Data compression method, data decompression method, and related apparatus

Abstract

Disclosed in the present application are a data compression method, a data decompression method, and a related apparatus. The data compression method comprises: on the basis of a first coding parameter, performing compression coding on data of one or more dimensions in first data, so as to obtain one or more pieces of compressed data; and sending second data, wherein the second data comprises the compressed data and first indication information, the first indication information being used for indicating the first coding parameter, which meets a target condition, the target condition being that a code length obtained by performing compression coding on the data of one or more dimensions in the first data on the basis of the first coding parameter is the shortest one of a plurality of code lengths, and the plurality of code lengths comprising code lengths obtained by separately performing compression coding on the data of one or more dimensions in the first data on the basis of a plurality of groups of coding parameters, which comprise the first coding parameter. The above method can improve the data compression performance.

Description

Data compression method, data decompression method and related devices Technical Field

[0001] The present application relates to the field of data compression, and in particular to a data compression method, a data decompression method, and related devices. Background Art

[0002] Existing compression techniques for multidimensional data fall into two main categories: vector quantization (VQ), and transform-domain compression. These include compression methods based on linear transforms such as the discrete cosine transform (DCT) and discrete wavelet transform (DWT), as well as nonlinear transform-domain compression methods based on deep learning tools such as variational auto-encoders (VAEs) and flow models. VQ is an effective lossy compression technique developed in the late 1970s, based on Shannon's rate-distortion theory. The basic principle of vector quantization is to replace the input vector with the index of the codeword in the codebook that best matches it for transmission and storage, while decoding requires only a simple table lookup. Its outstanding advantages include high compression ratios, simple decoding, and excellent preservation of signal details. Compression technology for multidimensional data remains an active research area.

[0003] Summary of the Invention

[0004] The embodiments of the present application disclose a data compression method, a data decompression method, and related devices, which can improve the performance of compressed data, especially the performance of compressed multi-dimensional data.

[0005] In a first aspect, an embodiment of the present application provides a data compression method, comprising: compressing and encoding data of one or more dimensions in first data based on first encoding parameters to obtain one or more compressed data; and transmitting second data, the second data comprising the compressed data and first indication information, the first indication information being used to indicate the first encoding parameters, wherein the first encoding parameters satisfy a target condition, wherein the target condition is that a code length obtained by compressing and encoding data of one or more dimensions in the first data based on the first encoding parameters is the shortest of a plurality of code lengths, the plurality of code lengths comprising code lengths obtained by compressing and encoding data of one or more dimensions in the first data based on multiple sets of encoding parameters, the plurality of sets of encoding parameters including the first encoding parameters. The shortest of the plurality of code lengths may be referred to as a first short code length, the shortest code length among the plurality of code lengths other than the first short code length may be referred to as a second short code length, the shortest code length among the plurality of code lengths other than the first short code length and the second segment code length may be referred to as a third short code length, and so on. The target condition may also be that the code length obtained by compressing and encoding data of one or more dimensions in the first data based on the first encoding parameter is one of the second short code length, the third short code length, or the fourth short code length among multiple code lengths, which is not limited in this application.

[0006] In an embodiment of the present application, data of one or more dimensions within the first data are compressed and encoded based on first encoding parameters to obtain one or more compressed data; and second data is transmitted, the second data including the compressed data and first indication information. Because the first encoding parameters meet the target conditions, the code length obtained by compressing and encoding the data of one or more dimensions within the first data based on the first encoding parameters is the shortest, thereby improving the performance of the compressed data. In addition, the transmitted second data includes the first indication information, so that the receiving end can decode the compressed data within the second data based on the first encoding parameters.

[0007] In one possible implementation, before compressing and encoding the data of one or more dimensions in the first data based on the first encoding parameters, the method further includes: obtaining the multiple code lengths obtained by compressing and encoding the data of one or more dimensions in the first data based on the multiple sets of encoding parameters, respectively, and the multiple sets of encoding parameters correspond one to one to the multiple code lengths; and determining that the code length obtained by compressing and encoding the data of one or more dimensions in the first data based on the first encoding parameters is the shortest of the multiple code lengths. The sending end is the executor of the method of the first aspect. It should be noted that the sending end can calculate the code length obtained by compressing and encoding the data of one or more dimensions in the first data based on the first encoding parameters, rather than compressing and encoding the data of one or more dimensions in the first data based on the first encoding parameters to obtain the code length. Alternatively, the sending end can calculate the code length obtained by compressing and encoding the data of one or more dimensions in the first data based on the first encoding parameters without obtaining the one or more sets of data.

[0008] In this implementation, it is determined that the code length obtained by compressing and encoding data of one or more dimensions in the first data based on the first encoding parameter is the shortest of multiple code lengths, so as to obtain the optimal encoding parameter.

[0009] In a possible implementation manner, the first encoding parameter is a Golomb code parameter.

[0010] In this implementation, the code length obtained by compressing and encoding data of one or more dimensions in the first data based on the first encoding parameter can be calculated without performing a compression operation, which can reduce data compression time.

[0011] In one possible implementation, the second data also includes k cluster center quantization data, at least one of the one or more compressed data corresponds to one of the k cluster center quantization data, and k is an integer greater than or equal to 1. For example, the k cluster center quantization data are k bit strings of fixed length, each bit string represents a quantization value and corresponds to a cluster center. Each compressed data of the one or more compressed data may correspond to one of the k cluster center quantization data. Optionally, the one or more compressed data are divided into k groups, each group corresponding to one cluster center quantization data, that is, the k groups of compressed data correspond one-to-one to the k cluster center quantization data.

[0012] In this implementation, the second data also includes k cluster center quantified data, which can effectively utilize the dynamic aggregation characteristics of the data for compression.

[0013] In a possible implementation, the second data further includes second indication information, where the second indication information is used to identify a correspondence between the k cluster center quantization data and the one or more compressed data.

[0014] In this implementation, the receiving end can learn the corresponding relationship between k cluster center quantized data and one or more compressed data.

[0015] In a possible implementation manner, the second data further includes third indication information, where the third indication information is used to indicate k.

[0016] In this implementation, the receiving end can learn the number of cluster center quantization data.

[0017] In a possible implementation, the second data includes first encoding information and second encoding information, the first encoding information includes data of a first dimension in the one or more compressed data, the second encoding information includes data of a second dimension in the one or more compressed data, the data of the first dimension in the one or more compressed data are compressed and encoded based on the data of the first dimension in the first data and the encoding parameters of the first dimension in the first encoding parameters, and the data of the second dimension in the one or more compressed data are compressed and encoded based on the data of the second dimension in the first data and the encoding parameters of the second dimension in the first encoding parameters.

[0018] In this implementation, the second data includes the first encoding information and the second encoding information. Since the data of the same dimension in the one or more compressed data are located in the same information, the data compression speed and the data decompression speed can be improved.

[0019] In one possible implementation, the first compressed data among the one or more compressed data corresponds to the first cluster center quantized data among the k cluster center quantized data, the first encoding information includes the data of the first dimension in the first compressed data, the data of the first dimension in the first compressed data is compressed and encoded by the first quantized value using the encoding parameters of the first dimension in the first encoding parameters, the first quantized value is quantized by the first differential value, and the first differential value is the difference between the data of the first dimension of the element in the first data corresponding to the first cluster center quantized data and the data of the first dimension of the first cluster center quantized data.

[0020] In one possible implementation, the compression encoding of one or more dimensions of the first data based on the first encoding parameter to obtain one or more compressed data includes: determining that the code length of the compressed data obtained by compression encoding the first data based on the information to be compressed corresponding to the first clustering result and the first encoding parameter is the shortest of the multiple code lengths, the first clustering result is a clustering result of the elements in the first data, the information to be compressed includes multiple non-negative integers obtained by mapping multiple quantization values, and the multiple quantization values ​​are quantized by the difference values ​​of the quantization data of the cluster centers corresponding to each element in the first data under the first clustering result. Determining that the code length of the compressed data obtained by compression encoding the first data based on the information to be compressed corresponding to the first clustering result and the first encoding parameter is the shortest of the multiple code lengths can be understood as: (jointly) optimizing the clustering result and the encoding parameters to obtain the optimal encoding parameters.

[0021] In this implementation, encoding parameters for obtaining the shortest code length by performing compression encoding on the first data can be obtained.

[0022] In one possible implementation, the one or more compressed data include f compressed data of the third dimension and g compressed data of the fourth dimension, the f compressed data of the third dimension and the g compressed data of the fourth dimension being obtained using different compression encoding methods, where f and g are integers greater than 0. The f compressed data of the third dimension and the g compressed data of the fourth dimension being obtained using different compression encoding methods may mean that the encoding parameters and / or compression encoding processes corresponding to the f compressed data of the third dimension and the g compressed data of the fourth dimension are different.

[0023] In this implementation, f compressed data of the third dimension and g compressed data of the fourth dimension are obtained through different compression encoding methods, which is suitable for compression scenarios where the third dimensional data and the fourth dimensional data have different characteristics.

[0024] In a possible implementation, the second data further includes fourth indication information, where the fourth indication information is used to indicate a corresponding relationship between the compressed data of the third dimension and the compressed data of the fourth dimension.

[0025] In this implementation, the receiving end can combine the decompressed third-dimensional data and fourth-dimensional data into two-dimensional data according to the correspondence between the third-dimensional compressed data and the fourth-dimensional compressed data.

[0026] In one possible implementation, the second data also includes one or more cluster centers corresponding to the g compressed data of the fourth dimension, the f compressed data of the third dimension in the second data are arranged in order, and the fourth indication information is used to indicate the compressed data of the third dimension corresponding to the one or more cluster centers, and the order of the entropy coded data corresponding to the same cluster center in the g compressed data of the fourth dimension is the same as the order of the corresponding data of the third dimension.

[0027] In this implementation, the second data also includes one or more cluster centers corresponding to the g fourth-dimensional compressed data, effectively utilizing the dynamic clustering characteristics of the fourth-dimensional data for compression. Furthermore, the fourth indication information is used to indicate the third-dimensional compressed data corresponding to one or more cluster centers. The f third-dimensional compressed data in the second data are arranged in sequence, and the order of the entropy-coded data corresponding to the same cluster center in the g fourth-dimensional compressed data is the same as the order of the corresponding third-dimensional data. This allows the receiving end to determine the correspondence between the third-dimensional compressed data and the fourth-dimensional compressed data, reducing signaling overhead.

[0028] In one possible implementation, the fourth indication information includes a bitmap, the number of bits in the bitmap is equal to f, each bit in the bitmap corresponds to the f compressed data of the third dimension arranged in order, and any bit in the bitmap is used to indicate whether the compressed data of the fourth dimension corresponding to the compressed data of the third dimension corresponding to any bit belongs to the first cluster center, and the first cluster center is one of the multiple cluster centers.

[0029] In this implementation, any bit in the bitmap is used to indicate whether the compressed data of the fourth dimension corresponding to the compressed data of the third dimension corresponding to the any bit belongs to the first cluster center, which can reduce signaling overhead.

[0030] In one possible implementation, the one or more compressed data include t fifth-dimensional data arranged in sequence, and the second data also includes a second quantization value, which is obtained by quantizing the minimum or maximum fifth-dimensional data in the first data. The t fifth-dimensional data arranged in sequence are obtained by performing a differential operation based on the fifth-dimensional data in the first data after being arranged in sequence, and t is an integer greater than or equal to 2.

[0031] In this implementation, the compression performance can be improved by performing differentiation on the t fifth-dimensional data arranged in sequence and then quantizing the obtained differential values.

[0032] In a possible implementation manner, the second data further includes fifth indication information, and the fifth indication information is used to indicate t.

[0033] In one possible implementation, the one or more compressed data also include t data of the sixth dimension arranged in sequence, and the t data of the sixth dimension arranged in sequence are obtained based on multiple differential values ​​obtained by periodically differencing the data of the sixth dimension in the first data. The second data also includes a third quantization value, and the third quantization value is obtained by one of two data with the maximum differential value obtained by differencing the quantized data of the sixth dimension.

[0034] In this implementation, the compression performance can be improved by performing differentiation on the t sequentially arranged data of the sixth dimension and then quantizing the obtained differential values.

[0035] In a possible implementation, the second data further includes sixth indication information, where the sixth indication information is used to indicate a correspondence between the t pieces of fifth-dimensional data and the t pieces of sixth-dimensional data.

[0036] In this implementation, the sixth indication information is used to indicate the correspondence between t fifth-dimensional data and t sixth-dimensional data, so that the receiving end merges the t fifth-dimensional data and the t sixth-dimensional data into t two-dimensional data according to the correspondence.

[0037] In a second aspect, the present application provides a data decompression method, comprising: receiving second data, the second data comprising one or more compressed data and first indication information, the first indication information being used to indicate a first encoding parameter, wherein the first encoding parameter satisfies a target condition, the target condition being that a code length obtained by compressing and encoding data of one or more dimensions in the first data based on the first encoding parameter is the shortest of multiple code lengths, the multiple code lengths comprising code lengths obtained by compressing and encoding data of one or more dimensions in the first data based on multiple sets of encoding parameters, the multiple sets of encoding parameters including the first encoding parameter; and decoding the compressed data in the second data based on the first entropy coding parameter to obtain the first data. The shortest of the multiple code lengths may be referred to as a first short code length, the shortest code length among the multiple code lengths other than the first short code length may be referred to as a second short code length, the shortest code length among the multiple code lengths other than the first short code length and the second segment code length may be referred to as a third short code length, and so on. The target condition may also be that the code length obtained by compressing and encoding data of one or more dimensions in the first data based on the first encoding parameter is one of the second short code length, the third short code length, or the fourth short code length among multiple code lengths, which is not limited in this application.

