ratio information based on the ratio between the index of the coding point corresponding to the AC coefficient and the preset total number of coding points;

[0285] The fifth determining module is used to determine the second order based on the ratio information and the total number of coding points.

[0286] Optionally, the AC coefficients are determined based on color information, and the AC coefficients include Y, U, and V components. The second decoding module 603 is specifically used for:

[0287] Based on the second order corresponding to each AC coefficient, determine the decoding order for Columbus decoding of the encoded result of the AC coefficient;

[0288] Based on the decoding order, the encoding results of the AC coefficients are subjected to Columbus decoding through the first set of context probability models and the second set of context probability models.

[0289] Wherein, if the second order corresponding to the AC coefficient is greater than a third preset value and less than or equal to a fourth preset value, the decoding order of the Y component in the AC coefficient using Columbus decoding is the second order, and the decoding order of the U and V components in the AC coefficient using Columbus decoding is 1; or,

[0290] When the second order corresponding to the AC coefficients is greater than the fourth preset value, the decoding order of the U and V components in the AC coefficients using Columbus decoding is the difference between the second order and the fourth preset value, and the decoding order of the Y component in the AC coefficients using Columbus decoding is 1; or,

[0291] When the second order corresponding to the AC coefficient is less than or equal to the third preset value, the decoding order of the Columbus decoding of the encoding result of the AC coefficient is 1.

[0292] The first group of context probability models includes multiple context probability models, the second group of context probability models includes multiple context probability models, and the multiple context probability models included in the second group of context probability models are different from the multiple context probability models included in the first group of context probability models.

[0293] Optionally, the AC coefficients are determined based on color information, and the AC coefficients include Y, U, and V components. The second decoding module 603 is further specifically used for:

[0294] The encoding results of the AC coefficients are Columbus decoded using the first set of context probability models and the second set of context probability models.

[0295] Wherein, the decoding order of the Columbus decoding of the encoding result of the AC coefficients is the same as the second order, the first group of context probability models includes multiple context probability models, the second group of context probability models includes multiple context probability models, and the multiple context probability models included in the second group of context probability models are different from the multiple context probability models included in the first group of context probability models.

[0296] Optionally, the AC coefficient is determined based on reflectivity information, and the second decoding module 603 is further specifically used for:

[0297] Columbus decoding is performed on the encoding results of each AC coefficient using the first set of context probability models and the second set of context probability models.

[0298] Wherein, if the second order corresponding to the AC coefficients is greater than the fifth preset value, the decoding order of the Columbus decoding of the encoding result of the AC coefficients is the same as the second order; or,

[0299] When the second order corresponding to the AC coefficient is less than or equal to the fifth preset value, the decoding order of the Columbus decoding of the encoding result of the AC coefficient is 1.

[0300] The first group of context probability models includes multiple context probability models, the second group of context probability models includes multiple context probability models, and the multiple context probability models included in the second group of context probability models are different from the multiple context probability models included in the first group of context probability models.

[0301] Optionally, the initial probability of the context probability model is a preset probability value.

[0302] Optionally, the first decoding module 602 is further specifically used for:

[0303] Obtain the target identifier;

[0304] When the target identifier is the target value, the encoding result of the DC coefficient is decoded according to the first order corresponding to the DC coefficient and the context probability model corresponding to the DC coefficient to obtain the DC coefficient.

[0305] The transform coefficient decoding device provided in this application embodiment can achieve... Figure 4 The various processes implemented in the method embodiments achieve the same technical effect, and will not be described again here to avoid repetition.

[0306] The transform coefficient encoding device and transform coefficient decoding device in the embodiments of this application can be electronic devices, such as electronic devices with an operating system, or components in electronic devices, such as integrated circuits or chips. The electronic device can be a terminal or other devices besides a terminal. For example, the terminal can include, but is not limited to, the types of terminals listed above, and other devices can be servers, network attached storage (NAS), etc., and the embodiments of this application do not specifically limit them.

