Entropy coding and decoding method and device

Abstract

The embodiment of the invention provides an entropy coding and decoding method and device. The entropy coding method comprises the following steps: acquiring first data, a probability distribution index of the first data and preset information; the preset information comprises N critical values and N quantitative probability groups corresponding to N probability distributions; each probability distribution in the N probability distributions corresponds to a critical value and a quantitative probability group; the relative entropy of n probability distributions in the N probability distributions is the optimal relative entropy; n is greater than a preset value; searching a critical value and a quantitative probability group corresponding to the first data from preset information according to the probability distribution index of the first data; and performing entropy coding on the first data according to the critical value and the quantization probability group corresponding to the first data. Therefore, the length of the code stream obtained by entropy coding can be shortened.

Description

[0001] This application is a divisional application. The original application has the application number 202311386262.1 and the original application date is October 23, 2023. The entire contents of the original application are incorporated herein by reference. Technical Field

[0002] This application relates to the field of encoding and decoding, and more particularly to an entropy encoding and decoding method and apparatus. Background Technology

[0003] Audio and video encoding (audio and video encoding and decoding) and image encoding and decoding (image encoding and decoding) are widely used in digital video applications, such as broadcast digital television, video transmission over the Internet and mobile networks, real-time conversation applications such as video chat and video conferencing, digital variadic discs (DVD) and Blu-ray discs, video content capture and editing systems, and security applications for portable cameras.

[0004] Even in shorter videos, a large amount of audio and video data needs to be described, which can be challenging when this data needs to be sent over bandwidth-constrained networks or transmitted otherwise. Therefore, audio and video data, or image data, is typically compressed before transmission over modern telecommunications networks. The size of audio and video data, or image data, can also be a problem when stored on storage devices due to potentially limited memory resources. Audio and video compression devices typically use software and / or hardware at the source side to encode the data before transmission or storage, thereby reducing the amount of data required to represent digital audio and video or images. The compressed data is then received at the destination by audio and video decompression devices. Given limited network resources and the growing demand for higher audio and video or image quality, there is a need for improved compression and decompression techniques that can increase compression ratios with minimal impact on image quality.

[0005] Among them, the audio, video or image compression process usually involves entropy coding; and the critical value and quantization probability group on which entropy coding depends affect the length of the encoded bitstream. Summary of the Invention

[0006] This application provides an entropy encoding / decoding method and apparatus. This entropy encoding method can shorten the length of the bitstream obtained by entropy encoding and reduce bit rate overhead.

[0007] In a first aspect, embodiments of this application provide an entropy encoding method, which includes: firstly, acquiring first data, a probability distribution index of the first data, and preset information; wherein, the preset information includes N critical values ​​and N quantized probability groups corresponding to N probability distributions; each of the N probability distributions corresponds to a critical value and a quantized probability group; the relative entropy of the n probability distributions is the optimal relative entropy; the relative entropy of the first probability distribution is used to characterize the distance between the reference probability distribution corresponding to the first probability distribution and the first probability distribution, the first probability distribution being any one of the n probability distributions; N is a positive integer, n is an integer between 1 and N, and n is greater than a preset value; each of the N probability distributions corresponds to a probability distribution index; then, according to the probability distribution index of the first data, searching for the critical value and quantized probability group corresponding to the first data from the preset information; and entropy encoding the first data according to the critical value and quantized probability group corresponding to the first data.

[0008] Here, the preset value can refer to the number of probability distributions among the N probability distributions of the preset information in the prior art that have the optimal relative entropy. Thus, in the entropy coding process of this application, the sum of the relative entropies of the N probability distributions in the preset information used is less than the sum of the relative entropies of the N probability distributions in the preset information used in the entropy coding process of the prior art. Since entropy coding is performed based on N probability distributions with a smaller sum of relative entropy, the resulting bitstream length is smaller; therefore, the entropy coding method of this application can shorten the length of the bitstream obtained by entropy coding and reduce the bitrate overhead.

[0009] For example, a probability distribution index can be used to uniquely identify a probability distribution.

[0010] For example, there is a one-to-one correspondence between the N probability distribution indices and the N probability distributions.

[0011] For example, the N probability distribution indices correspond one-to-one with the N critical values.

[0012] For example, there is a one-to-one correspondence between the N probability distribution indices and the N quantized probability groups.

[0013] For example, a single quantized probability set includes one or more numerical values.

[0014] For example, each of the N probability distributions is a quantized probability distribution obtained by quantizing the corresponding reference probability distribution.

[0015] For example, relative entropy can also be called KL distance; where KL distance is short for Kullback-Leibler difference.

[0016] In one possible scenario, the first data could be any one of image data, audio data, or video data.

[0017] In one possible scenario, the first data can be any one of processed image data (such as features of image data), processed audio data (such as features of audio data), or processed video data (such as features of video data).

[0018] It should be noted that this application does not impose any restrictions on N.

[0019] According to the first aspect, the preset information is calculated based on the type of reference probability distribution with the goal of minimizing relative entropy.

[0020] For example, for the i-th probability distribution (i is an integer between 1 and N), a critical value bound_table_r[i] and a quantized probability group pdf_r[i] can be calculated based on the type of the reference probability distribution corresponding to the i-th probability distribution with the goal of minimizing the relative entropy.

[0021] It should be noted that the N reference probability distributions corresponding to the N probability distributions (one probability distribution corresponds to one reference probability distribution) are of the same type; this application does not restrict the type of reference probability distribution.

[0022] Based on the first aspect, or any of the above implementations of the first aspect, the reference probability distribution type is a Gaussian probability distribution type.

[0023] It should be understood that the reference probability distribution can also be other probability distribution types, such as binomial distribution, Poisson distribution, etc.

[0024] According to the first aspect, or any implementation of the first aspect above, n = N. That is to say, the relative entropy of the N probability distributions is the optimal relative entropy; in this way, the length of the bitstream obtained by entropy coding can be shortened to the greatest extent.

[0025] According to the first aspect, or any implementation of the first aspect above, N is 32. Compared with the prior art where N is 35 (i.e., the preset information in the prior art includes 35 critical values ​​and 35 quantized probability groups corresponding to 35 probability distributions), this application can reduce the redundancy of the preset information and reduce the memory occupied by the preset information.

[0026] According to the first aspect, or any implementation of the first aspect above, the quantized probability set corresponding to the second probability distribution includes H values, where H is determined based on the critical value corresponding to the second probability distribution, H is a positive integer, and the second probability distribution is any one of the N probability distributions.

[0027] According to the first aspect, or any of the above implementations of the first aspect, the range of values ​​corresponding to the second probability distribution is [-(bound_table_r[i]-1), (bound_table_r[i]-1)], where H is the sum of the number of integers in the range, and the step size of the integers in the range is M, where M is a positive integer;

[0028] Where bound_table_r[i] is the critical value corresponding to the second probability distribution, i is the probability distribution index of the second probability distribution, and i is a positive integer.