[0038] In the embodiment of the present application, the compressed data in the second data is decoded based on the first entropy coding parameter. Because the first coding parameter satisfies the target condition, the code length obtained by compressing and encoding the data of one or more dimensions in the first data based on the first coding parameter is the shortest, which can improve the performance of the compressed data.

[0039] In a possible implementation manner, the first encoding parameter is a Golomb code parameter.

[0040] In this implementation, the code length obtained by compressing and encoding data of one or more dimensions in the first data based on the first encoding parameter can be calculated without performing a compression operation, which can reduce data compression time.

[0041] In one possible implementation, the second data also includes k cluster center quantization data, at least one of the one or more compressed data corresponds to one of the k cluster center quantization data, and k is an integer greater than or equal to 1. For example, the k cluster center quantization data are k bit strings of fixed length, each bit string represents a quantization value and corresponds to a cluster center. Each compressed data of the one or more compressed data may correspond to one of the k cluster center quantization data. Optionally, the one or more compressed data are divided into k groups, each group corresponding to one cluster center quantization data, that is, the k groups of compressed data correspond one-to-one to the k cluster center quantization data.

[0042] In this implementation, the second data also includes k cluster center quantified data, which can effectively utilize the dynamic aggregation characteristics of the data for compression.

[0043] In a possible implementation, the second data further includes second indication information, where the second indication information is used to identify a correspondence between the k cluster center quantization data and the one or more compressed data.

[0044] In this implementation, the receiving end can learn the corresponding relationship between k cluster center quantized data and one or more compressed data.

[0045] In a possible implementation manner, the second data further includes third indication information, where the third indication information is used to indicate k.

[0046] In this implementation, the receiving end can learn the number of cluster center quantization data.

[0047] In a possible implementation, the second data includes first encoding information and second encoding information, the first encoding information includes data of a first dimension in the one or more compressed data, the second encoding information includes data of a second dimension in the one or more compressed data, the data of the first dimension in the one or more compressed data are compressed and encoded based on the data of the first dimension in the first data and the encoding parameters of the first dimension in the first encoding parameters, and the data of the second dimension in the one or more compressed data are compressed and encoded based on the data of the second dimension in the first data and the encoding parameters of the second dimension in the first encoding parameters.

[0048] In this implementation, the second data includes the first encoding information and the second encoding information. Since the data of the same dimension in the one or more compressed data are located in the same information, the data compression speed and the data decompression speed can be improved.

[0049] In one possible implementation, the first compressed data among the one or more compressed data corresponds to the first cluster center quantized data among the k cluster center quantized data, the first encoding information includes the data of the first dimension in the first compressed data, the data of the first dimension in the first compressed data is compressed and encoded by the first quantized value using the encoding parameters of the first dimension in the first encoding parameters, the first quantized value is quantized by the first differential value, and the first differential value is the difference between the data of the first dimension of the element in the first data corresponding to the first cluster center quantized data and the data of the first dimension of the first cluster center quantized data.

[0050] In one possible implementation, the compression encoding of one or more dimensions of the first data based on the first encoding parameter to obtain one or more compressed data includes: determining that the code length of the compressed data obtained by compression encoding the first data based on the information to be compressed corresponding to the first clustering result and the first encoding parameter is the shortest of the multiple code lengths, the first clustering result is a clustering result of the elements in the first data, the information to be compressed includes multiple non-negative integers obtained by mapping multiple quantization values, and the multiple quantization values ​​are quantized by the difference values ​​of the quantization data of the cluster centers corresponding to each element in the first data under the first clustering result. Determining that the code length of the compressed data obtained by compression encoding the first data based on the information to be compressed corresponding to the first clustering result and the first encoding parameter is the shortest of the multiple code lengths can be understood as: (jointly) optimizing the clustering result and the encoding parameters to obtain the optimal encoding parameters.

[0051] In this implementation, encoding parameters for obtaining the shortest code length by performing compression encoding on the first data can be obtained.

[0052] In one possible implementation, the one or more compressed data include f compressed data of the third dimension and g compressed data of the fourth dimension, and the f compressed data of the third dimension and the g compressed data of the fourth dimension are obtained through different compression encoding methods, and f and g are integers greater than 0.

[0053] In this implementation, f compressed data of the third dimension and g compressed data of the fourth dimension are obtained through different compression encoding methods, which is suitable for compression scenarios where the third dimensional data and the fourth dimensional data have different characteristics.

[0054] In a possible implementation, the second data further includes fourth indication information, where the fourth indication information is used to indicate a corresponding relationship between the compressed data of the third dimension and the compressed data of the fourth dimension.

[0055] In this implementation, the receiving end can combine the decompressed third-dimensional data and fourth-dimensional data into two-dimensional data according to the correspondence between the third-dimensional compressed data and the fourth-dimensional compressed data.

[0056] In one possible implementation, the second data also includes one or more cluster centers corresponding to the g compressed data of the fourth dimension, the f compressed data of the third dimension in the second data are arranged in order, and the fourth indication information is used to indicate the compressed data of the third dimension corresponding to the one or more cluster centers, and the order of the entropy coded data corresponding to the same cluster center in the g compressed data of the fourth dimension is the same as the order of the corresponding data of the third dimension.

[0057] In this implementation, the second data also includes one or more cluster centers corresponding to the g fourth-dimensional compressed data, effectively utilizing the dynamic clustering characteristics of the fourth-dimensional data for compression. Furthermore, the fourth indication information is used to indicate the third-dimensional compressed data corresponding to one or more cluster centers. The f third-dimensional compressed data in the second data are arranged in sequence, and the order of the entropy-coded data corresponding to the same cluster center in the g fourth-dimensional compressed data is the same as the order of the corresponding third-dimensional data. This allows the receiving end to determine the correspondence between the third-dimensional compressed data and the fourth-dimensional compressed data, reducing signaling overhead.

[0058] In one possible implementation, the fourth indication information includes a bitmap, the number of bits in the bitmap is equal to f, each bit in the bitmap corresponds to the f compressed data of the third dimension arranged in order, and any bit in the bitmap is used to indicate whether the compressed data of the fourth dimension corresponding to the compressed data of the third dimension corresponding to any bit belongs to the first cluster center, and the first cluster center is one of the multiple cluster centers.

[0059] In this implementation, any bit in the bitmap is used to indicate whether the compressed data of the fourth dimension corresponding to the compressed data of the third dimension corresponding to the any bit belongs to the first cluster center, which can reduce signaling overhead.

[0060] In one possible implementation, the one or more compressed data include t fifth-dimensional data arranged in sequence, and the second data also includes a second quantization value, which is obtained by quantizing the minimum or maximum fifth-dimensional data in the first data. The t fifth-dimensional data arranged in sequence are obtained by performing a differential operation based on the fifth-dimensional data in the first data after being arranged in sequence, and t is an integer greater than or equal to 2.

[0061] In this implementation, the compression performance can be improved by performing differentiation on the t fifth-dimensional data arranged in sequence and then quantizing the obtained differential values.

[0062] In a possible implementation manner, the second data further includes fifth indication information, and the fifth indication information is used to indicate t.

[0063] In one possible implementation, the one or more compressed data also include t data of the sixth dimension arranged in sequence, and the t data of the sixth dimension arranged in sequence are obtained based on multiple differential values ​​obtained by periodically differencing the data of the sixth dimension in the first data. The second data also includes a third quantization value, and the third quantization value is obtained by one of two data with the maximum differential value obtained by differencing the quantized data of the sixth dimension.

[0064] In this implementation, the compression performance can be improved by performing differentiation on the t sequentially arranged data of the sixth dimension and then quantizing the obtained differential values.

[0065] In a possible implementation, the second data further includes sixth indication information, where the sixth indication information is used to indicate a correspondence between the t pieces of fifth-dimensional data and the t pieces of sixth-dimensional data.

[0066] In this implementation, the sixth indication information is used to indicate the correspondence between t fifth-dimensional data and t sixth-dimensional data, so that the receiving end merges the t fifth-dimensional data and the t sixth-dimensional data into t two-dimensional data according to the correspondence.

[0067] In a third aspect, an embodiment of the present application provides a data processing device having the function of implementing the behavior in the method embodiment of the first aspect above. The data processing device can be a communication device, or a component of a communication device (such as a processor, a chip, or a chip system, etc.), or a logic module or software that can implement all or part of the functions of the communication device. The functions of the data processing device can be implemented by hardware, or by hardware executing corresponding software, and the hardware or software includes one or more modules or units corresponding to the above functions. In one possible implementation, the data processing device includes a transceiver module and a processing module, wherein: the processing module is used to compress and encode data of one or more dimensions in the first data based on a first encoding parameter to obtain one or more compressed data; the transceiver module is used to send second data, the second data including the compressed data and first indication information, the first indication information being used to indicate the first encoding parameter; wherein the first encoding parameter satisfies a target condition, the target condition being that the code length obtained by compressing and encoding data of one or more dimensions in the first data based on the first encoding parameter is the shortest of multiple code lengths, the multiple code lengths including code lengths obtained by compressing and encoding data of one or more dimensions in the first data based on multiple sets of encoding parameters, the multiple sets of encoding parameters including the first encoding parameter.

[0068] In one possible implementation, the processing module is further used to obtain the multiple code lengths obtained by compressing and encoding data of one or more dimensions in the first data based on the multiple sets of encoding parameters, and the multiple sets of encoding parameters correspond one-to-one to the multiple code lengths; and determine that the code length obtained by compressing and encoding data of one or more dimensions in the first data based on the first encoding parameters is the shortest one among the multiple code lengths.

[0069] In one possible implementation, the processing module is specifically used to determine that the code length of the compressed data obtained by performing the compression encoding on the first data based on the information to be compressed corresponding to the first clustering result and the first encoding parameter is the shortest of the multiple code lengths, the first clustering result is a clustering result of the elements in the first data, the information to be compressed includes multiple non-negative integers obtained by mapping multiple quantization values, and the multiple quantization values ​​are quantized by the differential values ​​of each element in the first data relative to the corresponding cluster center quantization data under the first clustering result.

[0070] For the technical effects brought about by various possible implementations of the third aspect, reference may be made to the introduction to the technical effects of the first aspect or various possible implementations of the first aspect.

[0071] In a fourth aspect, an embodiment of the present application provides a data processing device having the function of implementing the behavior in the method embodiment of the second aspect above. The data processing device can be a communication device, or a component of a communication device (such as a processor, a chip, or a chip system, etc.), or a logic module or software that can implement all or part of the functions of the communication device. The functions of the data processing device can be implemented by hardware, or by hardware executing corresponding software, and the hardware or software includes one or more modules or units corresponding to the above functions. In one possible implementation, the data processing device includes a transceiver module and a processing module, wherein: the transceiver module is used to receive second data, the second data includes one or more compressed data and first indication information, the first indication information is used to indicate a first encoding parameter, wherein the first encoding parameter satisfies a target condition, the target condition is that the code length obtained by compressing and encoding data of one or more dimensions in the first data based on the first encoding parameter is the shortest of multiple code lengths, the multiple code lengths include code lengths obtained by compressing and encoding data of one or more dimensions in the first data based on multiple sets of encoding parameters, and the multiple sets of encoding parameters include the first encoding parameter; the processing module is used to decode the compressed data in the second data based on the first entropy coding parameter to obtain the first data.

[0072] Regarding the technical effects brought about by various possible implementations of the fourth aspect, reference may be made to the introduction to the technical effects of the second aspect or various possible implementations of the second aspect.

[0073] In a fifth aspect, an embodiment of the present application provides another data processing device, which includes a processor, the processor is coupled to a memory, and the memory is used to store programs or instructions. When the program or instructions are executed by the processor, the method shown in any possible implementation of the first or second aspect above is executed.

[0074] In the embodiment of the present application, during the execution of the above method, the process of sending information (or signals) in the above method can be understood as the process of outputting information based on the instructions of the processor. When outputting information, the processor outputs the information to the transceiver so that it can be transmitted by the transceiver. After being output by the processor, the information may also need to undergo other processing before reaching the transceiver. Similarly, when the processor receives input information, the transceiver receives the information and inputs it into the processor. Furthermore, after the transceiver receives the information, the information may need to undergo other processing before being input into the processor.

[0075] For operations such as sending and / or receiving involved in the processor, unless otherwise specified, or unless they conflict with their actual functions or internal logic in the relevant descriptions, they can be generally understood as instructions output based on the processor.

[0076] During implementation, the processor may be a processor specifically configured to execute these methods, or may be a processor that executes computer instructions in a memory to execute these methods, such as a general-purpose processor. For example, the processor may also be configured to execute a program stored in a memory. When the program is executed, the data processing apparatus performs the method described in the first aspect or any possible implementation of the first aspect.

[0077] In a possible implementation, the memory is located outside the data processing device. In a possible implementation, the memory is located inside the data processing device.