[0307] Optionally, such as Figure 7As shown, this application embodiment also provides a communication device 700, including a processor 701 and a memory 702. The memory 702 stores a program or instructions that can be executed on the processor 701. For example, when the communication device 700 is a terminal, when the program or instructions are executed by the processor 701, they implement the various steps of the above-described transform coefficient encoding method embodiment, or implement the various steps of the above-described transform coefficient decoding method embodiment, and can achieve the same technical effect.

[0308] This application embodiment also provides a terminal, including a processor and a communication interface, wherein the processor is used to perform the following operations:

[0309] Obtain the transformation coefficients corresponding to the point cloud;

[0310] The DC coefficients are encoded based on the first order corresponding to the DC coefficients and the context probability model corresponding to the DC coefficients.

[0311] Each AC coefficient is encoded based on the second order corresponding to each AC coefficient and the context probability model corresponding to each AC coefficient;

[0312] The target bitstream is generated based on the encoding results of the DC coefficients and the encoding results of each AC coefficient.

[0313] Alternatively, the processor may be used to perform the following operations:

[0314] Obtain the target bitstream;

[0315] Based on the first order corresponding to the DC coefficient and the context probability model corresponding to the DC coefficient, the encoding result of the DC coefficient is decoded to obtain the DC coefficient;

[0316] Based on the second order corresponding to each AC coefficient and the context probability model corresponding to each AC coefficient, the encoding result of each AC coefficient is decoded to obtain at least two AC coefficients;

[0317] The transformation coefficients are determined based on the DC coefficients and the at least two AC coefficients.

[0318] This terminal embodiment corresponds to the aforementioned terminal-side method embodiment. All implementation processes and methods of the aforementioned method embodiments can be applied to this terminal embodiment and achieve the same technical effect. Specifically, Figure 8 A schematic diagram of the hardware structure of a terminal to implement an embodiment of this application.

[0319] The terminal 800 includes, but is not limited to, components such as: radio frequency unit 801, network module 802, audio output unit 803, input unit 804, sensor 805, display unit 806, user input unit 807, interface unit 808, memory 809, and processor 810.

[0320] Those skilled in the art will understand that the terminal 800 may also include a power supply (such as a battery) for supplying power to various components. The power supply may be logically connected to the processor 810 through a power management system, thereby enabling functions such as managing charging, discharging, and power consumption through the power management system. Figure 8 The terminal structure shown does not constitute a limitation on the terminal. The terminal may include more or fewer components than shown, or combine certain components, or have different component arrangements, which will not be elaborated here.

[0321] It should be understood that, in this embodiment, the input unit 804 may include a graphics processing unit (GPU) 8041 and a microphone 8042. The GPU 8041 processes image data of still images or videos obtained by an image capture device (such as a camera) in video capture mode or image capture mode. The display unit 806 may include a display panel 8061, which may be configured in the form of a liquid crystal display, an organic light-emitting diode, or the like. The user input unit 807 includes at least one of a touch panel 8071 and other input devices 8072. The touch panel 8071 is also called a touch screen. The touch panel 8071 may include two parts: a touch detection device and a touch controller. Other input devices 8072 may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, power buttons, etc.), trackballs, mice, and joysticks, which will not be described in detail here.

[0322] In this embodiment, after receiving downlink data from the network-side device, the radio frequency unit 801 can transmit it to the processor 88 for processing; the radio frequency unit 801 can also send uplink data to the network-side device. Typically, the radio frequency unit 801 includes, but is not limited to, antennas, amplifiers, transceivers, couplers, low-noise amplifiers, and duplexers.

[0323] The memory 809 can be used to store software programs or instructions, as well as various data. The memory 809 may primarily include a first storage area for storing programs or instructions and a second storage area for storing data. The first storage area may store the operating system, application programs or instructions required for at least one function (such as sound playback, image playback, etc.). Furthermore, the memory 809 may include volatile memory or non-volatile memory, or both. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus RAM (DRRAM). The memory 809 in the embodiments of this application includes, but is not limited to, these and any other suitable types of memory.