[0029] For example, starting from the boundary value of the value range corresponding to the second probability distribution, values ​​can be taken from the value range according to the value step size M to obtain the integers within the value range.

[0030] For example, suppose bound_table_r[i] = 2; if M = 1, then the integers in the range [] - 1, 1] include: -1, 0, 1, H = 3. If M = 2, the integers in the range [] - 1, 1] include -1, 1, H = 2.

[0031] For example, each integer within the range of values ​​corresponding to the second probability distribution corresponds to a value in the quantized probability group corresponding to the second probability distribution.

[0032] According to the first aspect, or any implementation of the first aspect above, the H values ​​are H quantized probability numerators.

[0033] According to the first aspect, or any of the above implementations of the first aspect, the denominator of the quantization probability numerator is an integer power of 2.

[0034] First, quantizing the probability to the power of 2 makes it easier to generate preset information and improves the efficiency of entropy coding.

[0035] Secondly, the quantized probability group includes the quantized probability numerator but does not include the quantized probability denominator, which facilitates storage and can further reduce the memory occupied by the preset information.

[0036] According to the first aspect, or any implementation of the first aspect above, the quantization probability denominator is 256, and the quantization probability of the label value corresponding to each of the N probability distributions is 1 / 256.

[0037] Since the sum of the quantization probability numerators of all quantization probabilities in each quantization probability group and the quantization probability numerator of the label value equals the quantization probability denominator, the quantization probability denominator is 256. This can reduce the complexity of establishing preset information to some extent and improve the efficiency of entropy coding. In addition, it can ensure that the number of quantization probability numerators in each quantization probability group is not too large, thereby ensuring that the memory occupied by the preset information is not too large.

[0038] For example, the tag value can be set as needed, such as the negative of the critical value corresponding to the probability distribution (e.g., -bound_table_r[i]); this application does not limit this.

[0039] According to the first aspect, or any implementation of the first aspect above, the first data includes multiple symbols; entropy encoding is performed on the first data based on the critical value corresponding to the first data and the quantization probability group corresponding to the first data, including: determining whether the absolute value of the symbol to be entropy encoded in the first data is less than the critical value corresponding to the symbol to be entropy encoded; when the absolute value of the symbol to be entropy encoded is greater than or equal to the critical value corresponding to the symbol to be entropy encoded, the first entropy encoding algorithm is used to entropy encode the symbol to be entropy encoded; the symbol to be entropy encoded is updated to a marker value; the second entropy encoding algorithm is used to entropy encode the updated symbol to be entropy encoded based on the quantization probability group corresponding to the symbol to be entropy encoded; when the absolute value of the symbol to be entropy encoded is less than the critical value corresponding to the symbol to be entropy encoded, the second entropy encoding algorithm is used to entropy encode the symbol to be entropy encoded based on the quantization probability group corresponding to the symbol to be entropy encoded. This method can achieve entropy encoding for symbols with low probability (i.e., long-tail symbols), thereby effectively shortening the length of the bitstream obtained by entropy encoding.

[0040] For example, the first entropy coding algorithm can be the OUTBOUND algorithm, and the second entropy coding algorithm can be the INBOUND algorithm; it should be understood that this application does not limit the first entropy coding algorithm and the second entropy coding algorithm.

[0041] According to the first aspect, or any of the above implementations of the first aspect, some of the critical values ​​among the N critical values ​​are: 2, 2, 2, 2, 2, 3, 3.

[0042] Based on the first aspect, or any of the implementations of the first aspect above.

[0043] The first quantized probability group includes: 251, 2, 2;

[0044] The second quantized probability group includes: 245, 5, 5;

[0045] The third quantized probability group includes: 231, 12, 12;

[0046] The fourth quantization probability group includes: 211, 22, 22;

[0047] The fifth quantized probability group includes: 188, 33, 34;

[0048] The sixth quantized probability group includes: 157, 48, 48, 1, 1;

[0049] The seventh quantized probability group includes: 134, 57, 56, 4, 4.

[0050] It should be noted that the first quantized probability group, the second quantized probability group, the third quantized probability group, etc., are only used to distinguish between multiple quantized probability groups, and do not represent the sorting order of multiple quantized probability groups.

[0051] According to the first aspect, or any implementation of the first aspect above, when N = 32 and n = N, the N critical values ​​are: 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 4, 4, 5, 6, 7, 8, 9, 11, 13, 16, 18, 22, 26, 32, 38, 46, 55, 65, 77, 92, 106, 128.

[0052] It should be noted that the N critical values ​​can be arranged in other orders, and this application does not restrict the order of the N critical values.

[0053] According to the first aspect, or any implementation of the first aspect above, when N = 32 and n = N,

[0054] The first quantized probability group includes: 255;

[0055] The second quantized probability group includes: 255;

[0056] The third quantized probability group includes: 255;

[0057] The fourth quantization probability group includes: 251, 2, 2;

[0058] The fifth quantized probability group includes: 245, 5, 5;

[0059] The sixth quantization probability group includes: 231, 12, 12;

[0060] The seventh quantization probability group includes: 211, 22, 22;

[0061] The eighth quantization probability group includes: 188, 33, 34;

[0062] The ninth quantization probability group includes: 157, 48, 48, 1, 1;

[0063] The tenth quantized probability group includes: 134, 57, 56, 4, 4;

[0064] The eleventh quantized probability group includes: 111, 61, 61, 10, 10, 1, 1;

[0065] The twelfth quantized probability group includes: 94, 62, 61, 17, 17, 2, 2;

[0066] The thirteenth quantization probability group includes: 77, 58, 58, 24, 24, 6, 6, 1, 1;

[0067] The fourteenth quantized probability group includes: 63, 52, 52, 29, 29, 11, 11, 3, 3, 1, 1;

[0068] The fifteenth quantized probability group includes: 52, 46, 46, 31, 30, 16, 16, 6, 6, 2, 2, 1, 1;

[0069] The sixteenth quantized probability group includes: 43, 39, 39, 30, 30, 19, 19, 10, 10, 5, 5, 2, 2, 1, 1;

[0070] The seventeenth quantized probability group includes: 35, 33, 33, 28, 28, 21, 21, 13, 13, 8, 8, 4, 4, 2, 2, 1, 1;

[0071] The eighteenth quantized probability group includes: 29, 28, 28, 25, 25, 20, 20, 15, 15, 10, 10, 7, 7, 4, 4, 2, 2, 1, 1, 1, 1;

[0072] The nineteenth quantized probability group includes: 24, 23, 23, 21, 21, 19, 18, 15, 15, 12, 12, 9, 9, 6, 6, 4, 4, 3, 3, 2, 2, 1, 1, 1, 1;