[0078] In a possible implementation, the processor and the memory may also be integrated into one device, that is, the processor and the memory may also be integrated together.

[0079] In a possible implementation, the data processing device further includes a transceiver, and the transceiver is used to receive signals or send signals.

[0080] In a sixth aspect, the present application provides another data processing device, which includes a processing circuit and an interface circuit, wherein the interface circuit is used to acquire data or output data; the processing circuit is used to execute the corresponding method as shown in any possible implementation of the first aspect or the second aspect above.

[0081] In the seventh aspect, the present application provides a computer-readable storage medium, which stores a computer program, and the computer program includes program instructions, which, when executed, enable the computer to execute the method shown in any possible implementation of the first aspect or the second aspect above.

[0082] In an eighth aspect, the present application provides a computer program product, which includes a computer program, and the computer program includes program instructions, which, when executed, enable the computer to execute the method shown in any possible implementation of the first or second aspect above.

[0083] In the ninth aspect, the present application provides a communication system, including a data processing device described in the third aspect or any possible implementation of the third aspect or implementing the sending end function in the embodiment of the present application, and a data processing device described in the fourth aspect or any possible implementation of the fourth aspect or implementing the receiving end function in the embodiment of the present application.

[0084] In the tenth aspect, an embodiment of the present application provides a chip, including a processor and a communication interface, wherein the processor reads instructions stored in a memory through the communication interface and executes the method shown in any one of the first to second aspects above. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the background technology, the drawings required for use in the embodiments of the present application or the background technology will be described below.

[0086] FIG1 is a schematic diagram of ray tracing paths provided in an embodiment of the present application;

[0087] FIG2 is a schematic diagram of the definition of the horizontal angle and vertical angle of the departure angle / arrival angle provided in an embodiment of the present application;

[0088] FIG3 shows an example of ray tracing angle data;

[0089] FIG4 is a schematic diagram of the architecture of a communication system 1000 used in an embodiment of the present application;

[0090] FIG5 is a flow chart of a data compression transmission interaction method provided in an embodiment of the present application;

[0091] FIG6 is a flow chart of another data compression and transmission interaction method provided in an embodiment of the present application;

[0092] FIG7 shows an example of approximating the actual distribution of data using a geometric distribution with parameter p;

[0093] FIG8 is an example of second data provided in an embodiment of the present application;

[0094] FIG9 is a flow chart of another data compression and transmission interaction method provided in an embodiment of the present application;

[0095] FIG10 is a schematic diagram of a code length obtained by calculating a compressed two-dimensional differential value according to an embodiment of the present application;

[0096] FIG11 is an example of an indication of a correspondence relationship between data of different dimensions provided in an embodiment of the present application;

[0097] FIG12 is a flow chart of another data compression and transmission interaction method provided in an embodiment of the present application;

[0098] FIG13 is a flow chart of another data compression and transmission interaction method provided in an embodiment of the present application;

[0099] FIG14 is an example of another second data provided in an embodiment of the present application;

[0100] Figure 15 shows a schematic diagram of the arrival angles of three paths;

[0101] FIG16 is a schematic diagram of horizontal arrival angle differential compression;

[0102] FIG17 is a schematic diagram of differential compression of vertical arrival angle;

[0103] FIG18 is a flow chart of another data compression and transmission interaction method provided in an embodiment of the present application;

[0104] FIG19 shows the compression effect of the adaptive arithmetic coding scheme;

[0105] FIG20 shows the compression effect of the Huffman coding scheme;

[0106] FIG21 shows the compression effect of the clustering + Golomb joint optimization coding scheme;

[0107] Figure 22 shows the compression effect when compressing different numbers of diameters;

[0108] FIG23 is a schematic structural diagram of a data processing device 2300 provided in an embodiment of the present application;

[0109] FIG24 is a schematic structural diagram of another data processing device 240 provided in an embodiment of the present application;

[0110] FIG25 is a schematic structural diagram of another data processing device 250 provided in an embodiment of the present application. DETAILED DESCRIPTION

[0111] The terms "first" and "second" in the specification, claims and drawings of the present application are only used to distinguish different objects, rather than to describe a specific order. It will be understood that the various digital numbers involved in the embodiments of the present application are only for the convenience of description and are not used to limit the scope of the embodiments of the present application. The size of the sequence number of each of the above processes does not mean the order of execution, and the order of execution of each process should be determined by its function and inherent logic. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices.

[0112] References to "embodiments" herein mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it refer to independent or alternative embodiments that are mutually exclusive of other embodiments. It will be understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0113] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification of the present application and the appended claims, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear contrary indication in the context. It should also be understood that the term "and / or" used in the present application refers to and includes any or all possible combinations of one or more listed items. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The term "multiple" used in the present application refers to two or more. In the textual description of the present application, the character " / " generally indicates that the objects associated before and after are in an "or" relationship. The term "at least one" used in the present application refers to one or more.

[0114] It is understood that in each embodiment of the present application, "A corresponds to B" means that there is a corresponding relationship between A and B, and B can be determined according to A. However, it should also be understood that determining (or generating) B according to (or based on) A does not mean that B is determined (or generated) only according to (or based on) A, and B can also be determined (or generated) according to (or based on) A and / or other information.

[0115] In order to facilitate understanding of the solutions of the present application, the following first introduces the terms involved in the embodiments of the present application.

[0116] Air interface native data that meets the characteristics of spatial dynamic aggregation

[0117] The spatial dynamic clustering characteristic means that the data itself has the characteristic of clustering in space, but the location of the clusters changes dynamically. The spatial dynamic clustering characteristic is valid for many air interface native data, such as ray tracing data, the position and speed of multiple targets, and so on. The following uses ray tracing data as an example for explanation. The method in this application can also be applied to other data with similar characteristics, but it will not be repeated here.

[0118] The ray tracing data mainly consists of data of multiple ray paths, where each path includes parameters such as delay, path loss, departure angle (divided into horizontal departure angle and vertical departure angle), and arrival angle (also divided into horizontal and vertical). The principle diagram of each ray tracing path is shown in Figure 1. Figure 1 is a principle diagram of each ray tracing path provided in an embodiment of the present application. In Figure 1, two rectangular blocks represent buildings, and there are multiple ray paths between the transmitter and the receiver, where LoS (line of sight) represents the line of sight between the transmitter and the receiver, and ground represents the ground. Figure 2 is a schematic diagram of the definition of the horizontal and vertical angles of the angle of departure (AoD) / angle of arrival (AoA) provided in an embodiment of the present application. As shown in Figure 2, the azimuth is the horizontal angle (horizontal), which is the angle between the projection of the line connecting the origin and the vertex (zenith) on the horizontal plane (i.e., the plane formed by the x-axis and y-axis) and the y-axis. The vertical angle is the angle between the line connecting the origin and the vertex and the horizontal plane.

[0119] Ray tracing data can be compactly represented as a high-dimensional vector set {x1,x2,…,x N}, where each x i is a multidimensional vector (corresponding to the angle, path loss, or delay in ray tracing data, etc.), and N is the number of data points to be transmitted (i.e., the number of ray tracing paths). In fact, many other native air interface data can also be written in this form. For example, for multi-target positioning data, N is the number of positioning targets, and each x iThis corresponds to the position coordinates of the i-th target. Both ray tracing data and multi-target positioning data are generated by the cellular system's air interface, not by upper-layer applications. Their primary function is to enhance air interface communication performance, so they are referred to as native air interface data. Currently, the most widely known native air interface data in 5G systems is channel state information (CSI).

[0120] Ray tracing data is typically used to estimate large-scale channel fading and assist in network optimization. Future communication systems will significantly improve their perception capabilities and environmental modeling accuracy. Ray tracing data can accurately estimate some main path information and be directly used to assist communications (such as beam management and channel estimation), including but not limited to the following scenarios:

[0121] The terminal device feeds back the main path data of ray tracing to assist the base station in downlink precoding and beam management.

[0122] The base station uses ray tracing data to assist in downlink precoding (no compression required);

[0123] The terminal device feeds back the main path data of ray tracing to assist the base station in downlink precoding;

[0124] The base station sends down some of the main path data of ray tracing to assist the terminal device in downlink reception or uplink transmission.

[0125] In the sidelink scenario, the base station obtains part of the main path data between devices based on the location information of the terminal device and sends it to assist in communication between devices.

[0126] In these scenarios, the transmitter (referred to as the transmitter) sends information about the main paths (such as delay, path loss, and angle of arrival) obtained through ray tracing to the receiver (referred to as the receiver). Depending on the specific application scenario, the compression and transmission of ray tracing data must meet specific accuracy requirements.

[0127] For example, for delayed data, the error between the delay of each path recovered by decompression at the receiving end and the delay of the original data must be strictly less than a given upper bound ∈, where ∈ depends on the receiver bandwidth (time resolution) and the application scenario.

[0128] The compression accuracy requirements for angle data and path loss data are similar.

[0129] Obviously, these requirements are also applicable to the compression feedback of other air interface native data. Here we only use ray tracing data as an example to illustrate.

[0130] Multiple ray tracing paths cluster spatially, and ray tracing data contains strong statistical redundancy. Figure 3 illustrates this clustering property using departure angle and arrival angle as examples. Figure 3 shows an example of ray tracing angle data (only the 10 most powerful paths are shown). As shown in Figure 3, the snowflake-shaped symbols represent the departure angle / arrival angle, and the circles represent the cluster centers. The departure angles of the ray paths cluster spatially, and the arrival angles of the ray paths cluster spatially.

[0131] The following first introduces the communication system to which the data compression scheme and data decompression scheme provided by this application are applicable.

[0132] The data compression scheme and data decompression scheme provided in this application can be applied to long term evolution (LTE) systems, LTE frequency division duplex (FDD) systems, LTE time division duplex (TDD) systems, universal mobile telecommunication systems (UMTS), world-wide interoperability for microwave access (WiMAX) communication systems, fifth generation (5G) communication systems, and future sixth generation (6G) communication systems. Of course, the coding and decoding scheme provided in this application can also be applied to other possible communication systems, for example, in the Internet of Things (IoT) network, a wireless local area network system supporting the 802.11 series of protocols, a wireless personal area network system based on ultra-wideband UWB, a sensing system, and a vehicle to vehicle (V2X), machine type communication (MTC), machine-to-machine information interaction (long term evolution-machine, LTE-M), machine-to-machine communication (M2M), vehicle-to-vehicle communication (V2V), workshop information interaction (long term evolution-vehicle, LTE-V), satellite communication systems, and future wireless communication systems with perception functions, such as next-generation cellular systems, short-range wireless communication systems, etc. The above-mentioned communication systems applicable to this application are only examples, and the communication systems applicable to this application are not limited to these. They are uniformly described here and will not be repeated below.

[0133] Figure 4 is a schematic diagram of the architecture of a communication system 1000 used in an embodiment of the present application. As shown in Figure 4, the communication system includes a radio access network 100 and a core network 200. Optionally, the communication system 1000 may also include the Internet 300. The radio access network 100 may include at least one radio access network device (such as 110a and 110b in Figure 4) and at least one terminal (such as 120a-120j in Figure 4). The terminal (i.e., terminal device) is wirelessly connected to the radio access network device, and the radio access network device is wirelessly or wiredly connected to the core network. The core network device and the radio access network device may be independent and distinct physical devices, or the functions of the core network device and the logical functions of the radio access network device may be integrated into the same physical device, or a single physical device may integrate some of the functions of the core network device and some of the functions of the radio access network device. Terminals and radio access network devices may be connected to each other via wired or wireless connections. FIG4 is only a schematic diagram. The communication system may further include other network devices, such as wireless relay devices and wireless backhaul devices, which are not shown in FIG4 .

[0134] Radio access network equipment is the access device that terminals use to wirelessly access a communication system. Radio access network equipment can be a base station, an evolved NodeB (eNodeB), a transmission reception point (TRP), a next-generation NodeB (gNB) in fifth-generation (5G) mobile communication systems, a next-generation base station in sixth-generation (6G) mobile communication systems, a base station in future mobile communication systems, or an access node in a WiFi system. It can also be a module or unit that performs some of the functions of a base station, such as a centralized unit (CU) or a distributed unit (DU). The CU here completes the functions of the radio resource control protocol and the packet data convergence protocol (PDCP) of the base station, and can also complete the function of the service data adaptation protocol (SDAP); the DU completes the functions of the radio link control layer and the medium access control (MAC) layer of the base station, and can also complete the functions of part of the physical layer or all of the physical layer. For the specific description of the above-mentioned various protocol layers, please refer to the relevant technical specifications of the 3rd Generation Partnership Project (3GPP). The wireless access network device can be a macro base station (such as 110a in Figure 4), a micro base station or an indoor station (such as 110b in Figure 4), or a relay node or a donor node, etc. The embodiments of the present application do not limit the specific technology and specific device form adopted by the wireless access network device. For the convenience of description, the following description takes the base station as an example of the wireless access network device.