[0324] Processor 810 may include one or more processing units; optionally, processor 810 integrates an application processor and a modem processor, wherein the application processor mainly handles operations involving the operating system, user interface, and applications, and the modem processor mainly handles wireless communication signals, such as a baseband processor. It is understood that the aforementioned modem processor may also not be integrated into processor 810.

[0325] The processor 810 is used to perform the following operations:

[0326] Obtain the transformation coefficients corresponding to the point cloud;

[0327] The DC coefficients are encoded based on the first order corresponding to the DC coefficients and the context probability model corresponding to the DC coefficients.

[0328] Each AC coefficient is encoded based on the second order corresponding to each AC coefficient and the context probability model corresponding to each AC coefficient;

[0329] The target bitstream is generated based on the encoding results of the DC coefficients and the encoding results of each AC coefficient.

[0330] Alternatively, processor 810 may also be used to perform the following operations:

[0331] Obtain the target bitstream;

[0332] Based on the first order corresponding to the DC coefficient and the context probability model corresponding to the DC coefficient, the encoding result of the DC coefficient is decoded to obtain the DC coefficient;

[0333] Based on the second order corresponding to each AC coefficient and the context probability model corresponding to each AC coefficient, the encoding result of each AC coefficient is decoded to obtain at least two AC coefficients;

[0334] The transformation coefficients are determined based on the DC coefficients and the at least two AC coefficients.

[0335] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described transform coefficient encoding method embodiment or the various processes of the above-described transform coefficient decoding method embodiment, and can achieve the same technical effect. To avoid repetition, they will not be described again here.

[0336] The processor is the processor in the terminal described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0337] This application embodiment also provides a chip, which 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 implement the various processes of the above-described transform coefficient encoding method embodiment, or to implement the various processes of the above-described transform coefficient decoding method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0338] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0339] This application also provides a computer program / program product, which is stored in a storage medium and executed by at least one processor to implement the various processes of the above-described transform coefficient encoding method embodiment or the various processes of the above-described transform coefficient decoding method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0340] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0341] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0342] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A transform coefficient encoding method, characterized in that, include: The encoder obtains the transformation coefficients corresponding to the point cloud. The conversion coefficients include a DC coefficient and at least two AC coefficients; The encoding end encodes the DC coefficients according to the first order corresponding to the DC coefficients and the context probability model corresponding to the DC coefficients; The encoding end encodes each AC coefficient according to the second order corresponding to each AC coefficient and the context probability model corresponding to each AC coefficient; The second order is determined based on the first order and the index of the coding point corresponding to the AC coefficient; The encoding end generates the target bitstream based on the encoding results of the DC coefficients and the encoding results of each AC coefficient; Wherein, the first order and the second order are the Columbus order.

2. The method according to claim 1, characterized in that, The DC coefficient is determined based on color information. Before encoding the DC coefficient according to the first order corresponding to the DC coefficient and the context probability model corresponding to the DC coefficient, the method further includes: The encoding end determines the first order corresponding to the DC coefficient based on the Columbus order corresponding to at least some of the components of the DC coefficient.

3. The method according to claim 2, characterized in that, Determining the first order corresponding to the DC coefficient based on the Columbus order corresponding to at least a portion of the components of the DC coefficient includes: The encoding end obtains the Columbus order corresponding to each component of the DC coefficient; The encoding end determines the Columbus order with the highest frequency among the Columbus orders corresponding to each component as the first order corresponding to the DC coefficient.

4. The method according to claim 2, characterized in that, Determining the first order corresponding to the DC coefficient based on the Columbus order corresponding to at least a portion of the components of the DC coefficient includes: The encoding end obtains the Columbus order corresponding to each component of the DC coefficient; The encoding end takes the average value of the Columbus order corresponding to each component of the DC coefficient and obtains the first order corresponding to the DC coefficient.