[0073] The twentieth quantified probability group includes: 19, 19, 19, 18, 18, 17, 17, 15, 15, 12, 12, 10, 10, 8, 8, 6, 6, 4, 4, 3, 3, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1;

[0074] The twenty-first quantized probability group includes: 17, 16, 16, 16, 16, 15, 15, 13, 13, 12, 12, 10, 10, 9, 9, 7, 7, 6, 6, 4, 4, 3, 3, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1;

[0075] The twenty-second quantized probability group includes: 14, 13, 13, 13, 13, 12, 12, 12, 12, 11, 11, 10, 10, 9, 9, 8, 8, 6, 6, 5, 5, 4, 5, 4, 4, 3, 3, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1;

[0076] The twenty-third quantized probability group includes: 11, 11, 11, 11, 11, 10, 10, 10, 10, 10, 10, 9, 9, 8, 8, 7, 7, 7, 7, 6, 6, 5, 5, 5, 5, 4, 4, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1;

[0077] The twenty-fourth quantized probability group includes: 9, 9, 9, 9, 9, 9, 9, 8, 8, 8, 8, 8, 8, 7, 7, 7, 7, 6, 6, 6, 6, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1;

[0078] The twenty-fifth quantized probability group includes: 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 6, 6, 6, 6, 6, 6, 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1;

[0079] The twenty-sixth quantified probability group includes: 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, 6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1 ...

[0080] The twenty-seventh quantized probability group includes: 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1 ...

[0081] The twenty-eighth quantized probability group includes: 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2 ... ,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1;

[0082] The twenty-ninth quantified probability group includes: 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2 ... ,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1;

[0083] The thirtieth quantized probability group includes: 2, ...1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ,1 ...

[0084] The thirty-first quantized probability group includes: 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1 ... ,1 ...

[0085] The thirty-second quantized probability group includes: 1, ... 1, ... ,1 ... ,1 ...

[0086] It should be understood that the N quantized probability groups can be arranged in other orders, and this application does not restrict the sorting order of the N quantized probability groups.

[0087] Furthermore, this application does not restrict the order of the quantized probability molecules in each quantized probability group.

[0088] For example, the position of the i-th critical value corresponding to the i-th probability distribution among the N critical values ​​is the same as the position of the i-th quantized probability group corresponding to the i-th probability distribution among the N quantized probability groups.

[0089] Secondly, embodiments of this application provide an entropy decoding method, which includes: first, receiving a bitstream; then, acquiring the probability distribution index and preset information of second data in the bitstream; wherein, the preset information includes N critical values ​​and N quantization probability groups corresponding to N probability distributions; each of the N probability distributions corresponds to a critical value and a quantization probability group; the relative entropy of the n probability distributions is the optimal relative entropy; wherein, the relative entropy of the first probability distribution is used to characterize the distance between the reference probability distribution corresponding to the first probability distribution and the first probability distribution, the first probability distribution being any one of the n probability distributions; N is a positive integer, n is an integer between 1 and N, and n is greater than a preset value; each of the N probability distributions corresponds to a probability distribution index; subsequently, according to the probability distribution index of the second data, searching for the critical value and quantization probability group corresponding to the second data from the preset information; and then, performing entropy decoding on the second data according to the critical value and quantization probability group corresponding to the second data.

[0090] According to the second aspect, the preset information is calculated based on the type of reference probability distribution with the goal of minimizing relative entropy.

[0091] According to the second aspect, or any implementation of the second aspect above, the reference probability distribution type is a Gaussian probability distribution type.

[0092] According to the second aspect, or any implementation of the second aspect above, n = N.

[0093] According to the second aspect, or any implementation of the second aspect above, N is 32.

[0094] According to the second aspect, or any implementation of the second aspect above, the quantized probability set corresponding to the second probability distribution includes H values, where H is determined based on the critical value corresponding to the second probability distribution, H is a positive integer, and the second probability distribution is any one of the N probability distributions.

[0095] According to the second aspect, or any implementation of the second aspect above, the range of values ​​corresponding to the second probability distribution is [-(bound_table_r[i]-1), (bound_table_r[i]-1)], where H is the sum of the number of integers in the range, and the step size of the integers in the range is M, where M is a positive integer;

[0096] Where bound_table_r[i] is the critical value corresponding to the second probability distribution, i is the probability distribution index of the second probability distribution, and i is a positive integer.

[0097] According to the second aspect, or any implementation of the second aspect above, the H values ​​are H quantized probability numerators.

[0098] According to the second aspect, or any implementation of the second aspect above, the denominator of the quantization probability numerator is an integer power of 2.

[0099] According to the second aspect, or any implementation of the second aspect above, the quantization probability denominator is 256, and the quantization probability of the label value corresponding to each of the N probability distributions is 1 / 256.

[0100] According to the second aspect, or any implementation of the second aspect above, the second data is entropy decoded based on the critical value and quantization probability group corresponding to the second data, including: using a second entropy decoding algorithm to entropy decode the symbol to be entropy decoded based on the quantization probability group corresponding to the symbol to be entropy decoded in the second data, to obtain entropy-decoded data; determining whether the entropy-decoded data is a marker value; when the entropy-decoded data is a marker value, using a first entropy decoding algorithm to entropy decode the entropy-decoded data, and updating the entropy-decoded data to the result of entropy decoding using the first entropy decoding algorithm.

[0101] According to the second aspect, or any implementation of the second aspect above, some of the critical values ​​among the N critical values ​​are: 2, 2, 2, 2, 2, 3, 3.

[0102] Based on the second aspect, or any implementation of the second aspect above.

[0103] The first quantized probability group includes: 251, 2, 2;

[0104] The second quantized probability group includes: 245, 5, 5;

[0105] The third quantized probability group includes: 231, 12, 12;

[0106] The fourth quantization probability group includes: 211, 22, 22;

[0107] The fifth quantized probability group includes: 188, 33, 34;

[0108] The sixth quantized probability group includes: 157, 48, 48, 1, 1;

[0109] The seventh quantized probability group includes: 134, 57, 56, 4, 4.

[0110] According to the second aspect, or any implementation of the second aspect above, when N = 32 and n = N, the other critical values ​​among the N critical values ​​are: 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 4, 4, 5, 6, 7, 8, 9, 11, 13, 16, 18, 22, 26, 32, 38, 46, 55, 65, 77, 92, 106, 128.