[0135] A terminal is a device with wireless transceiver capabilities that can send signals to a base station or receive signals from a base station. A terminal may also be referred to as a terminal device, user equipment (UE), mobile station, mobile terminal, etc. Terminals can be widely used in various scenarios, such as device-to-device (D2D), vehicle-to-everything (V2X) communication, machine-type communication (MTC), Internet of Things (IoT), virtual reality, augmented reality, industrial control, autonomous driving, telemedicine, smart grid, smart furniture, smart office, smart wearable, smart transportation, smart city, etc. A terminal can be a mobile phone, tablet computer, computer with wireless transceiver capabilities, wearable device, vehicle, airplane, ship, robot, robotic arm, smart home device, etc. The embodiments of this application do not limit the specific technology and specific device form adopted by the terminal.

[0136] Base stations and terminals can be fixed or mobile. They can be deployed on land, indoors or outdoors, handheld or vehicle-mounted; on water; or on aircraft, balloons, and satellites. The embodiments of this application do not limit the application scenarios of base stations and terminals.

[0137] The roles of base stations and terminals can be relative. For example, the helicopter or drone 120i in Figure 4 can be configured as a mobile base station. To terminals 120j accessing the wireless access network 100 via 120i, terminal 120i is a base station. However, to base station 110a, 120i is a terminal, meaning that communication between 110a and 120i occurs via a wireless air interface protocol. Of course, communication between 110a and 120i can also occur via a base station-to-base station interface protocol. In this case, 120i is also a base station relative to 110a. Therefore, base stations and terminals can be collectively referred to as communication devices. 110a and 110b in Figure 4 can be referred to as communication devices with base station functionality, while 120a-120j in Figure 4 can be referred to as communication devices with terminal functionality.

[0138] Communication between base stations and terminals, between base stations, and between terminals can be carried out through authorized spectrum, unauthorized spectrum, or both; communication can be carried out through spectrum below 6 gigahertz (GHz), spectrum above 6 GHz, or spectrum below 6 GHz and spectrum above 6 GHz. The embodiments of the present application do not limit the spectrum resources used for wireless communication.

[0139] In the embodiments of the present application, the functions of the base station may also be performed by a module (such as a chip) in the base station, or by a control subsystem that includes the base station functions. The control subsystem that includes the base station functions here may be a control center in the above-mentioned application scenarios such as smart grid, industrial control, smart transportation, and smart city. The functions of the terminal may also be performed by a module (such as a chip or modem) in the terminal, or by a device that includes the terminal functions.

[0140] In this application, a base station sends downlink signals or downlink information to a terminal, and the downlink information is carried on a downlink channel; the terminal sends uplink signals or uplink information to the base station, and the uplink information is carried on an uplink channel. In order to communicate with the base station, the terminal needs to establish a wireless connection with the cell controlled by the base station. The cell with which the terminal has established a wireless connection is called the serving cell of the terminal. When the terminal communicates with the serving cell, it will also be subject to interference from signals in neighboring cells.

[0141] As mentioned in the background section, there is a need to research compression technologies with improved performance for multidimensional data. This application primarily targets multidimensional air interface native data with dynamic clustering characteristics, proposing an efficient compression method that ensures that the accuracy of the decompressed data in one or more dimensions is strictly less than a given threshold. Existing multidimensional data compression methods struggle to meet this requirement. The following, combined with the accompanying figures, describes the data compression and decompression schemes provided by this application.

[0142] FIG5 is a flow chart of a data compression and transmission interaction method provided by an embodiment of the present application. As shown in FIG5 , the method includes:

[0143] 501. The sending end compresses and encodes data of one or more dimensions in the first data based on the first encoding parameter to obtain one or more compressed data.

[0144] The transmitting end may be a terminal or a network device, such as a base station. For example, one or more elements in the first data are one-dimensional data, and the first encoding parameters include encoding parameters for one dimension. The transmitting end uses the encoding parameters for the dimension to perform compression encoding (e.g., entropy encoding) on ​​the one or more elements of the dimension in the first data to obtain second data. For another example, one or more elements in the first data are two-dimensional data, and the first encoding parameters include encoding parameters for the first dimension and encoding parameters for the second dimension. The transmitting end uses the encoding parameters for the first dimension to perform compression encoding on the data of the first dimension in the one or more elements and uses the encoding parameters for the second dimension to perform compression encoding on the data of the second dimension in the one or more elements to obtain second data. One or more elements in the first data may be three-dimensional, four-dimensional, or higher-dimensional. It should be understood that the transmitting end may use the encoding parameters for each dimension in the first encoding parameters in a similar manner to perform compression encoding on the data of the corresponding dimension in one or more elements in the first data. It should be noted that the transmitting end compressing and encoding the data of one or more dimensions in the first data based on the first encoding parameters may be: the transmitting end compresses and encodes part or all of the data in the first data based on the first encoding parameters. That is, the sending end may perform compression encoding on each element in the first data, or may perform compression encoding on only some elements in the first data.

[0145] The above-mentioned first encoding parameter can meet the target condition, and the above-mentioned target condition is that the code length obtained by compressing and encoding the data of one or more dimensions in the above-mentioned first data based on the above-mentioned first encoding parameter is the shortest one of the multiple code lengths. The above-mentioned multiple code lengths include code lengths obtained by compressing and encoding the data of one or more dimensions in the above-mentioned first data based on multiple groups of encoding parameters, and the above-mentioned multiple groups of encoding parameters include the above-mentioned first encoding parameter. In some possible embodiments, before executing step 501, the sending end obtains multiple code lengths obtained by compressing and encoding the data of one or more dimensions in the above-mentioned first data based on multiple groups of encoding parameters; then, determines the encoding parameter corresponding to the shortest one of the multiple code lengths, that is, the first encoding parameter. The first encoding parameter can also be a set of encoding parameters obtained by other means to compress and encode the data of one or more dimensions in the first data to obtain a shorter code length.

[0146] In one possible implementation, before executing step 501, the transmitting end obtains multiple code lengths obtained by compressing and encoding data of one or more dimensions in the first data based on multiple sets of encoding parameters, wherein the multiple sets of encoding parameters correspond one-to-one to the multiple code lengths; and determines that the code length obtained by compressing and encoding data of one or more dimensions in the first data based on the first encoding parameters is the shortest of the multiple code lengths. In this implementation, determining that the code length obtained by compressing and encoding data of one or more dimensions in the first data based on the first encoding parameters is the shortest of the multiple code lengths is performed to obtain optimal encoding parameters.

[0147] 502. The sending end sends second data to the receiving end.

[0148] Correspondingly, the receiving end receives the second data from the sending end. The sending end can be a terminal or a network device. For example, the sending end is any terminal in the communication system shown in Figure 4, and the receiving end is a wireless access network device in the communication system shown in Figure 4. For another example, the sending end is a wireless access network device in the communication system shown in Figure 4, and the receiving end is any terminal in the communication system shown in Figure 4. For another example, the sending end and the receiving end are any two terminals in the communication system shown in Figure 4 that can communicate directly (for example, through the PC5 interface). The second data includes the above-mentioned one or more compressed data and the first indication information. The above-mentioned first indication information is used to indicate the above-mentioned first encoding parameter.

[0149] In one possible implementation, the one or more compressed data include t sequentially arranged data of the fifth dimension, the second data further includes a second quantized value, the second quantized value being quantized by the smallest or largest data of the fifth dimension in the first data, and the t sequentially arranged data of the fifth dimension being obtained by subtracting the data of the fifth dimension in the first data after sequential arrangement, where t is an integer greater than or equal to 2. In this implementation, differencing the t sequentially arranged data of the fifth dimension and then quantizing the obtained differential value can improve compression performance.

[0150] In a possible implementation, the second data further includes fifth indication information, and the fifth indication information is used to indicate the t.

[0151] In one possible implementation, the one or more compressed data further includes t sequentially arranged data of the sixth dimension, where the t sequentially arranged data of the sixth dimension are obtained based on multiple differential values ​​obtained by periodically differencing the data of the sixth dimension in the first data. The second data further includes a third quantized value, where the third quantized value is obtained by differencing two data with the maximum differential value obtained by quantizing the data of the sixth dimension. In this implementation, differencing the t sequentially arranged data of the sixth dimension and then quantizing the obtained differential values ​​can improve compression performance.

[0152] In one possible implementation, the second data further includes sixth indication information, where the sixth indication information is used to indicate a correspondence between the t pieces of fifth-dimensional data and the t pieces of sixth-dimensional data. In this implementation, the sixth indication information is used to indicate a correspondence between the t pieces of fifth-dimensional data and the t pieces of sixth-dimensional data, so that the receiving end can combine the t pieces of fifth-dimensional data and the t pieces of sixth-dimensional data into t pieces of two-dimensional data based on this correspondence.

[0153] 503. The receiving end decodes one or more compressed data in the second data based on the first encoding parameter to obtain first data.

[0154] The transmitting end may be a terminal or a network device, such as a base station. For example, the transmitting end is a terminal and the receiving end is a network device. For another example, the transmitting end is a network device and the receiving end is a terminal. Before executing step 503, the receiving end may determine the encoding parameters of each dimension in the first encoding parameter based on the first indication information in the second data. For example, one or more compressed data in the second data is one-dimensional data, and the first encoding parameter includes the encoding parameter of one dimension. The receiving end uses the encoding parameter of the dimension to decode the one or more compressed data of the dimension in the second data to obtain the first data. For another example, one or more compressed data in the second data is two-dimensional data (or elements), and the first encoding parameter includes the encoding parameter of the first dimension and the encoding parameter of the second dimension. The receiving end uses the encoding parameter of the first dimension to decode the data of the first dimension in the one or more compressed data and uses the encoding parameter of the second dimension to decode the data of the second dimension in the one or more compressed data to obtain the first data. The one or more compressed data in the second data may be three-dimensional, four-dimensional or higher-dimensional data. It should be understood that the receiving end may use the encoding parameters of each dimension in the first encoding parameters in a similar manner to decode data of corresponding dimensions of one or more compressed data in the second data. It should be noted that the receiving end using the encoding parameters of each dimension in the first encoding parameters to decode the entropy-coded data of the corresponding dimension in the second data may be: using the encoding parameters of each dimension in the first encoding parameters to decode part or all of the data of the corresponding dimension in the second data.

[0155] In an embodiment of the present application, the transmitting end compresses and encodes data of one or more dimensions in the first data based on the first encoding parameter. Since the code length obtained by compressing and encoding the data of one or more dimensions in the first data based on the first encoding parameter is the shortest of multiple code lengths, compressing and encoding the data of one or more dimensions in the first data based on the first encoding parameter can improve compression performance, that is, obtain the shortest code length. In addition, the second data sent by the transmitting end to the receiving end includes first indication information for indicating the encoding parameters of each dimension in the first encoding parameter, which can dynamically indicate the encoding parameters.

[0156] FIG6 is a flow chart of another data compression and transmission interactive method provided by an embodiment of the present application. The method flow in FIG6 is a possible implementation of the method described in FIG5. In this implementation, the transmitting end compresses the data using a clustering + entropy coding method, which can improve compression performance by fully utilizing the dynamic clustering characteristics of the data. As shown in FIG6, the method includes:

[0157] 601. The sending end clusters the elements in the first data to obtain k cluster centers.

[0158] The first data may be air interface native data that satisfies the spatial dynamic aggregation characteristics, such as ray tracing data, the position and speed of multiple targets sensed, etc. The elements in the first data may be one-dimensional data, two-dimensional data, or multi-dimensional data, which is not limited in this application. For example, the transmitting end uses the k-means clustering algorithm to cluster the elements in the first data. The clustering parameter k used by the transmitting end, that is, the number of cluster centers, may be configured in advance by the transmitting end, or the transmitting end may dynamically indicate the clustering parameter k with the best compression performance during each transmission. k is an integer greater than 0. The transmitting end may also use other methods to cluster the elements in the first data, which is not limited in this application. The following uses the four angle data of compressed ray tracing as an example to illustrate the details of the solution provided by this application. The transmitting end may represent the angle of each ray path as a four-dimensional vector (horizontally leaving, vertically leaving, horizontally arriving, and vertically arriving), and the order of the four angles is agreed upon by the transmitting end. If only the angle of arrival data is compressed, the two-dimensional vector composed of the two angles of arrival is considered. The solution provided by this application is not limited to the two cases where the dimensions are equal to two or four.

[0159] 602. The transmitting end quantizes k cluster centers respectively to obtain k cluster center quantization data and k quantized reconstruction values.

[0160] The transceiver can configure the quantization accuracy used to quantize the cluster centers in advance. In other words, the transceiver can configure the quantization accuracy of the cluster center data in advance. The transmitter can quantize the k cluster centers according to the configured quantization accuracy to obtain k cluster center quantization data and k quantization reconstruction values. The k cluster center quantization data correspond one-to-one to the k cluster centers. For example, the k cluster centers include cluster center 1, cluster center 2, ..., cluster center k, and the k cluster center quantization data include cluster center quantization data 1, cluster center quantization data 2, ..., cluster center quantization data k. Cluster center quantization data 1 is obtained by quantizing cluster center 1, cluster center quantization data 2 is obtained by quantizing cluster center 2, ..., cluster center quantization data k is obtained by quantizing cluster center k. The k quantization reconstruction values ​​(or reconstruction points) correspond one-to-one to the k cluster center quantization data. Each cluster center quantization data is one or more bit strings, and each bit string corresponds to a dimension. Each quantization reconstruction value is the numerical value represented by the cluster center quantization data corresponding to the quantization reconstruction value. For example, the cluster center quantized data 1 is (110111, 01111110), and the quantized reconstruction value corresponding to the cluster center quantized data 1 is (value A, value B), where value A is the value represented by 110111, and value B is the value represented by 01111110. Each element in the first data corresponds to a cluster center and a quantized reconstruction value. For example, an element in the first data belongs to the class (group) corresponding to cluster center 1, and the element corresponds to cluster center 1. The element corresponds to the quantized reconstruction value corresponding to the cluster center quantized data 1 obtained by quantizing the cluster center 1.