5. The method according to any one of claims 1-4, characterized in that, The step of encoding the DC coefficients based on the first order corresponding to the DC coefficients and the context probability model corresponding to the DC coefficients includes: The encoding end performs Columbus coding on the DC coefficients using the first set of context probability models and the second set of context probability models. Wherein, the coding order of the DC coefficients using Golomb coding is the same as the first order; The first group of context probability models includes multiple context probability models, the second group of context probability models includes multiple context probability models, and the multiple context probability models included in the second group of context probability models are different from the multiple context probability models included in the first group of context probability models.

6. The method according to any one of claims 1-4, characterized in that, Before encoding each AC coefficient based on the second-order number corresponding to each AC coefficient and the context probability model corresponding to each AC coefficient, the method further includes: The encoding end determines the difference between the first order and the first value as the second order; Wherein, the first value is the product of the second value and the preset amplitude control parameter, the second value is the result of performing a logarithmic calculation on the third value, the third value is the maximum value between the first preset value and the fourth value, the fourth value is the result of multiplying the fifth value and the second preset value, and the fifth value is the result of dividing the index of the coding point by the total number of coding points.

7. The method according to any one of claims 1-4, characterized in that, The DC coefficient is determined based on color information, and the DC coefficient includes Y, U, and V components. Encoding the DC coefficient according to its first order and the context probability model includes: The encoding end determines the encoding order of each component of the DC coefficient for Columbus encoding based on the first order corresponding to the DC coefficient; Based on the coding order, the encoding end performs Columbus coding on each component of the DC coefficient through the first set of context probability models and the second set of context probability models; Wherein, if the first order corresponding to the DC coefficient is greater than a third preset value and less than or equal to a fourth preset value, the coding order of the Y component in the DC coefficient using Golomb coding is the first order, and the coding order of the U and V components in the DC coefficient using Golomb coding is 1; or, When the first order corresponding to the DC coefficient is greater than the fourth preset value, the coding order of the U and V components in the DC coefficient using Golomb coding is the difference between the first order and the fourth preset value, and the coding order of the Y component in the DC coefficient using Golomb coding is 1; or, When the first order corresponding to the DC coefficient is less than or equal to the third preset value, the coding order of each component of the DC coefficient using Columbus coding is 1. The first group of context probability models includes multiple context probability models, the second group of context probability models includes multiple context probability models, and the multiple context probability models included in the second group of context probability models are different from the multiple context probability models included in the first group of context probability models.

8. The method according to any one of claims 1-4, characterized in that, The AC coefficients are determined based on color information, and each AC coefficient includes a Y component, a U component, and a V component. Encoding each AC coefficient according to its second-order coefficient and its corresponding context probability model includes: The encoding end determines the encoding order for Columbus coding of each component of the AC coefficient based on the second order corresponding to each AC coefficient; Based on the coding order, the encoding end performs Columbus coding on each component of the AC coefficients through the first set of context probability models and the second set of context probability models. Wherein, if the second order corresponding to the AC coefficients is greater than a third preset value and less than or equal to a fourth preset value, the coding order of the Y component in the AC coefficients using Golomb coding is the second order, and the coding order of the U and V components in the AC coefficients using Golomb coding is 1; or, When the second order corresponding to the AC coefficients is greater than the fourth preset value, the coding order of the U and V components in the AC coefficients using Golomb coding is the difference between the second order and the fourth preset value, and the coding order of the Y component in the AC coefficients using Golomb coding is 1; or, When the second order corresponding to the AC coefficient is less than or equal to the third preset value, the coding order of each component of the AC coefficient using Columbus coding is 1. The first group of context probability models includes multiple context probability models, the second group of context probability models includes multiple context probability models, and the multiple context probability models included in the second group of context probability models are different from the multiple context probability models included in the first group of context probability models.