[0111] According to the second aspect, or any implementation of the second aspect above, when N = 32 and n = N,

[0112] The first quantized probability group includes: 255;

[0113] The second quantized probability group includes: 255;

[0114] The third quantized probability group includes: 255;

[0115] The fourth quantization probability group includes: 251, 2, 2;

[0116] The fifth quantized probability group includes: 245, 5, 5;

[0117] The sixth quantization probability group includes: 231, 12, 12;

[0118] The seventh quantization probability group includes: 211, 22, 22;

[0119] The eighth quantization probability group includes: 188, 33, 34;

[0120] The ninth quantization probability group includes: 157, 48, 48, 1, 1;

[0121] The tenth quantized probability group includes: 134, 57, 56, 4, 4;

[0122] The eleventh quantized probability group includes: 111, 61, 61, 10, 10, 1, 1;

[0123] The twelfth quantized probability group includes: 94, 62, 61, 17, 17, 2, 2;

[0124] The thirteenth quantization probability group includes: 77, 58, 58, 24, 24, 6, 6, 1, 1;

[0125] The fourteenth quantized probability group includes: 63, 52, 52, 29, 29, 11, 11, 3, 3, 1, 1;

[0126] The fifteenth quantized probability group includes: 52, 46, 46, 31, 30, 16, 16, 6, 6, 2, 2, 1, 1;

[0127] The sixteenth quantized probability group includes: 43, 39, 39, 30, 30, 19, 19, 10, 10, 5, 5, 2, 2, 1, 1;

[0128] The seventeenth quantized probability group includes: 35, 33, 33, 28, 28, 21, 21, 13, 13, 8, 8, 4, 4, 2, 2, 1, 1;

[0129] The eighteenth quantized probability group includes: 29, 28, 28, 25, 25, 20, 20, 15, 15, 10, 10, 7, 7, 4, 4, 2, 2, 1, 1, 1, 1;

[0130] The nineteenth quantized probability group includes: 24, 23, 23, 21, 21, 19, 18, 15, 15, 12, 12, 9, 9, 6, 6, 4, 4, 3, 3, 2, 2, 1, 1, 1, 1;

[0131] The twentieth quantified probability group includes: 19, 19, 19, 18, 18, 17, 17, 15, 15, 12, 12, 10, 10, 8, 8, 6, 6, 4, 4, 3, 3, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1;

[0132] The twenty-first quantized probability group includes: 17, 16, 16, 16, 16, 15, 15, 13, 13, 12, 12, 10, 10, 9, 9, 7, 7, 6, 6, 4, 4, 3, 3, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1;

[0133] The twenty-second quantized probability group includes: 14, 13, 13, 13, 13, 12, 12, 12, 12, 11, 11, 10, 10, 9, 9, 8, 8, 6, 6, 5, 5, 4, 5, 4, 4, 3, 3, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1;

[0134] The twenty-third quantized probability group includes: 11, 11, 11, 11, 11, 10, 10, 10, 10, 10, 10, 9, 9, 8, 8, 7, 7, 7, 7, 6, 6, 5, 5, 5, 5, 4, 4, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1;

[0135] The twenty-fourth quantized probability group includes: 9, 9, 9, 9, 9, 9, 9, 8, 8, 8, 8, 8, 8, 7, 7, 7, 7, 6, 6, 6, 6, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1;

[0136] The twenty-fifth quantized probability group includes: 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 6, 6, 6, 6, 6, 6, 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1;

[0137] The twenty-sixth quantified probability group includes: 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, 6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1 ...

[0138] The twenty-seventh quantized probability group includes: 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1 ...

[0139] The twenty-eighth quantized probability group includes: 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2 ... ,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1;

[0140] The twenty-ninth quantified probability group includes: 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2 ... ,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1;

[0141] The thirtieth quantized probability group includes: 2, ...1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ,1 ...

[0142] The thirty-first quantized probability group includes: 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1 ... ,1 ...

[0143] The thirty-second quantized probability group includes: 1, ... 1, ... ,1 ... ,1 ...

[0144] The second aspect and any implementation thereof correspond to the first aspect and any implementation thereof, respectively. The technical effects of the second aspect and any implementation thereof are similar to those of the first aspect and any implementation thereof, and will not be repeated here.

[0145] Thirdly, embodiments of this application provide an entropy encoding method, which includes: firstly, acquiring first data, a probability distribution index of the first data, and preset information; wherein, the preset information includes N critical values ​​and N quantized probability groups corresponding to N probability distributions, each of the N probability distributions corresponding to a critical value and a quantized probability group, and N being a positive integer; each of the N probability distributions corresponding to a probability distribution index; nextly, searching for the critical value and quantized probability group corresponding to the first data from the preset information according to the probability distribution index of the first data; and then, performing entropy encoding on the first data according to the critical value and quantized probability group corresponding to the first data; wherein,

[0146] The first critical value is: 2;

[0147] The second critical value is: 2;

[0148] The third critical value is: 2;

[0149] The fourth critical value is 2;

[0150] The fifth critical value is: 2;

[0151] The sixth critical value is 3;

[0152] The seventh critical value is 3;

[0153] The first quantized probability group includes: 251, 2, 2;

[0154] The second quantized probability group includes: 245, 5, 5;

[0155] The third quantized probability group includes: 231, 12, 12;

[0156] The fourth quantization probability group includes: 211, 22, 22;

[0157] The fifth quantized probability group includes: 188, 33, 34;

[0158] The sixth quantized probability group includes: 157, 48, 48, 1, 1;

[0159] The seventh quantized probability group includes: 134, 57, 56, 4, 4.

[0160] The relative entropy of the multiple probability distributions corresponding to the multiple critical values ​​and multiple quantization probability groups shown above is the optimal relative entropy; in this way, the sum of the relative entropies of the N probability distributions in the preset information used for entropy coding can be reduced; since entropy coding is performed based on the N probability distributions with smaller sum of relative entropy, the length of the resulting bitstream is smaller; therefore, the entropy coding method of this application can shorten the length of the bitstream obtained by entropy coding and reduce the bit rate overhead.

[0161] It should be understood that the first critical value, the second critical value, the third critical value, etc., are only used to distinguish multiple critical values, and do not represent the order of the multiple critical values.

[0162] According to the third aspect, when N = 32,

[0163] The eighth critical value is: 1;

[0164] The ninth critical value is: 1;

[0165] The tenth critical value is: 1;

[0166] The eleventh critical value is: 4;

[0167] The twelfth critical value is: 4;

[0168] The thirteenth critical value is: 5;

[0169] The fourteenth critical value is: 6;

[0170] The fifteenth critical value is: 7;

[0171] The sixteenth critical value is: 8;

[0172] The seventeenth critical value is: 9;

[0173] The eighteenth critical value is: 11;

[0174] The nineteenth critical value is: 13;

[0175] The twentieth critical value is: 16;

[0176] The twenty-first critical value is: 18;

[0177] The twenty-second critical value is: 22;

[0178] The twenty-third critical value is: 26;

[0179] The twenty-fourth critical value is: 32;

[0180] The twenty-fifth critical value is: 38;

[0181] The twenty-sixth critical value is: 46;

[0182] The twenty-seventh critical value is: 55;

[0183] The twenty-eighth critical value is: 65;

[0184] The twenty-ninth critical value is: 77;

[0185] The 30th critical value is: 92;

[0186] The thirty-first critical value is: 106

[0187] The thirty-second critical value is 128.