[0161] 603. The transmitting end performs a differential operation on each element in the first data and its corresponding quantized reconstruction value to obtain differential data.

[0162] For example, element 1 in the first data corresponds to quantized reconstruction value 1, and element 2 in the first data corresponds to quantized reconstruction value 2. The sending end differentiates each element in the first data from its corresponding quantized reconstruction value, including: differentiating element 1 from quantized reconstruction value 1 to obtain differential value 1, and differentiating element 2 from quantized reconstruction value 2 to obtain differential value 2. The first differential data includes differential value 1 and differential value 2.

[0163] 604. The transmitting end quantizes each differential value in the differential data to obtain quantized data.

[0164] Quantized data includes quantized values ​​obtained by quantizing each differential value in the differential data. For example, the transmitter performs uniform scalar quantization on each differential value in the differential data according to a preconfigured quantization precision to obtain quantized data. The quantization precision requirements for each dimension of data are preconfigured by the transmitter and receiver. When the differential value is two-dimensional or multi-dimensional data, the transmitter quantizes the data of different dimensions in the differential value separately, and the quantization precision requirements for the data of different dimensions in the differential value may be the same or different.

[0165] 605. The sending end determines, based on the quantized data, a first encoding parameter for compression encoding the first data.

[0166] A possible implementation of step 605 is as follows: the transmitter calculates multiple code lengths obtained by compressing and encoding data of one or more dimensions in the quantized data based on multiple sets of encoding parameters, each code length corresponding to a set of encoding parameters; when the code length obtained by compressing and encoding data of one or more dimensions in the quantized data based on the first encoding parameter is the shortest code length among the multiple code lengths, the transmitter determines to use the first encoding parameter for compressing and encoding the first data. For example, the transmitter calculates the code length obtained by compressing and encoding data of one or more dimensions in the quantized data using each set of encoding parameters in the candidate parameter set; when the code length obtained by compressing and encoding the quantized data using the first encoding parameter is the shortest, the transmitter determines to use the first encoding parameter for entropy encoding the first data. For another example, the transmitter sequentially calculates the code length obtained by compressing and encoding data of one or more dimensions in the quantized data based on each set of encoding parameters in the candidate parameter set; when the code length obtained by compressing and encoding the quantized data using the first encoding parameter is less than a code length threshold, the transmitter determines to use the first encoding parameter for compressing and encoding the first data, and stops calculating the code lengths obtained by compressing and encoding the quantized data using other sets of encoding parameters. In this example, before calculating the code length for compressing and encoding the quantized data using the first coding parameter, the code lengths calculated using the coding parameters in the candidate parameter set are all less than the code length threshold. The code length threshold can be configured based on actual needs and is not limited here. The sending end can also use other methods to determine the first coding parameter to be used for compressing and encoding the first data, which are not limited here.

[0167] An example of a code length obtained by the transmitter through compression encoding of one or more dimensions of quantized data based on a set of coding parameters is as follows: each quantized value in the quantized data is mapped to a non-negative integer to obtain the data to be encoded; and the code length obtained by Golomb encoding each non-negative integer in the data to be encoded based on a set of coding parameters is calculated. One possible way for the transmitter to transform the quantized value into a non-negative integer is to take the absolute value of the quantized value. This method requires that the sign bits of all elements be retained and sent to the receiver. One possible way for the transmitter to transform the quantized value into a non-negative integer is to first determine the value range [ymin, ymax] (where ymin is negative and ymax is positive) of the quantized value (denoted as t), and denote the smaller absolute value of the two as T = min{|ymin|,|ymax|}. Then, t is converted into a non-negative integer through the following transformation f(t):

[0168] f(t) represents a non-negative integer mapped by the quantized value t.

[0169] First, here are the specific compression details of Golomb coding. Let the Golomb code parameter be m and the non-negative integer to be compressed be n. The main steps of compression are as follows:

[0170] 1) Calculate the quotient of n divided by m Sum remainder r = n - qm (0 ≤ r ≤ m - 1);

[0171] 2) Compress the quotient q using a unary code: q is represented as 11…10 (where the number of 1s is q);

[0172] 3) Compress the remainder r into The binary representation of bits.

[0173] The unary encoding of a non-negative integer q is defined as (q-1) 1s followed by a 0. Golomb coding satisfies the following property: let m be the Golomb code parameter and n be the non-negative integer to be compressed. Then the length of the output codeword of the Golomb code after compressing n can be expressed as a function of m and n:

[0174] f(n,m) represents the output codeword length of Golomb code after compressing n. Express Take the value downwards, Indicates that (log2m) is rounded up. According to formula (2), the transmitter can directly calculate the length of the output codeword after compression of n without performing compression operation on n. In formula (2) is the length of the quotient q after unary code compression, and is the length of the binary code representation of the remainder r. Formula (2) shows the output codeword length of the Golomb code after compressing n. It should be understood that the transmitting end can use a similar method to calculate the output codeword length after Golomb coding each non-negative integer in the encoded data, and then obtain the code length obtained by Golomb coding each non-negative integer in the encoded data.

[0175] 606. The sending end compresses and encodes data of one or more dimensions in the quantized data based on the first encoding parameter to obtain one or more compressed data.

[0176] Step 606 may refer to step 501. The second data may include the above-mentioned one or more compressed data, the first indication information and the above-mentioned k cluster center quantization data, and one cluster center quantization data corresponds to one or more compressed data in the above-mentioned second data. k is an integer greater than or equal to 1. For example, the k cluster center quantization data are k bit strings of fixed length, each bit string represents a quantized reconstruction value and corresponds to a cluster center. Each compressed data in the above-mentioned one or more compressed data may correspond to one of the above-mentioned k cluster center quantization data. Optionally, the above-mentioned one or more compressed data are divided into k groups, each group corresponds to one cluster center quantization data, that is, the k groups of compressed data correspond one to one with the k cluster center quantization data.

[0177] In one possible implementation, the second data further includes second indication information, where the second indication information is used to identify a correspondence between the k cluster center quantized data and the one or more compressed data. In this implementation, the receiving end can be informed of the correspondence between the k cluster center quantized data and the one or more compressed data.

[0178] In a possible implementation, the second data further includes third indication information, and the third indication information is used to indicate k. In this implementation, the receiving end can be informed of the number of cluster center quantization data.

[0179] In some possible embodiments, steps 605 and 606 are replaced by: the transmitting end independently performs entropy encoding on the data of the corresponding dimensions of one or more elements in the quantized data based on the encoding parameters of each dimension in the first encoding parameter, and constructs respective Huffman codebooks. That is, the data of different dimensions of one or more elements in the quantized data are independently entropy encoded, that is, different encoding parameters are used for data of different dimensions. Data of different dimensions can also reuse encoding parameters and codebooks. In some possible embodiments, Monte Carlo simulation is used (this process is performed offline) to statistically analyze the probability distribution of non-negative integers sent by the transmitting end, and this probability distribution can be well approximated by a geometric distribution with a parameter p. Figure 7 shows an example of approximating the actual distribution of data using a geometric distribution with a parameter p. In Figure 7, each column represents the probability distribution of a non-negative integer, and the curve represents the geometric distribution with a parameter p.

[0180] To further reduce the parameter indication overhead, the parameter p can be limited to (where m is a positive integer), optimize the parameter m, and the actual probability model is:

[0181] n represents a non-negative integer to be compressed. In practice, the set of alternative parameters can be configured in advance. From the set of alternative parameters Select the best parameter m, that is, the parameter of one dimension in the first encoding parameter, and use bits indicate the subscript of the selected parameter m. For example, the parameter m that minimizes the distance between the probability distribution shown in formula (3) and the statistical probability distribution can be selected. The distance metric can be KL divergence or total variation distance. The first coding parameter includes the following: The optimal parameters of one or more dimensions are selected. It is understood that the optimal parameter m can be optimized offline through MC simulation and configured for the transceiver. The transceiver includes a transmitter (referred to as the transmitter) and a receiver (referred to as the receiver). The transceiver constructs the same Huffman codebook based on this parameter m and uses it for a long time after configuration. Alternatively, the transmitter or receiver can optimize the parameter m based on historical data over a period of time, and if there is a change, an update is triggered.

[0182] In some possible embodiments, step 605 and step 606 are replaced by: representing data of one or more dimensions in the quantized data as a bit string; using Lempel-Ziv coding or adaptive arithmetic coding to compress and encode the bit string in the quantized data to obtain second data, which does not include the first indication information.

[0183] 607. The sending end sends second data to the receiving end.

[0184] Step 607 may refer to step 502 .

[0185] In one possible implementation, the second data includes first and second encoding information. The first encoding information includes data of a first dimension in the one or more compressed data, and the second encoding information includes data of a second dimension in the one or more compressed data. The data of the first dimension in the one or more compressed data is obtained by compression encoding based on the data of the first dimension in the first data and the encoding parameters of the first dimension in the first encoding parameters. The data of the second dimension in the one or more compressed data is obtained by compression encoding based on the data of the second dimension in the first data and the encoding parameters of the second dimension in the first encoding parameters. The second data may also include third and / or fourth encoding information. The third encoding information includes data of a third dimension in the one or more compressed data, and the fourth encoding information includes data of a fourth dimension in the one or more compressed data, where the third and fourth dimensions are different. When the compressed data in the second data is two-dimensional data, the second data includes the first and second encoding information. When the compressed data in the second data is three-dimensional data, the second data includes the first, second, and third encoding information. When the compressed data in the second data is four-dimensional data, the second data includes the first, second, third, and fourth encoding information. The first and second encoded information are merely examples of data of two dimensions within the second data. The second data may include multiple third, fourth, or other encoded information, which are not limited herein. In this implementation, the second data includes the first and second encoded information. Because data of the same dimensions within one or more compressed data are located in the same information, data compression and decompression speeds can be improved.

[0186] In one possible implementation, the first compressed data among the one or more compressed data corresponds to the first cluster center quantized data among the k cluster center quantized data, the first coded information includes the data of the first dimension in the first compressed data, the data of the first dimension in the first compressed data is obtained by compressing and encoding the first quantized value using the coding parameters of the first dimension in the first coding parameters, the first quantized value is quantized by the first differential value, and the first differential value is the difference between the data of the first dimension of an element in the first data corresponding to the first cluster center quantized data and the data of the first dimension of the first cluster center quantized data. This implementation provides how one of the data in the first coded information is obtained. It is understandable that a similar method can be used to obtain the second coded information, the third coded information, etc., which will not be repeated here.

[0187] FIG8 is an example of the second data provided in an embodiment of the present application. The second data in FIG8 is obtained by compressing and encoding ray tracing data (i.e., the first data) including four dimensions: horizontal angle of departure (AoD horizontal), vertical angle of departure (AoD vertical), horizontal angle of arrival (AoA horizontal), and vertical angle of arrival (AoA vertical). As shown in FIG8 , the second data includes: clustering parameter k (optional); cluster center quantization data (center1, center2, ..., center k in FIG8 represent a cluster center quantization data respectively); parameter m1, parameter m2, parameter m3, and parameter m4 are encoding parameters of the four dimensions in the first encoding parameter; one or more compressed data, wherein the first row from top to bottom is the compressed data of AoD horizontal, the second row is the compressed data of AoD vertical, the third row is the compressed data of AoA horizontal, and the fourth row is the compressed data of AoA vertical; various types of number indications that identify the corresponding relationship between k cluster center quantization data and the above one or more compressed data. Taking the AoD horizontal compressed data in Figure 8 as an example, the AoD horizontal compressed data is arranged from left to right. The first compressed data to the s1th compressed data correspond to center 1, the (s1+1)th compressed data to the s2th compressed data correspond to center 2, the (s2+1)th compressed data to the s3th compressed data correspond to center 3, and so on. Here, s1, s2, and s3 are all integers greater than 0. It can be understood that the receiving end can determine the cluster center quantized data corresponding to each compressed data based on the various number indicators. Compressed data of different dimensions share various number indicators that identify the correspondence between k cluster center quantized data and the aforementioned one or more compressed data. The various number indicators in the second data dynamically indicate the number of vectors corresponding to each cluster in the clustering results. As shown in Figure 8, the optimal parameters, namely parameters m1, m2, m3, and m4, are dynamically displayed. When entropy encoding the differential data, the order of the data of different classes must be consistent with the order of the cluster center data.

[0188] 608. The receiving end decodes one or more compressed data in the second data based on the first encoding parameter to obtain the first data.

[0189] Step 608 can refer to step 503. It should also be noted here that when the differential values ​​are compressed and encoded, the order of the differential data of different cluster centers needs to be consistent with the order of the cluster center data, so as to ensure correct decoding at the receiving end. The receiving end can first decode the clustering parameter k, then decode the cluster center quantized data (or quantized reconstruction value), then decode the differential value, and finally add the differential value and the corresponding quantized reconstruction value (it is this step that requires the order of the differential data to be consistent with the order of the cluster center data), and finally restore the original data, that is, the first data.