9. The method according to any one of claims 1-4, characterized in that, The AC coefficients are determined based on color information, and each AC coefficient includes a Y component, a U component, and a V component. Encoding each AC coefficient according to its second-order coefficient and its corresponding context probability model includes: The encoding end performs Columbus coding on the U and V components in the AC coefficients through a first set of context probability models; the coding order of the Columbus coding on the U and V components is the same as the second order, and the first set of context probability models includes multiple context probability models. The encoding end performs Columbus coding on the Y component in the AC coefficients through the second set of context probability models; the coding order of the Y component is the same as the second order, the second set of context probability models includes multiple context probability models, and the multiple context probability models included in the second set of context probability models are different from the multiple context probability models included in the first set of context probability models.

10. The method according to any one of claims 1-4, characterized in that, The AC coefficients are determined based on reflectivity information. Encoding each AC coefficient according to its second-order coefficient and the context probability model includes: The encoding end performs Columbus coding on each AC coefficient using the first set of context probability models and the second set of context probability models. Wherein, if the second order corresponding to the AC coefficient is greater than the fifth preset value, the coding order of the AC coefficient using Golomb coding is the same as the second order; or, When the second order corresponding to the AC coefficient is less than or equal to the fifth preset value, the coding order of the AC coefficient using Columbus coding is 1. The first group of context probability models includes multiple context probability models, the second group of context probability models includes multiple context probability models, and the multiple context probability models included in the second group of context probability models are different from the multiple context probability models included in the first group of context probability models.

11. The method according to any one of claims 1-4, characterized in that, The initial probability of the context probability model is a preset probability value.

12. The method according to any one of claims 1-4, characterized in that, The attribute information parameter set of the target bitstream includes the first order and the scale information, wherein the scale information is used to determine the second order; The ratio information is used to characterize the range of the Columbus order, and the ratio information is determined based on the ratio between the index of the coding point corresponding to the AC coefficient and the preset total number of coding points.

13. The method according to any one of claims 1-4, characterized in that, The step of encoding the DC coefficients based on the first order corresponding to the DC coefficients and the context probability model corresponding to the DC coefficients includes: The encoding end obtains the target identifier; When the target identifier is the target value, the encoding end encodes the DC coefficient according to the first order corresponding to the DC coefficient and the context probability model corresponding to the DC coefficient.

14. A method for decoding transform coefficients, characterized in that, include: The decoding end acquires the target bitstream; the target bitstream includes the encoding result of the DC coefficient in the transform coefficient and the encoding result of at least two AC coefficients in the transform coefficient; The decoding end decodes the encoding result of the DC coefficient according to the first order corresponding to the DC coefficient and the context probability model corresponding to the DC coefficient, and obtains the DC coefficient. The decoding end decodes the encoding result of each AC coefficient according to the second order corresponding to each AC coefficient and the context probability model corresponding to each AC coefficient, to obtain at least two AC coefficients; the second order is determined based on the first order and the index of the encoding point corresponding to the AC coefficient, or based on the proportion information included in the attribute information parameter set of the target bitstream; The decoding end determines the transformation coefficients based on the DC coefficients and the at least two AC coefficients; Wherein, the first order and the second order are Columbus orders, and the ratio information is used to characterize the range of Columbus orders. The ratio information is determined based on the ratio between the index of the coding point corresponding to the AC coefficient and the preset total number of coding points.

15. The method according to claim 14, characterized in that, Before decoding the encoding result of the DC coefficients based on the first order corresponding to the DC coefficients and the context probability model corresponding to the DC coefficients, the method further includes: The decoding end obtains the first order from the attribute information parameter set of the target bitstream.

16. The method according to claim 14 or 15, characterized in that, Decoding the encoding result of the DC coefficient based on the first order corresponding to the DC coefficient and the context probability model corresponding to the DC coefficient includes: The decoding end performs Columbus decoding on the encoding results of the DC coefficients through the first set of context probability models and the second set of context probability models. Wherein, the decoding order of the Columbus decoding of the encoding result of the DC coefficient is the same as the first order, the first group of context probability models includes multiple context probability models, the second group of context probability models includes multiple context probability models, and the multiple context probability models included in the second group of context probability models are different from the multiple context probability models included in the first group of context probability models.