[0188] According to the third aspect, or any implementation of the third aspect above, when N = 32,

[0189] The eighth quantized probability group includes: 255;

[0190] The ninth quantized probability group includes: 255;

[0191] The tenth quantized probability group includes: 255;

[0192] The eleventh quantized probability group includes: 111, 61, 61, 10, 10, 1, 1;

[0193] The twelfth quantized probability group includes: 94, 62, 61, 17, 17, 2, 2;

[0194] The thirteenth quantization probability group includes: 77, 58, 58, 24, 24, 6, 6, 1, 1;

[0195] The fourteenth quantized probability group includes: 63, 52, 52, 29, 29, 11, 11, 3, 3, 1, 1;

[0196] The fifteenth quantized probability group includes: 52, 46, 46, 31, 30, 16, 16, 6, 6, 2, 2, 1, 1;

[0197] The sixteenth quantized probability group includes: 43, 39, 39, 30, 30, 19, 19, 10, 10, 5, 5, 2, 2, 1, 1;

[0198] The seventeenth quantized probability group includes: 35, 33, 33, 28, 28, 21, 21, 13, 13, 8, 8, 4, 4, 2, 2, 1, 1;

[0199] The eighteenth quantized probability group includes: 29, 28, 28, 25, 25, 20, 20, 15, 15, 10, 10, 7, 7, 4, 4, 2, 2, 1, 1, 1, 1;

[0200] The nineteenth quantized probability group includes: 24, 23, 23, 21, 21, 19, 18, 15, 15, 12, 12, 9, 9, 6, 6, 4, 4, 3, 3, 2, 2, 1, 1, 1, 1;

[0201] The twentieth quantified probability group includes: 19, 19, 19, 18, 18, 17, 17, 15, 15, 12, 12, 10, 10, 8, 8, 6, 6, 4, 4, 3, 3, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1;

[0202] The twenty-first quantized probability group includes: 17, 16, 16, 16, 16, 15, 15, 13, 13, 12, 12, 10, 10, 9, 9, 7, 7, 6, 6, 4, 4, 3, 3, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1;

[0203] The twenty-second quantized probability group includes: 14, 13, 13, 13, 13, 12, 12, 12, 12, 11, 11, 10, 10, 9, 9, 8, 8, 6, 6, 5, 5, 4, 5, 4, 4, 3, 3, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1;

[0204] The twenty-third quantized probability group includes: 11, 11, 11, 11, 11, 10, 10, 10, 10, 10, 10, 9, 9, 8, 8, 7, 7, 7, 7, 6, 6, 5, 5, 5, 5, 4, 4, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1;

[0205] The twenty-fourth quantized probability group includes: 9, 9, 9, 9, 9, 9, 9, 8, 8, 8, 8, 8, 8, 7, 7, 7, 7, 6, 6, 6, 6, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1;

[0206] The twenty-fifth quantized probability group includes: 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 6, 6, 6, 6, 6, 6, 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1;

[0207] The twenty-sixth quantified probability group includes: 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, 6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1 ...

[0208] The twenty-seventh quantized probability group includes: 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1 ...

[0209] The twenty-eighth quantized probability group includes: 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2 ... ,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1;

[0210] The twenty-ninth quantified probability group includes: 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2 ... ,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1;

[0211] The thirtieth quantized probability group includes: 2, ...1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ,1 ...

[0212] The thirty-first quantized probability group includes: 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1 ... ,1 ...

[0213] The thirty-second quantized probability group includes: 1, ... 1, ... ,1 ... ,1 ...

[0214] Fourthly, embodiments of this application provide an entropy decoding method, which includes: first, receiving a bitstream; then, acquiring a probability distribution index and preset information of second data in the bitstream; wherein, the preset information includes N critical values ​​and N quantization probability groups corresponding to N probability distributions, each of the N probability distributions corresponding to a critical value and a quantization probability group, and N being a positive integer; each of the N probability distributions corresponding to a probability distribution index; subsequently, searching for the critical value and quantization probability group corresponding to the second data from the preset information according to the probability distribution index of the second data; and then, performing entropy decoding on the second data according to the critical value and quantization probability group corresponding to the second data; wherein,

[0215] The first critical value is: 2;

[0216] The second critical value is: 2;

[0217] The third critical value is: 2;

[0218] The fourth critical value is 2;

[0219] The fifth critical value is: 2;

[0220] The sixth critical value is 3;

[0221] The seventh critical value is 3;

[0222] The first quantized probability group includes: 251, 2, 2;

[0223] The second quantized probability group includes: 245, 5, 5;

[0224] The third quantized probability group includes: 231, 12, 12;

[0225] The fourth quantization probability group includes: 211, 22, 22;

[0226] The fifth quantized probability group includes: 188, 33, 34;

[0227] The sixth quantized probability group includes: 157, 48, 48, 1, 1;

[0228] The seventh quantized probability group includes: 134, 57, 56, 4, 4.

[0229] According to the fourth aspect, when N = 32,

[0230] The eighth critical value is: 1;

[0231] The ninth critical value is: 1;

[0232] The tenth critical value is: 1;

[0233] The eleventh critical value is: 4;

[0234] The twelfth critical value is: 4;

[0235] The thirteenth critical value is: 5;

[0236] The fourteenth critical value is: 6;

[0237] The fifteenth critical value is: 7;

[0238] The sixteenth critical value is: 8;

[0239] The seventeenth critical value is: 9;

[0240] The eighteenth critical value is: 11;

[0241] The nineteenth critical value is: 13;

[0242] The twentieth critical value is: 16;

[0243] The twenty-first critical value is: 18;

[0244] The twenty-second critical value is: 22;

[0245] The twenty-third critical value is: 26;

[0246] The twenty-fourth critical value is: 32;

[0247] The twenty-fifth critical value is: 38;

[0248] The twenty-sixth critical value is: 46;

[0249] The twenty-seventh critical value is: 55;

[0250] The twenty-eighth critical value is: 65;

[0251] The twenty-ninth critical value is: 77;

[0252] The 30th critical value is: 92;

[0253] The thirty-first critical value is: 106

[0254] The thirty-second critical value is 128.