[0190] In the embodiment of the present application, data is compressed by clustering difference, which can effectively utilize the dynamic aggregation characteristics of the data for compression and has good compression performance.

[0191] FIG9 is a flow chart of another data compression and transmission interactive method provided in an embodiment of the present application. The method flow in FIG9 is a possible implementation of the method described in FIG5. In this implementation, the transmitting end compresses data using a combined optimization method of clustering and entropy coding. By fully utilizing the dynamic aggregation characteristics of the data, compression performance can be improved. As shown in FIG9, the method includes:

[0192] 901. A transmitting end obtains multiple code lengths obtained by compressing and encoding data of one or more dimensions in first data based on multiple combinations of clustering results and encoding parameters.

[0193] The multiple code lengths obtained by compressing and encoding data of one or more dimensions in the first data based on multiple combinations of clustering results and encoding parameters may include: code length #1 obtained by compressing and encoding data of one or more dimensions in the first data based on the combination of clustering result 1 and encoding parameter 1, code length #2 obtained by compressing and encoding data of one or more dimensions in the first data based on the combination of clustering result 1 and encoding parameter 2, code length #3 obtained by compressing and encoding data of one or more dimensions in the first data based on the combination of clustering result 2 and encoding parameter 1, code length #4 obtained by compressing and encoding data of one or more dimensions in the first data based on the combination of clustering result 2 and encoding parameter 2, code length #5 obtained by compressing and encoding data of one or more dimensions in the first data based on the combination of clustering result 1 and encoding parameter 3, code length #6 obtained by compressing and encoding data of one or more dimensions in the first data based on the combination of clustering result 2 and encoding parameter 3, and so on. Clustering result 1 and clustering result 2 are two hypothetical clustering results of elements in the first data. A clustering result may include one or more cluster centers, and a correspondence between each element in the first data and the one or more cluster centers, with each element corresponding to and only corresponding to one cluster center. Coding parameter 1, coding parameter 2, and coding parameter 3 are three different groups of coding parameters. The number of combinations of the above clustering results and coding parameters is not limited. For example, the number of combinations of clustering results and coding parameters is configured according to the processing capability of the sending end and the delay requirement of the first data. It should be understood that the more code lengths the sending end obtains by compressing and encoding the data of one or more dimensions in the first data based on different combinations of clustering results and coding parameters, the more likely it is to obtain the optimal combination of clustering results and coding parameters, and the longer it takes. For another example, the sending end is pre-configured to determine multiple combinations of clustering results and coding parameters according to preset rules, and the preset rules are not limited here.

[0194] The main principle of the method flow in Figure 9 is to determine the combination of the better clustering result and coding parameters through the joint optimization of clustering and entropy coding in order to improve the compression performance. The embodiment of the present application proposes to use the function shown in formula (2) as the loss function of the clustering algorithm (the calculation method is shown in Figure 10 below) and perform joint optimization with the clustering algorithm. Figure 10 is a schematic diagram of a code length obtained by calculating the difference value of the compressed two-dimensional provided by the embodiment of the present application. As shown in Figure 10, x j Represents a two-dimensional data, c i Represents the cluster center quantitative data corresponding to the two-dimensional data, n1 represents x j The first dimension of the data is the same as c i The difference of the first dimension of the data, n2 represents x j The second dimension of data is the same as c iThe difference of the second dimension of the data, CodeLen1 represents the code length obtained by compressing n1 based on the encoding parameter m1, CodeLen2 represents the code length obtained by compressing n2 based on the encoding parameter m2, d(x j , c i ) is equal to the sum of CodeLen1 and CodeLen2, d(x j , c i ) represents the value of x j with c i The code length is obtained by compressing the differential value of . The sender can use an iterative method to achieve joint optimization of clustering and entropy coding. The algorithm iteration steps are similar to the k-means algorithm and can be divided into two steps: assignment and update:

[0195] Step 1: Assign a cluster center to each data point

[0196] Step 2 Update: Update the cluster centers of various types

[0197] The update cluster center can be performed using gradient descent (ignoring rounding in the expression). Since the cluster centers need to be quantized, the update can also be performed directly on the quantized grid. The number of cluster centers assigned in the first step can be fixed, increasing, or decreasing.

[0198] 902. The transmitting end determines that a code length obtained by compressing and encoding data of one or more dimensions in the first data based on a combination of the first clustering result and the first encoding parameter is the shortest one of the above multiple code lengths.

[0199] The first clustering result includes k cluster centers and a corresponding relationship between each element in the first data and the k cluster centers, and each element corresponds to and only corresponds to one cluster center.

[0200] 903. The transmitting end quantizes the k cluster centers respectively to obtain k cluster center quantization data and k quantization reconstruction values.

[0201] Step 903 may refer to step 602 .

[0202] 904. The transmitting end performs a differential operation on each element in the first data and its corresponding quantized reconstruction value to obtain differential data.

[0203] Step 904 may refer to step 603 .

[0204] 905. The transmitting end quantizes each differential value in the differential data to obtain quantized data.

[0205] Step 905 may refer to step 604 .

[0206] 906. The sending end compresses and encodes data of one or more dimensions in the quantized data based on the first encoding parameter to obtain one or more compressed data.

[0207] Step 906 may refer to step 501 .

[0208] 907. The sending end sends second data to the receiving end.

[0209] Step 907 may refer to step 502 .

[0210] 908. The receiving end decodes one or more compressed data in the second data based on the first encoding parameter to obtain the first data.

[0211] Step 908 may refer to step 503 .

[0212] Each time the transmitter transmits data, it (jointly) optimizes the clustering and coding parameters to obtain the optimal parameter m (i.e., the first coded data), the clustering result (i.e., the first clustering result), and the clustering parameter k. The optimal parameter m is dynamically indicated using its subscript (a number of bits) in the candidate parameter set. For example, the candidate parameter set is configured for the transmitter and receiver via higher-layer signaling. An example of the second data sent by the transmitter to the receiver can be seen in Figure 8.

[0213] In an embodiment of the present application, the sending end compresses data by adopting a clustering + entropy coding joint optimization method, and can select the optimal clustering result and coding parameters to improve the compression performance.

[0214] Since data of different dimensions of a high-dimensional vector may meet different clustering characteristics, data of different dimensions are compressed using clustering-based methods (or other methods). After decoding, the receiving end cannot associate data of different dimensions belonging to the same vector. Taking the ray tracing scenario as an example, the clustering of arrival angle data and the clustering of delay or path loss data may be completely different, and the arrival angle and path loss data of the same path need to be matched one by one at the receiving end. The embodiment of the present application proposes to use a fixed order of one of the data as a reference, and to indicate the data of other types (dimensions) through one or more bitmaps, and calibrate them to the given data. Here, taking the ray tracing arrival angle data as an example, the correspondence between the arrival angle and the delay of each path is indicated, so that the arrival angle and delay data are matched one by one. The principle is shown in Figure 11. Figure 11 is an example of an indication of the correspondence between data of different dimensions provided by an embodiment of the present application. Figure 11 takes 20 paths and the number of cluster centers k equal to 3 as an example. As shown in Figure 11, the number of bits in the bit string is equal to the number of elements (one element for each path), and their order corresponds to the ascending order of the delays of the paths (as agreed upon). Bit 1 indicates that the arrival angle of the path with the current delay belongs to the (k-1)th category, and bit 0 indicates that the arrival angle of the path with the current delay does not belong to the (k-1)th category. The order of the elements in the entropy encoding of the arrival angle differential values ​​must remain consistent with the fixed order of the delays. In Figure 11, a bit string (or bitmap) represents a cluster (group), and cluster 1, cluster 2, ..., cluster (k-1) cluster result indicators represent different clusters.

[0215] FIG12 is a flow chart of another data compression and transmission interaction method provided in an embodiment of the present application. The method flow in FIG12 is a possible implementation of the method described in FIG5. As shown in FIG12, the method includes:

[0216] 1201. The sending end arranges the data of the third dimension of each element in the first data in order to obtain first sub-data.

[0217] In this application, the order of arrangement can be from small to large or from large to small.

[0218] 1202. The sending end performs front-to-back differential processing on each element in the first sub-data to obtain first sub-differential data.

[0219] 1203. The transmitting end performs scalar quantization on each differential value in the first sub-differential data to obtain first sub-quantized data.

[0220] 1204. The transmitting end quantizes the first element in the first sub-data to obtain a sub-quantization value.

[0221] 1205. The transmitting end compresses and encodes the quantized value in the first sub-quantized data based on the parameter of the third dimension in the first encoding parameter to obtain f compressed data of the third dimension.

[0222] 1206. The sending end clusters the data of the fourth dimension of the multiple elements in the first data to obtain k cluster centers.

[0223] 1207. The sending end quantizes the k cluster centers respectively to obtain k cluster center quantization data and k quantization reconstruction values.

[0224] 1208. The transmitting end performs a differential operation on the fourth dimension data of each element in the first data and the corresponding quantized reconstruction value to obtain second sub-differential data.

[0225] 1209. The transmitting end quantizes each differential value in the second sub-differential data to obtain second sub-quantized data.

[0226] 1210. The transmitting end compresses and encodes the quantized values ​​in the second sub-quantized data based on the parameters of the fourth dimension in the first encoding parameters to obtain g compressed data of the fourth dimension.

[0227] 1211. The sending end sends second data to the receiving end.

[0228] The second data includes f compressed data of the third dimension, g compressed data of the fourth dimension, the sub-quantization value, k cluster center quantization data, and fourth indication information, wherein the fourth indication information is used to indicate the correspondence between the compressed data of the third dimension and the compressed data of the fourth dimension. The f compressed data of the third dimension in the second data are arranged in sequence, the fourth indication information is used to indicate the compressed data of the third dimension corresponding to the one or more cluster centers, and the order of the entropy coded data corresponding to the same cluster center in the g compressed data of the fourth dimension is the same as the order of the corresponding data of the third dimension.

[0229] In one possible implementation, the fourth indication information includes a bitmap, the number of bits in the bitmap is equal to f, and each bit in the bitmap corresponds to the f compressed data of the third dimension arranged in order, and any bit in the bitmap is used to indicate whether the compressed data of the fourth dimension corresponding to the compressed data of the third dimension corresponding to any bit belongs to the first cluster center, and the first cluster center is one of the multiple cluster centers. The fourth indication information may include multiple bitmaps, and different bitmaps correspond to different cluster centers. In this implementation, any bit in the bitmap is used to indicate whether the compressed data of the fourth dimension corresponding to the compressed data of the third dimension corresponding to any bit belongs to the first cluster center, which can reduce signaling overhead.

[0230] 1212. The receiving end decodes one or more compressed data in the second data based on the first encoding parameter to obtain the first data.

[0231] Step 1212 may refer to step 503 .

[0232] In an embodiment of the present application, the second data also includes one or more cluster centers corresponding to the g fourth-dimensional compressed data, which can effectively utilize the dynamic clustering characteristics of the fourth-dimensional data for compression. In addition, the fourth indication information is used to indicate the third-dimensional compressed data corresponding to one or more cluster centers. The f third-dimensional compressed data in the second data are arranged in sequence, and the order of the entropy coded data corresponding to the same cluster center in the g fourth-dimensional compressed data is the same as the order of the corresponding third-dimensional data. This allows the receiving end to determine the corresponding relationship between the third-dimensional compressed data and the fourth-dimensional compressed data, reducing signaling overhead.

[0233] In some cases, the clustering effect between one-dimensional vector groups is not obvious, but the intervals between them are relatively small. The embodiment of the present application also provides a compression encoding scheme for certain sortable one-dimensional data. Figure 13 is a flow chart of another data compression transmission interaction method provided by the embodiment of the present application. The method flow in Figure 13 is a possible implementation of the method described in Figure 5. As shown in Figure 13, the method includes:

[0234] 1301. The sending end arranges the elements in the first data in order to obtain third data.

[0235] The elements in the first data are one-dimensional data.

[0236] 1302. The sending end performs front-to-back differential processing on each element in the third data to obtain differential data.

[0237] 1303. The transmitting end performs scalar quantization on each differential value in the differential data to obtain quantized data.

[0238] 1304. The transmitting end quantizes the first element in the third data to obtain a second quantization value.

[0239] 1305. The transmitting end performs entropy coding on the quantized values ​​in the quantized data based on the first coding parameter, to obtain t compressed data.

[0240] 1306. The sending end sends second data to the receiving end.

[0241] The second data may include t compressed data and a second quantization value. The second quantization value is obtained by quantizing the minimum or maximum data in the above-mentioned first data. In a possible implementation, the above-mentioned second data also includes fifth indication information, and the above-mentioned fifth indication information is used to indicate the above-mentioned t. Figure 14 is an example of another second data provided in an embodiment of the present application. As shown in Figure 14, the second data includes: first element data, that is, the second quantization value; number indication (t); entropy coding parameter m, that is, the first coding parameter; differential entropy coding data, that is, t compressed data.

[0242] 1307. The receiving end decodes one or more compressed data in the second data based on the first encoding parameter to obtain the first data.