17. The method according to claim 14, characterized in that, Before decoding the encoding result of each AC coefficient based on the second order corresponding to each AC coefficient and the context probability model corresponding to each AC coefficient, the method further includes: The decoding end determines the difference between the first order and the first value as the second order; Wherein, the first value is the product of the second value and the preset amplitude control parameter, the second value is the result of performing a logarithmic calculation on the third value, the third value is the maximum value between the first preset value and the fourth value, the fourth value is the result of multiplying the fifth value and the second preset value, and the fifth value is the result of dividing the index of the current coding point corresponding to the AC coefficient by the total number of coding points.

18. The method according to claim 14 or 15, characterized in that, The DC coefficients are determined based on color information, and the DC coefficients include Y, U, and V components. Decoding the encoding result of the DC coefficients according to the first order corresponding to the DC coefficients and the context probability model corresponding to the DC coefficients includes: The decoding end determines the decoding order for Columbus decoding of each component of the DC coefficient based on the first order corresponding to each DC coefficient; Based on the decoding order, the decoding end performs Columbus decoding on the encoding result of the DC coefficients through a first context probability model and a second context probability model. Wherein, if the first order corresponding to the DC coefficient is greater than a third preset value and less than or equal to a fourth preset value, the decoding order of the Y component in the DC coefficient using Columbus decoding is the first order, and the decoding order of the U and V components in the DC coefficient using Columbus decoding is 1; or, When the first order corresponding to the DC coefficient is greater than the fourth preset value, the decoding order of the U and V components in the DC coefficient for Columbus decoding is the difference between the first order and the fourth preset value, and the decoding order of the Y component in the DC coefficient for Columbus decoding is 1; or, When the first order corresponding to the DC coefficient is less than or equal to the third preset value, the decoding order of the Columbus decoding of the encoding result of the DC coefficient is 1.

19. The method according to claim 14, characterized in that, Before decoding the encoding result of each AC coefficient based on the second order corresponding to each AC coefficient and the context probability model corresponding to each AC coefficient, the method further includes: The decoding end obtains the ratio information from the attribute information parameter set of the target bitstream; The decoding end determines the second order based on the ratio information and the preset total number of encoding points.

20. The method according to any one of claims 14, 17, and 19, characterized in that, The AC coefficients are determined based on color information, and each AC coefficient includes a Y component, a U component, and a V component. Decoding the encoding result of each AC coefficient based on its second-order coefficient and the context probability model includes: The decoding end determines the decoding order of the Columbus decoding of the encoding result of the AC coefficients based on the second order corresponding to each AC coefficient; Based on the decoding order, the decoding end performs Columbus decoding on the encoding result of the AC coefficients through the first set of context probability models and the second set of context probability models. Wherein, if the second order corresponding to the AC coefficient is greater than a third preset value and less than or equal to a fourth preset value, the decoding order of the Y component in the AC coefficient using Columbus decoding is the second order, and the decoding order of the U and V components in the AC coefficient using Columbus decoding is 1; or, When the second order corresponding to the AC coefficients is greater than the fourth preset value, the decoding order of the U and V components in the AC coefficients using Columbus decoding is the difference between the second order and the fourth preset value, and the decoding order of the Y component in the AC coefficients using Columbus decoding is 1; or, When the second order corresponding to the AC coefficient is less than or equal to the third preset value, the decoding order of the Columbus decoding of the encoding result of the AC coefficient is 1. The first group of context probability models includes multiple context probability models, the second group of context probability models includes multiple context probability models, and the multiple context probability models included in the second group of context probability models are different from the multiple context probability models included in the first group of context probability models.

21. The method according to any one of claims 14, 17, and 19, characterized in that, The AC coefficients are determined based on color information, and each AC coefficient includes a Y component, a U component, and a V component. Decoding the encoding result of each AC coefficient based on its second-order coefficient and the context probability model includes: The decoding end performs Columbus decoding on the encoding results of the AC coefficients through the first set of context probability models and the second set of context probability models. Wherein, the decoding order of the Columbus decoding of the encoding result of the AC coefficients is the same as the second order, the first group of context probability models includes multiple context probability models, the second group of context probability models includes multiple context probability models, and the multiple context probability models included in the second group of context probability models are different from the multiple context probability models included in the first group of context probability models.