[0255] According to the fourth aspect, or any implementation of the fourth aspect above, when N = 32,

[0256] The eighth quantized probability group includes: 255;

[0257] The ninth quantized probability group includes: 255;

[0258] The tenth quantized probability group includes: 255;

[0259] The eleventh quantized probability group includes: 111, 61, 61, 10, 10, 1, 1;

[0260] The twelfth quantized probability group includes: 94, 62, 61, 17, 17, 2, 2;

[0261] The thirteenth quantization probability group includes: 77, 58, 58, 24, 24, 6, 6, 1, 1;

[0262] The fourteenth quantized probability group includes: 63, 52, 52, 29, 29, 11, 11, 3, 3, 1, 1;

[0263] The fifteenth quantized probability group includes: 52, 46, 46, 31, 30, 16, 16, 6, 6, 2, 2, 1, 1;

[0264] The sixteenth quantized probability group includes: 43, 39, 39, 30, 30, 19, 19, 10, 10, 5, 5, 2, 2, 1, 1;

[0265] The seventeenth quantized probability group includes: 35, 33, 33, 28, 28, 21, 21, 13, 13, 8, 8, 4, 4, 2, 2, 1, 1;

[0266] The eighteenth quantized probability group includes: 29, 28, 28, 25, 25, 20, 20, 15, 15, 10, 10, 7, 7, 4, 4, 2, 2, 1, 1, 1, 1;

[0267] The nineteenth quantized probability group includes: 24, 23, 23, 21, 21, 19, 18, 15, 15, 12, 12, 9, 9, 6, 6, 4, 4, 3, 3, 2, 2, 1, 1, 1, 1;

[0268] The twentieth quantified probability group includes: 19, 19, 19, 18, 18, 17, 17, 15, 15, 12, 12, 10, 10, 8, 8, 6, 6, 4, 4, 3, 3, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1;

[0269] The twenty-first quantized probability group includes: 17, 16, 16, 16, 16, 15, 15, 13, 13, 12, 12, 10, 10, 9, 9, 7, 7, 6, 6, 4, 4, 3, 3, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1;

[0270] The twenty-second quantized probability group includes: 14, 13, 13, 13, 13, 12, 12, 12, 12, 11, 11, 10, 10, 9, 9, 8, 8, 6, 6, 5, 5, 4, 5, 4, 4, 3, 3, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1;

[0271] The twenty-third quantized probability group includes: 11, 11, 11, 11, 11, 10, 10, 10, 10, 10, 10, 9, 9, 8, 8, 7, 7, 7, 7, 6, 6, 5, 5, 5, 5, 4, 4, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1;

[0272] The twenty-fourth quantized probability group includes: 9, 9, 9, 9, 9, 9, 9, 8, 8, 8, 8, 8, 8, 7, 7, 7, 7, 6, 6, 6, 6, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1;

[0273] The twenty-fifth quantized probability group includes: 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 6, 6, 6, 6, 6, 6, 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1;

[0274] The twenty-sixth quantified probability group includes: 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, 6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1 ...

[0275] The twenty-seventh quantized probability group includes: 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1 ...

[0276] The twenty-eighth quantized probability group includes: 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2 ... ,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1;

[0277] The twenty-ninth quantified probability group includes: 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2 ... ,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1;

[0278] The thirtieth quantized probability group includes: 2, ...1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ,1 ...

[0279] The thirty-first quantized probability group includes: 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1 ... ,1 ...

[0280] The thirty-second quantized probability group includes: 1, ... 1, ... ,1 ... ,1 ...

[0281] Fifthly, embodiments of this application provide an apparatus for entropy coding, the apparatus comprising:

[0282] The first information acquisition module is used to acquire first data, the probability distribution index of the first data, and preset information. The preset information includes N critical values ​​and N quantized probability groups corresponding to N probability distributions. Each of the N probability distributions corresponds to a critical value and a quantized probability group. The relative entropy of the n probability distributions is the optimal relative entropy. The relative entropy of the first probability distribution is used to characterize the distance between the reference probability distribution corresponding to the first probability distribution and the first probability distribution itself. The first probability distribution is any one of the n probability distributions. N is a positive integer, and n is an integer between 1 and N, where n is greater than a preset value. Each of the N probability distributions corresponds to a probability distribution index.

[0283] The first information lookup module is used to look up the critical value and quantized probability group corresponding to the first data from preset information based on the probability distribution index of the first data.

[0284] The first entropy encoding module is used to entropy encode the first data based on the critical value and quantization probability group corresponding to the first data.

[0285] For example, the above-described apparatus for entropy coding can be used to perform the entropy coding method in the first aspect or any possible implementation of the first aspect.

[0286] The fifth aspect and any implementation thereof correspond to the first aspect and any implementation thereof, respectively. The technical effects of the fifth aspect and any implementation thereof are similar to those of the first aspect and any implementation thereof, and will not be repeated here.

[0287] Sixthly, embodiments of this application provide an apparatus for entropy decoding, the apparatus comprising:

[0288] The first stream receiving module is used to receive the stream.

[0289] The second information acquisition module is used to acquire the probability distribution index and preset information of the second data in the bitstream. The preset information includes N critical values ​​and N quantization probability groups corresponding to N probability distributions. Each of the N probability distributions corresponds to a critical value and a quantization probability group. The relative entropy of the n probability distributions is the optimal relative entropy. The relative entropy of the first probability distribution is used to characterize the distance between the reference probability distribution corresponding to the first probability distribution and the first probability distribution itself. The first probability distribution is any one of the n probability distributions. N is a positive integer, and n is an integer between 1 and N, where n is greater than a preset value. Each of the N probability distributions corresponds to a probability distribution index.

[0290] The second information lookup module is used to look up the critical value and quantized probability group corresponding to the second data from preset information based on the probability distribution index of the second data.

[0291] The first entropy decoding module is used to perform entropy decoding on the second data based on the critical value and quantization probability group corresponding to the second data.

[0292] For example, the above-described apparatus for entropy decoding can be used to perform the entropy decoding method in the second aspect or any possible implementation of the second aspect.

[0293] The sixth aspect and any implementation thereof correspond to the second aspect and any implementation thereof, respectively. The technical effects of the sixth aspect and any implementation thereof are similar to those of the second aspect and any implementation thereof, and will not be repeated here.

[0294] In a seventh aspect, embodiments of this application provide an apparatus for entropy coding, the apparatus comprising:

[0295] The third information acquisition module is used to acquire first data, the probability distribution index of the first data, and preset information; wherein, the preset information includes N critical values ​​and N quantized probability groups corresponding to N probability distributions, each of the N probability distributions corresponds to a critical value and a quantized probability group, and N is a positive integer; each of the N probability distributions corresponds to a probability distribution index;

[0296] The third information search module is used to search for the critical value and quantized probability group corresponding to the first data from preset information based on the probability distribution index of the first data.

[0297] The second entropy encoding module is used to entropy encode the first data according to the critical value and quantization probability group corresponding to the first data;

[0298] The first critical value is 2.