[0243] A possible implementation of step 1307 is as follows: decoding one or more compressed data in the second data based on the first encoding parameter to obtain one or more differential values; adding the one or more differential values ​​to the numerical value corresponding to the second quantization value to obtain the first data.

[0244] In the embodiment of the present application, the compression performance can be improved by performing a differential operation on t sequentially arranged data and then quantizing the obtained differential value.

[0245] The following introduces a more general example of using data spatial correlation to perform compression. Still taking ray tracing data as an example, when the number of paths is relatively small, the clustering characteristics of the angle data may not be obvious, and the compression effect of the method introduced in the previous embodiment is not good. However, it is observed that when the number of paths is small, there is still a certain spatial correlation between the angle data of each path. For example, the arrival angle data in the vertical direction tends to be concentrated in a smaller range, while the arrival angles in the horizontal direction are relatively close to each other. This is mainly caused by the spatial position distribution of strong reflectors in the actual physical space. For this situation, the embodiment of the present application proposes a new compression method using spatial correlation. The core idea is to independently compress the vertical and horizontal arrival angles separately.

[0246] For the horizontal angle of arrival, a periodic difference is first performed with a period of 360 degrees, the largest difference value is discarded, and then only the remaining difference values ​​and the original data ranked first in the difference order are retained. Taking three paths as an example (the arrival angle is shown in Figure 15), the differential compression of the horizontal angle is shown in Figure 16. There are three horizontal arrival angle data in Figure 16, namely h1, h2, and h3, and the cyclic differences between them are d1, d2, and d3. What is finally retained is h1, and the two difference values ​​d1 and d2. d1 and d2 are compressed using the scalar quantization + entropy coding method in the previous embodiment. Figure 15 shows a schematic diagram of the arrival angle of three paths. Figure 16 is a schematic diagram of horizontal arrival angle differential compression.

[0247] For vertical angle of arrival compression, the angles are simply arranged in ascending order and then differentiated. Ultimately, only the smallest angle and subsequent differential values ​​are retained. The principle is shown in Figure 17. Finally, angle v2 (the smallest angle) is quantized, and the differentials s1 and s2 are quantized and entropy coded. Figure 17 shows a schematic diagram of vertical angle of arrival differential compression.

[0248] Finally, the correspondence between horizontal and vertical angles needs to be represented and compressed. A full permutation indication method is used here. The core idea is to retain the subscript of the correspondence to be compressed in the set of all possible correspondences arranged in lexicographic order. Taking three diameters as an example, we can see that the correspondence between the horizontal arrival angles (h1, h2, h3) and the vertical arrival angles (v2, v1, v3) can be recorded as the full permutation {2, 1, 3}. Ultimately, the subscript retained is {2, 1, 3} in the lexicographic order of all full permutations of length 3. For example, if the full permutations of three elements are listed in lexicographic order: {1, 2, 3}, {1, 3, 2}, {2, 1, 3}, {2, 3, 1}, {3, 1, 2}, {3, 2, 1}, then the correspondence {2, 1, 3} is ranked third among all the above correspondences, and its subscript is 3.

[0249] FIG18 is a flow chart of another data compression and transmission interaction method provided by an embodiment of the present application. The method flow in FIG18 is a possible implementation of the method described in FIG5. As shown in FIG18, the method includes:

[0250] 1801. The sending end arranges the data of the fifth dimension of each element in the first data in order to obtain first sub-data.

[0251] 1802. The sending end performs front-to-back differential processing on each element in the first sub-data to obtain first sub-differential data.

[0252] 1803. The transmitting end performs scalar quantization on each differential value in the first sub-differential data to obtain first sub-quantized data.

[0253] 1804. The transmitting end quantizes the first element in the first sub-data to obtain a second quantization value.

[0254] The second quantized value is obtained by quantizing the smallest or largest fifth-dimensional data in the first data.

[0255] 1805. The transmitting end compresses and encodes the quantized value in the first sub-quantized data based on the parameter of the fifth dimension in the first encoding parameter to obtain t compressed data of the fifth dimension.

[0256] 1806. The sending end performs periodic differentiation on the sixth dimension data of each element in the first data with a period of 360 degrees to obtain multiple differential values.

[0257] For example, the data of the sixth dimension of each element in the first data includes h1, h2, and h3, and the cyclic differences between h1, h2, and h3 are d1, d2, and d3, see Figure 16. In this example, d1, d2, and d3 are multiple differential values ​​obtained by periodically differencing the data of the sixth dimension of each element in the first data with a period of 360 degrees.

[0258] 1807. The transmitting end performs scalar quantization on each differential value except the maximum differential value among the plurality of differential values ​​to obtain second sub-quantized data.

[0259] 1808. The sending end compresses and encodes the quantized value in the second sub-quantized data based on the parameter of the sixth dimension in the first encoding parameter to obtain t compressed data of the sixth dimension.

[0260] 1809. The sending end quantizes the target data to obtain a third quantization value.

[0261] The target data is one of two data with the maximum difference value obtained by performing a difference on the data of the sixth dimension of each element in the first data.

[0262] 1810. The sending end sends second data to the receiving end.

[0263] The second data may include t compressed data of the fifth dimension, t compressed data of the sixth dimension, a second quantization value, a third quantization value and sixth indication information, wherein the sixth indication information is used to indicate the correspondence between the t fifth-dimensional data and the t sixth-dimensional data.

[0264] 1811. The receiving end decodes one or more compressed data in the second data based on the first encoding parameter to obtain the first data.

[0265] In the embodiment of the present application, the data of the fifth dimension and the data of the sixth dimension in the first data are compressed separately and independently, and the compression performance can be improved by utilizing the spatial correlation of the data.

[0266] The following simulation is used to illustrate the main beneficial effects of the solution provided in the embodiment of the application.

[0267] First, the performance simulation results of compressing ray tracing angle data from 150 randomly located receivers are presented. The simulations were set up to consider only the arrival angle data from the 10 most powerful paths, and the reconstruction error of the angle data was required to be strictly less than 360 / 256 = 1.4063 degrees. Figures 19, 20, and 21 respectively show the compression effects of the adaptive arithmetic coding scheme, Huffman coding scheme, and clustering + Golomb joint optimization coding scheme described above. Figure 19 shows the compression effect of the adaptive arithmetic coding scheme. Figure 20 shows the compression effect of the Huffman coding scheme. Figure 21 shows the compression effect of the clustering + Golomb joint optimization coding scheme (hereinafter referred to as the joint optimization scheme). The horizontal axis in the figure represents the data subscript, with a total of 150 data sets. The vertical axis represents the amount of compressed data output. The reference schemes used in the simulations in each figure directly quantize each element data according to the given accuracy requirements.

[0268] Figure 22 shows the compression effect of each scheme under different simulation parameter configurations (mainly compressing different numbers of paths). It can be seen that the overall trend is that the larger the number of compressed paths, the better the compression effect. This is because the spatial aggregation effect is stronger when the number of paths is large, so the compression scheme based on clustering will have better performance. Figure 22 shows the compression effect when compressing different numbers of paths. On the contrary, when the number of paths is small, the spatial aggregation effect is weakened and the overall redundancy is reduced, so the compression effect of the scheme will also be worse. In Figure 22, the reference scheme (baseline) is to directly quantize each element data according to the given accuracy requirements; scheme (scheme) 1 is an adaptive arithmetic code scheme; scheme (scheme) 2 is a Huffman coding scheme; scheme (scheme) 3 is a joint optimization scheme; scheme (scheme) 4 is the compression coding scheme in the method flow of Figure 13.

[0269] The following describes the structure of the data processing device (transmitting end and receiving end) provided by the embodiment of the present application in conjunction with the accompanying drawings.

[0270] Figure 23 is a schematic diagram of the structure of a data processing device 2300 provided in an embodiment of the present application. The data processing device 2300 can implement the functions or steps implemented by the transmitting end in each of the above-mentioned method embodiments, and can also implement the functions or steps implemented by the receiving end in each of the above-mentioned method embodiments. The data processing device may include a processing module 2310 and a transceiver module 2320. Optionally, it may also include a storage unit, which can be used to store instructions (code or program) and / or data. The processing module 2310 and the transceiver module 2320 can be coupled to the storage unit. For example, the processing module 2310 can read the instructions (code or program) and / or data in the storage unit to implement the corresponding method. The above-mentioned units can be provided independently or partially or fully integrated. For example, the transceiver module 2320 may include a transmitting module and a receiving module. The transmitting module can be a transmitter, and the receiving module can be a receiver. The entity corresponding to the transceiver module 2320 can be a transceiver or a communication interface.

[0271] In some possible implementations, the data processing device 2300 can implement the behaviors and functions of the transmitting end in the above-mentioned method embodiments. For example, the data processing device 2300 can be a transmitting end, or it can be a component (such as a chip or circuit) applied to the transmitting end. The transceiver module 2320 can be used to perform all receiving or sending operations performed by the transmitting end in the embodiments of Figures 5, 6, 9, 12, 13, and 18, and / or other processes for supporting the technology described herein. The processing module 2310 is used to perform all operations except the transceiver operations performed by the transmitting end in the embodiments of Figures 5, 6, 9, 12, 13, and 18.

[0272] In some possible implementations, the data processing device 2300 can implement the behaviors and functions of the receiving end in the above-mentioned method embodiments. For example, the data processing device 2300 can be a receiving end, or it can be a component (such as a chip or circuit) used in the receiving end. The transceiver module 2320 can be used to perform all receiving or sending operations performed by the receiving end in the embodiments of Figures 5, 6, 9, 12, 13, and 18, and / or other processes for supporting the technology described herein. The processing module 2310 is used to perform all operations except the transceiver operations performed by the receiving end in the embodiments of Figures 5, 6, 9, 12, 13, and 18.

[0273] Figure 24 is a schematic diagram of the structure of another data processing device 240 provided in an embodiment of the present application. The data processing device in Figure 24 can be the above-mentioned sending end or the above-mentioned receiving end.

[0274] As shown in FIG. 24 , the data processing device 240 includes at least one processor 2410 and a transceiver 2420 .

[0275] In some embodiments of the present application, the processor 2410 and the transceiver 2420 may be configured to execute functions or operations performed by the transmitting end. The transceiver 2420, for example, executes all receiving or transmitting operations performed by the transmitting end in the embodiments of Figures 5, 6, 9, 12, 13, and 18, and / or other processes for supporting the techniques described herein. The processor 2410, for example, executes all operations performed by the transmitting end in the embodiments of Figures 5, 6, 9, 12, 13, and 18, except for the transmitting and receiving operations.

[0276] In some embodiments of the present application, the processor 2410 and the transceiver 2420 may be configured to execute functions or operations performed by an access terminal. For example, the transceiver 2420 executes all receiving or transmitting operations performed by the receiving terminal in the embodiments of Figures 5, 6, 9, 12, 13, and 18, and / or other processes for supporting the techniques described herein. The processor 2410 may be configured to execute all operations performed by the receiving terminal in the embodiments of Figures 5, 6, 9, 12, 13, and 18, except for the transmitting and receiving operations.

[0277] Transceiver 2420 is used to communicate with other devices / apparatuses via a transmission medium. Processor 2410 utilizes transceiver 2420 to transmit and receive data and / or signaling, and is used to implement the methods described in the above method embodiments. Processor 2410 can implement the functions of processing module 2310, and transceiver 2420 can implement the functions of transceiver module 2320.

[0278] Optionally, transceiver 2420 may include a radio frequency circuit and an antenna. The radio frequency circuit is primarily used for converting baseband signals into radio frequency signals and processing radio frequency signals. The antenna is primarily used for transmitting and receiving radio frequency signals in the form of electromagnetic waves. Input and output devices, such as a touch screen, display, and keyboard, are primarily used for receiving user input and outputting data to the user.

[0279] Optionally, the data processing device 240 may further include at least one memory 2430 for storing program instructions and / or data. The memory 2430 is coupled to the processor 2410. Coupling in the embodiments of the present application is an indirect coupling or communication connection between devices, units, or modules, which may be electrical, mechanical, or other forms, and is used for information exchange between devices, units, or modules. The processor 2410 may operate in conjunction with the memory 2430. The processor 2410 may execute program instructions stored in the memory 2430. At least one of the at least one memory may be included in the processor.

[0280] When data processing device 240 is powered on, processor 2410 can read the software program in memory 2430, interpret and execute the instructions of the software program, and process the data of the software program. When data needs to be transmitted wirelessly, processor 2410 performs baseband processing on the data to be transmitted and outputs the baseband signal to the radio frequency circuit. The radio frequency circuit performs radio frequency processing on the baseband signal and then transmits the radio frequency signal to the outside in the form of electromagnetic waves via the antenna. When data is sent to the data processing device, the radio frequency circuit receives the radio frequency signal via the antenna, converts the radio frequency signal into a baseband signal, and outputs the baseband signal to processor 2410. Processor 2410 converts the baseband signal into data and processes the data.

[0281] In another implementation, the above-mentioned RF circuit and antenna can be set independently of the processor performing baseband processing. For example, in a distributed scenario, the RF circuit and antenna can be arranged remotely from the data processing device.

[0282] The specific connection medium between the transceiver 2420, processor 2410, and memory 2430 is not limited in the embodiments of the present application. In Figure 24, the memory 2430, processor 2410, and transceiver 2420 are connected via a bus 2440. The bus is represented by a bold line in Figure 24. The connection methods between other components are merely schematic and are not limiting. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, Figure 24 only uses a single bold line, but this does not mean that there is only one bus or only one type of bus.