22. The method according to any one of claims 14, 17, and 19, characterized in that, The AC coefficients are determined based on reflectivity information. Decoding the encoding result of each AC coefficient according to its second-order coefficient and the context probability model includes: The decoding end performs Columbus decoding on the encoding results of each AC coefficient through the first set of context probability models and the second set of context probability models; Wherein, if the second order corresponding to the AC coefficients is greater than the fifth preset value, the decoding order of the Columbus decoding of the encoding result of the AC coefficients is the same as the second order; or, When the second order corresponding to the AC coefficient is less than or equal to the fifth preset value, the decoding order of the Columbus decoding of the encoding result of the AC coefficient is 1. The first group of context probability models includes multiple context probability models, the second group of context probability models includes multiple context probability models, and the multiple context probability models included in the second group of context probability models are different from the multiple context probability models included in the first group of context probability models.

23. The method according to any one of claims 14, 15, 17, and 19, characterized in that, The initial probability of the context probability model is a preset probability value.

24. The method according to any one of claims 14, 15, 17, and 19, characterized in that, The step of decoding the encoding result of the DC coefficients based on the first order corresponding to the DC coefficients and the context probability model corresponding to the DC coefficients to obtain the DC coefficients includes: The decoding end obtains the target identifier; When the target identifier is the target value, the decoding end decodes the encoding result of the DC coefficient according to the first order corresponding to the DC coefficient and the context probability model corresponding to the DC coefficient to obtain the DC coefficient.

25. A transform coefficient encoding device, characterized in that, include: The acquisition module is used to acquire the transformation coefficients corresponding to the point cloud. The conversion coefficients include a DC coefficient and at least two AC coefficients; The first encoding module is used to encode the DC coefficients according to the first order corresponding to the DC coefficients and the context probability model corresponding to the DC coefficients; The second encoding module is used to encode each AC coefficient according to the second order corresponding to each AC coefficient and the context probability model corresponding to each AC coefficient; The second order is determined based on the first order and the index of the coding point corresponding to the AC coefficient; A generation module is used to generate a target bitstream based on the encoding results of the DC coefficients and the encoding results of each AC coefficient; Wherein, the first order and the second order are the Columbus order.

26. A transform coefficient decoding device, characterized in that, include: The first acquisition module is used to acquire the target code stream; the target code stream includes the encoding result of the DC coefficient in the transform coefficient and the encoding result of at least two AC coefficients in the transform coefficient; The first decoding module is used to decode the encoding result of the DC coefficient according to the first order corresponding to the DC coefficient and the context probability model corresponding to the DC coefficient, so as to obtain the DC coefficient. The second decoding module is used to decode the encoding result of each AC coefficient according to the second order corresponding to each AC coefficient and the context probability model corresponding to each AC coefficient, to obtain at least two AC coefficients; the second order is determined based on the first order and the index of the encoding point corresponding to the AC coefficient, or based on the proportion information included in the attribute information parameter set of the target bitstream; The first determining module is used to determine the transformation coefficients based on the DC coefficients and the at least two AC coefficients; Wherein, the first order and the second order are Columbus orders, and the ratio information is used to characterize the range of Columbus orders. The ratio information is determined based on the ratio between the index of the coding point corresponding to the AC coefficient and the preset total number of coding points.

27. A terminal, characterized in that, It includes a processor and a memory, the memory storing a program or instructions that can run on the processor, the program or instructions being executed by the processor to implement the steps of the transform coefficient encoding method as described in any one of claims 1-13, or to implement the steps of the transform coefficient decoding method as described in any one of claims 14-24.

28. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the transform coefficient encoding method as described in any one of claims 1-13, or the steps of the transform coefficient decoding method as described in any one of claims 14-24.

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