[0299] The second critical value is: 2;

[0300] The third critical value is: 2;

[0301] The fourth critical value is 2;

[0302] The fifth critical value is: 2;

[0303] The sixth critical value is 3;

[0304] The seventh critical value is 3;

[0305] The first quantized probability group includes: 251, 2, 2;

[0306] The second quantized probability group includes: 245, 5, 5;

[0307] The third quantized probability group includes: 231, 12, 12;

[0308] The fourth quantization probability group includes: 211, 22, 22;

[0309] The fifth quantized probability group includes: 188, 33, 34;

[0310] The sixth quantized probability group includes: 157, 48, 48, 1, 1;

[0311] The seventh quantized probability group includes: 134, 57, 56, 4, 4.

[0312] For example, the above-described apparatus for entropy coding can be used to perform the entropy coding method in the third aspect or any possible implementation of the third aspect.

[0313] The seventh aspect and any implementation thereof correspond to the third aspect and any implementation thereof, respectively. The technical effects of the seventh aspect and any implementation thereof can be found in the technical effects of the third aspect and any implementation thereof, as described above, and will not be repeated here.

[0314] Eighthly, embodiments of this application provide an apparatus for entropy decoding, the apparatus comprising:

[0315] The second stream receiving module is used to receive the stream.

[0316] The fourth information acquisition module is used to acquire the probability distribution index and preset information of the second data in the bitstream; wherein, the preset information includes N critical values ​​and N quantization probability groups corresponding to N probability distributions, each of the N probability distributions corresponds to a critical value and a quantization probability group, and N is a positive integer; each of the N probability distributions corresponds to a probability distribution index;

[0317] The fourth information search module is used to search for the critical value and quantized probability group corresponding to the second data from the preset information based on the probability distribution index of the second data.

[0318] The second entropy decoding module is used to perform entropy decoding on the second data based on the critical value and quantization probability group corresponding to the second data.

[0319] The first critical value is 2.

[0320] The second critical value is: 2;

[0321] The third critical value is: 2;

[0322] The fourth critical value is 2;

[0323] The fifth critical value is: 2;

[0324] The sixth critical value is 3;

[0325] The seventh critical value is 3;

[0326] The first quantized probability group includes: 251, 2, 2;

[0327] The second quantized probability group includes: 245, 5, 5;

[0328] The third quantized probability group includes: 231, 12, 12;

[0329] The fourth quantization probability group includes: 211, 22, 22;

[0330] The fifth quantized probability group includes: 188, 33, 34;

[0331] The sixth quantized probability group includes: 157, 48, 48, 1, 1;

[0332] The seventh quantized probability group includes: 134, 57, 56, 4, 4.

[0333] For example, the above-described apparatus for entropy decoding can be used to perform the entropy decoding method in the fourth aspect or any possible implementation of the fourth aspect.

[0334] The eighth aspect and any implementation thereof correspond to the fourth aspect and any implementation thereof, respectively. The technical effects of the eighth aspect and any implementation thereof can be found in the technical effects of the fourth aspect and any implementation thereof, as described above, and will not be repeated here.

[0335] Ninthly, embodiments of this application provide an entropy encoder that can be used to execute the entropy encoding method in the first aspect and any implementation thereof.

[0336] The ninth aspect and any implementation thereof correspond to the first aspect and any implementation thereof, respectively. The technical effects corresponding to the ninth aspect and any implementation thereof are similar to those corresponding to the first aspect and any implementation thereof, and will not be repeated here.

[0337] In a tenth aspect, embodiments of this application provide an entropy decoder that can be used to execute the entropy decoding method in the second aspect and any implementation thereof.

[0338] The tenth aspect and any implementation thereof correspond to the second aspect and any implementation thereof, respectively. The technical effects corresponding to the tenth aspect and any implementation thereof are similar to those corresponding to the second aspect and any implementation thereof, and will not be repeated here.

[0339] Eleventhly, embodiments of this application provide an entropy encoder that can be used to execute the entropy encoding method in the third aspect and any implementation thereof.

[0340] The eleventh aspect and any implementation thereof correspond to the third aspect and any implementation thereof, respectively. The technical effects corresponding to the eleventh aspect and any implementation thereof can be found in the technical effects corresponding to the third aspect and any implementation thereof, and will not be repeated here.

[0341] In a twelfth aspect, embodiments of this application provide an entropy decoder that can be used to execute the entropy decoding method in the fourth aspect and any implementation thereof.

[0342] The twelfth aspect and any implementation thereof correspond to the fourth aspect and any implementation thereof, respectively. The technical effects corresponding to the twelfth aspect and any implementation thereof are similar to those corresponding to the fourth aspect and any implementation thereof, and will not be repeated here.

[0343] In a thirteenth aspect, embodiments of this application provide an encoder that includes an entropy encoder as described in the ninth aspect.

[0344] The thirteenth aspect and any implementation thereof correspond to the first aspect and any implementation thereof, respectively. The technical effects corresponding to the thirteenth aspect and any implementation thereof are similar to the technical effects corresponding to the first aspect and any implementation thereof, and will not be repeated here.

[0345] It should be noted that the encoder in aspect thirteen can be a traditional encoder or an AI (Artificial Intelligence, AI) encoder.

[0346] In a fourteenth aspect, embodiments of this application provide a decoder, the encoder including the entropy decoder of the tenth aspect.

[0347] The fourteenth aspect and any implementation thereof correspond to the second aspect and any implementation thereof, respectively. The technical effects corresponding to the fourteenth aspect and any implementation thereof are similar to those corresponding to the second aspect and any implementation thereof, and will not be repeated here.

[0348] It should be noted that the encoder in the fourteenth aspect can be a traditional decoder or an AI decoder.

[0349] In the fifteenth aspect, embodiments of this application provide an encoder, which includes the entropy encoder of the eleventh aspect.

[0350] The fifteenth aspect and any implementation thereof correspond to the third aspect and any implementation thereof, respectively. The technical effects corresponding to the fifteenth aspect and any implementation thereof are similar to those corresponding to the third aspect and any implementation thereof, and will not be repeated here.

[0351] It should be noted that the encoder in aspect fifteen can be a traditional encoder or an AI encoder.

[0352] In a sixteenth aspect, embodiments of this application provide a decoder, the encoder including the entropy decoder of the twelfth aspect.

[0353] The sixteenth aspect and any implementation thereof correspond to the fourth aspect and any implementation thereof, respectively. The technical effects of the sixteenth aspect and any implementation thereof can be found in the technical effects of the fourth aspect and any implementation thereof, as described above, and will not be repeated here.

[0354] It should be noted that the encoder in the sixteenth aspect can be a traditional decoder or an AI decoder.