[0283] In the embodiments of the present application, the processor may be a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component, and may implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present application. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of the present application may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0284] Figure 25 is a schematic diagram of the structure of another data processing device 250 provided in an embodiment of the present application. As shown in Figure 25, the data processing device shown in Figure 25 includes a logic circuit 2501 and an interface 2502. The processing module 2310 in Figure 23 can be implemented with a logic circuit 2501, and the transceiver module 2320 in Figure 23 can be implemented with an interface 2502. Among them, the logic circuit 2501 can be a chip, a processing circuit, an integrated circuit or a system on chip (SoC) chip, etc., and the interface 2502 can be a communication interface, an input and output interface, etc. In the embodiment of the present application, the logic circuit and the interface can also be coupled to each other. The embodiment of the present application does not limit the specific connection method of the logic circuit and the interface.

[0285] In some embodiments of the present application, the logic circuit and interface may be used to execute the functions or operations performed by the aforementioned transmitting end.

[0286] In some embodiments of the present application, the logic circuit and interface may be used to execute the functions or operations performed by the aforementioned receiving end.

[0287] The present application also provides a computer-readable storage medium having a computer program or instruction stored therein, and when the computer program or instruction is run on a computer, the computer executes the method of the above embodiment. The computer-readable storage medium can be any available medium that can be accessed by the computer or a data storage device such as a server or a data center that includes one or more available media integrations. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state drive (SSD)). The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0288] It should be noted that those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be performed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. Computer-readable storage media include read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0289] The present application also provides a computer program product comprising instructions or a computer program that, when executed on a computer, causes the methods of the above embodiments to be performed. The computer program product comprises one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are fully or partially generated.

[0290] The above embodiments may be implemented in whole or in part through software, hardware, firmware, or any combination thereof. When implemented using software, they may be implemented in whole or in part in the form of a computer program product.

[0291] The present application also provides a communication system, comprising the above-mentioned transmitting end and the above-mentioned receiving end.

[0292] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the above claims.

Claims

1. A data compression method, characterized in that: include: Compress and encode data of one or more dimensions in the first data based on the first encoding parameter to obtain one or more compressed data; Sending second data, where the second data includes the compressed data and first indication information, where the first indication information is used to indicate the first encoding parameter; The first encoding parameter satisfies a target condition, which is that a code length obtained by compressing and encoding data of one or more dimensions in the first data based on the first encoding parameter is the shortest of multiple code lengths, and the multiple code lengths include code lengths obtained by compressing and encoding data of one or more dimensions in the first data based on multiple groups of encoding parameters, respectively, and the multiple groups of encoding parameters include the first encoding parameter.

2. The method according to claim 1, characterized in that The second data also includes k cluster center quantization data, at least one compressed data among the one or more compressed data corresponds to one of the k cluster center quantization data, and k is an integer greater than or equal to 1.

3. The method according to claim 2, characterized in that The second data also includes second indication information, and the second indication information is used to identify the corresponding relationship between the k cluster center quantization data and the one or more compressed data.

4. The method according to claim 2 or 3, characterized in that: The second data also includes third indication information, and the third indication information is used to indicate the k.

5. The method according to any one of claims 2 to 4, characterized in that: The second data includes first encoding information and second encoding information, the first encoding information includes data of a first dimension in the one or more compressed data, the second encoding information includes data of a second dimension in the one or more compressed data, the data of the first dimension in the one or more compressed data are compressed and encoded based on the data of the first dimension in the first data and the encoding parameters of the first dimension in the first encoding parameters, and the data of the second dimension in the one or more compressed data are compressed and encoded based on the data of the second dimension in the first data and the encoding parameters of the second dimension in the first encoding parameters.

6. The method according to claim 1, characterized in that The one or more compressed data include f compressed data of the third dimension and g compressed data of the fourth dimension, and the f compressed data of the third dimension and the g compressed data of the fourth dimension are obtained through different compression encoding methods, and f and g are integers greater than 0.

7. The method according to claim 6, characterized in that The second data further includes fourth indication information, where the fourth indication information is used to indicate a corresponding relationship between the compressed data of the third dimension and the compressed data of the fourth dimension.

8. The method according to claim 7, characterized in that The second data also includes one or more cluster centers corresponding to the g compressed data of the fourth dimension. The f compressed data of the third dimension in the second data are arranged in order. The fourth indication information is used to indicate the compressed data of the third dimension corresponding to the one or more cluster centers. The order of the entropy coded data corresponding to the same cluster center in the g compressed data of the fourth dimension is the same as the order of the corresponding data of the third dimension.

9. A data decompression method, characterized in that: include: receiving second data, the second data including one or more compressed data and first indication information, the first indication information being used to indicate a first encoding parameter, wherein the first encoding parameter satisfies a target condition, the target condition being that a code length obtained by compressing and encoding data of one or more dimensions in the first data based on the first encoding parameter is the shortest of multiple code lengths, the multiple code lengths including code lengths obtained by compressing and encoding data of one or more dimensions in the first data based on multiple sets of encoding parameters, respectively, the multiple sets of encoding parameters including the first encoding parameter; The compressed data in the second data is decoded based on the first encoding parameter to obtain the first data.

10. The method according to claim 9, characterized in that The second data also includes k cluster center quantization data, at least one compressed data among the one or more compressed data corresponds to one of the k cluster center quantization data, and k is an integer greater than or equal to 1.

11. The method according to claim 10, characterized in that The second data also includes second indication information, where the second indication information identifies the corresponding relationship between the k cluster center quantization data and the one or more compressed data.

12. The method according to claim 10 or 11, characterized in that: The second data also includes third indication information, and the third indication information is used to indicate the k.

13. The method according to any one of claims 10 to 12, characterized in that: The second data includes first encoding information and second encoding information, the first encoding information includes data of a first dimension in the one or more compressed data, the second encoding information includes data of a second dimension in the one or more compressed data, the data of the first dimension in the one or more compressed data are compressed and encoded based on the data of the first dimension in the first data and the encoding parameters of the first dimension in the first encoding parameters, and the data of the second dimension in the one or more compressed data are compressed and encoded based on the data of the second dimension in the first data and the encoding parameters of the second dimension in the first encoding parameters.

14. The method according to claim 9, characterized in that The one or more compressed data include f compressed data of the third dimension and g compressed data of the fourth dimension, and the f compressed data of the third dimension and the g compressed data of the fourth dimension are obtained through different compression encoding methods, and f and g are integers greater than 0.

15. The method according to claim 14, characterized in that The second data further includes fourth indication information, where the fourth indication information is used to indicate a corresponding relationship between the compressed data of the third dimension and the compressed data of the fourth dimension.

16. The method according to claim 15, characterized in that The second data also includes one or more cluster centers corresponding to the g compressed data of the fourth dimension. The f compressed data of the third dimension in the second data are arranged in order. The fourth indication information is used to indicate the compressed data of the third dimension corresponding to the one or more cluster centers. The order of the entropy coded data corresponding to the same cluster center in the g compressed data of the fourth dimension is the same as the order of the corresponding data of the third dimension.

17. A data processing device, characterized in that: include: A processing module, configured to compress and encode data of one or more dimensions in the first data based on the first encoding parameter to obtain one or more compressed data; a transceiver module, configured to send second data, where the second data includes the compressed data and first indication information, where the first indication information is used to indicate the first encoding parameter; The first encoding parameter satisfies a target condition, which is that a code length obtained by compressing and encoding data of one or more dimensions in the first data based on the first encoding parameter is the shortest of multiple code lengths, and the multiple code lengths include code lengths obtained by compressing and encoding data of one or more dimensions in the first data based on multiple groups of encoding parameters, respectively, and the multiple groups of encoding parameters include the first encoding parameter.

18. The device according to claim 17, characterized in that The second data also includes k cluster center quantization data, at least one compressed data among the one or more compressed data corresponds to one of the k cluster center quantization data, and k is an integer greater than or equal to 1.

19. The device according to claim 18, characterized in that The second data also includes second indication information, where the second indication information identifies the corresponding relationship between the k cluster center quantization data and the one or more compressed data.

20. The device according to claim 18 or 19, characterized in that The second data also includes third indication information, and the third indication information is used to indicate the k.

21. The device according to any one of claims 18 to 20, characterized in that The second data includes first encoding information and second encoding information, the first encoding information includes data of a first dimension in the one or more compressed data, the second encoding information includes data of a second dimension in the one or more compressed data, the data of the first dimension in the one or more compressed data are compressed and encoded based on the data of the first dimension in the first data and the encoding parameters of the first dimension in the first encoding parameters, and the data of the second dimension in the one or more compressed data are compressed and encoded based on the data of the second dimension in the first data and the encoding parameters of the second dimension in the first encoding parameters.

22. The device according to claim 17, characterized in that The one or more compressed data include f compressed data of the third dimension and g compressed data of the fourth dimension, and the f compressed data of the third dimension and the g compressed data of the fourth dimension are obtained through different compression encoding methods, and f and g are integers greater than 0.

23. The device according to claim 22, characterized in that The second data further includes fourth indication information, where the fourth indication information is used to indicate a corresponding relationship between the compressed data of the third dimension and the compressed data of the fourth dimension.

24. The device according to claim 23, characterized in that The second data also includes one or more cluster centers corresponding to the g compressed data of the fourth dimension. The f compressed data of the third dimension in the second data are arranged in order. The fourth indication information is used to indicate the compressed data of the third dimension corresponding to the one or more cluster centers. The order of the entropy coded data corresponding to the same cluster center in the g compressed data of the fourth dimension is the same as the order of the corresponding data of the third dimension.

25. A data processing device, characterized in that: include: a transceiver module, configured to receive second data, the second data including one or more compressed data and first indication information, the first indication information being used to indicate a first encoding parameter, wherein the first encoding parameter satisfies a target condition, the target condition being that a code length obtained by compressing and encoding data of one or more dimensions in the first data based on the first encoding parameter is the shortest of multiple code lengths, the multiple code lengths including code lengths obtained by compressing and encoding data of one or more dimensions in the first data based on multiple sets of encoding parameters, respectively, the multiple sets of encoding parameters including the first encoding parameter; A processing module is used to decode the compressed data in the second data based on the first entropy coding parameter to obtain the first data.

26. The device according to claim 25, characterized in that The second data also includes k cluster center quantization data, at least one compressed data among the one or more compressed data corresponds to one of the k cluster center quantization data, and k is an integer greater than or equal to 1.

27. The device according to claim 26, characterized in that The second data also includes second indication information, where the second indication information identifies the corresponding relationship between the k cluster center quantization data and the one or more compressed data.

28. The device according to claim 26 or 27, characterized in that The second data also includes third indication information, and the third indication information is used to indicate the k.

29. The device according to any one of claims 26 to 28, characterized in that The second data includes first encoding information and second encoding information, the first encoding information includes data of a first dimension in the one or more compressed data, the second encoding information includes data of a second dimension in the one or more compressed data, the data of the first dimension in the one or more compressed data are compressed and encoded based on the data of the first dimension in the first data and the encoding parameters of the first dimension in the first encoding parameters, and the data of the second dimension in the one or more compressed data are compressed and encoded based on the data of the second dimension in the first data and the encoding parameters of the second dimension in the first encoding parameters.

30. The device according to claim 25, characterized in that The one or more compressed data include f compressed data of the third dimension and g compressed data of the fourth dimension, and the f compressed data of the third dimension and the g compressed data of the fourth dimension are obtained through different compression encoding methods, and f and g are integers greater than 0.

31. The device according to claim 30, characterized in that The second data further includes fourth indication information, where the fourth indication information is used to indicate a corresponding relationship between the compressed data of the third dimension and the compressed data of the fourth dimension.

32. The device according to claim 31, characterized in that The second data also includes one or more cluster centers corresponding to the g compressed data of the fourth dimension. The f compressed data of the third dimension in the second data are arranged in order. The fourth indication information is used to indicate the compressed data of the third dimension corresponding to the one or more cluster centers. The order of the entropy coded data corresponding to the same cluster center in the g compressed data of the fourth dimension is the same as the order of the corresponding data of the third dimension.

33. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed, the computer executes the method as claimed in any one of claims 1 to 8, or when the program instructions are executed, the computer executes the method as claimed in any one of claims 9 to 16.

34. A communication device, characterized in that: The method comprises a processor, wherein the processor is configured to, when executing instructions, cause the communication device to execute the method according to any one of claims 1 to 8, or cause the communication device to execute the method according to any one of claims 9 to 16.

35. The device according to claim 34, characterized in that The apparatus also includes a memory for storing the instructions.

36. A communication system, characterized in that: The method comprises the communication device according to any one of claims 17 to 24 and the communication device according to any one of claims 25 to 32.

37. A computer program product, characterized in that The computer program product comprises a computer program, wherein the computer program comprises program instructions, and when the program instructions are executed, the computer is caused to perform the method according to any one of claims 1 to 16.

38. A chip, characterized in that: include: A communication interface, used for sending and receiving signals of the chip; A processor, configured to execute computer program instructions so that a data processing device including the chip executes the method according to any one of claims 1 to 16.

Images