[0355] In a seventeenth aspect, embodiments of this application provide a decoder, including: a memory and a processor, the memory being coupled to the processor; the memory storing program instructions, which, when executed by the processor, cause an electronic device to perform the entropy coding method in the first aspect or any possible implementation thereof.

[0356] The seventeenth aspect and any implementation thereof correspond to the first aspect and any implementation thereof, respectively. The technical effects of the seventeenth aspect and any implementation thereof are similar to those of the first aspect and any implementation thereof, and will not be repeated here.

[0357] In an eighteenth aspect, embodiments of this application provide a decoder, including: a memory and a processor, the memory being coupled to the processor; the memory storing program instructions, which, when executed by the processor, cause an electronic device to perform the entropy decoding method in the second aspect or any possible implementation thereof.

[0358] The eighteenth aspect and any implementation thereof correspond to the second aspect and any implementation thereof, respectively. The technical effects corresponding to the eighteenth aspect and any implementation thereof are similar to those corresponding to the second aspect and any implementation thereof, and will not be repeated here.

[0359] In a nineteenth aspect, embodiments of this application provide a decoder, including: a memory and a processor, the memory being coupled to the processor; the memory storing program instructions, which, when executed by the processor, cause an electronic device to perform an entropy encoding method in the third aspect or any possible implementation thereof.

[0360] The nineteenth aspect and any implementation thereof correspond to the third aspect and any implementation thereof, respectively. The technical effects corresponding to the nineteenth aspect and any implementation thereof are similar to those corresponding to the third aspect and any implementation thereof, and will not be repeated here.

[0361] In a twentieth aspect, embodiments of this application provide a decoder, including: a memory and a processor, the memory being coupled to the processor; the memory storing program instructions, which, when executed by the processor, cause an electronic device to perform the entropy decoding method in the fourth aspect or any possible implementation of the fourth aspect.

[0362] The twentieth aspect and any implementation thereof correspond to the fourth aspect and any implementation thereof, respectively. The technical effects corresponding to the twentieth aspect and any implementation thereof can be found in the technical effects corresponding to the fourth aspect and any implementation thereof, as described above, and will not be repeated here.

[0363] In a twentieth aspect, embodiments of this application provide a computer-readable storage medium storing a computer program that, when run on a computer or processor, causes the computer or processor to execute the entropy encoding method in the first aspect or any possible implementation thereof, or causes the computer or processor to execute the entropy encoding method in the third aspect or any possible implementation thereof.

[0364] The twenty-first aspect and any implementation thereof correspond to the first aspect and any implementation thereof, or correspond to the third aspect and any implementation thereof. The technical effects corresponding to the twenty-first aspect and any implementation thereof can be found in the technical effects corresponding to the first aspect and any implementation thereof, or in the technical effects corresponding to the third aspect and any implementation thereof, and will not be repeated here.

[0365] In a twentieth aspect, embodiments of this application provide a computer-readable storage medium storing a computer program that, when run on a computer or processor, causes the computer or processor to execute the entropy decoding method in the second aspect or any possible implementation thereof, or to execute the entropy decoding method in the fourth aspect or any possible implementation thereof.

[0366] The twenty-second aspect and any implementation thereof correspond to the second aspect and any implementation thereof, or correspond to the fourth aspect and any implementation thereof. The technical effects corresponding to the twenty-second aspect and any implementation thereof can be found in the technical effects corresponding to the second aspect and any implementation thereof, or in the technical effects corresponding to the fourth aspect and any implementation thereof, and will not be repeated here.

[0367] In a twentieth aspect, embodiments of this application provide a computer program product, the computer program product including computer instructions, which, when executed by a computer or processor, cause the computer or processor to perform an entropy encoding method in the first aspect or any possible implementation thereof, or cause the computer or processor to perform an entropy encoding method in the third aspect or any possible implementation thereof.

[0368] The twenty-third aspect and any implementation thereof correspond to the first aspect and any implementation thereof, or correspond to the third aspect and any implementation thereof. The technical effects corresponding to the twenty-third aspect and any implementation thereof can be found in the technical effects corresponding to the first aspect and any implementation thereof, or in the technical effects corresponding to the third aspect and any implementation thereof, and will not be repeated here.

[0369] In a twentieth aspect, embodiments of this application provide a computer program product, the computer program product including computer instructions, which, when executed by a computer or processor, cause the computer or processor to perform the entropy decoding method in the second aspect or any possible implementation thereof, or to perform the entropy decoding method in the fourth aspect or any possible implementation thereof.

[0370] The twenty-fourth aspect and any implementation thereof correspond to the second aspect and any implementation thereof, or to the fourth aspect and any implementation thereof. The technical effects corresponding to the twenty-fourth aspect and any implementation thereof can be found in the technical effects corresponding to the second aspect and any implementation thereof, or in the technical effects corresponding to the fourth aspect and any implementation thereof, and will not be repeated here.

[0371] In a twentieth aspect, embodiments of this application provide a bitstream generation method that can generate a bitstream according to the first aspect and any implementation thereof; or, generate a bitstream according to the third aspect and any implementation thereof.

[0372] The twenty-fifth aspect and any implementation thereof correspond to the first aspect and any implementation thereof, or correspond to the third aspect and any implementation thereof. The technical effects corresponding to the twenty-fifth aspect and any implementation thereof can be found in the technical effects corresponding to the first aspect and any implementation thereof, or in the technical effects corresponding to the third aspect and any implementation thereof, and will not be repeated here.

[0373] In a twentieth aspect, embodiments of this application provide an apparatus for storing a bitstream, the apparatus comprising: a receiver and at least one storage medium, the receiver being used to receive the bitstream; the at least one storage medium being used to store the bitstream; the bitstream being generated according to the first aspect and any implementation thereof, or according to the third aspect and any implementation thereof.

[0374] The twenty-sixth aspect and any implementation thereof correspond to the first aspect and any implementation thereof, or correspond to the third aspect and any implementation thereof. The technical effects corresponding to the twenty-sixth aspect and any implementation thereof can be found in the technical effects corresponding to the first aspect and any implementation thereof, or in the technical effects corresponding to the third aspect and any implementation thereof, and will not be repeated here.

[0375] In a twentieth aspect, embodiments of this application provide an apparatus for transmitting a bitstream, the apparatus comprising: a transmitter and at least one storage medium, the at least one storage medium being used to store the bitstream, the bitstream being generated according to the first aspect and any implementation thereof, or according to the third aspect and any implementation thereof; the transmitter being used to acquire the bitstream from the storage medium and transmit the bitstream to an end-side device via the transmission medium.

[0376] The twenty-seventh aspect and any implementation thereof correspond to the first aspect and any implementation thereof, or correspond to the third aspect and any implementation thereof. The technical effects corresponding to the twenty-seventh aspect and any implementation thereof can be found in the technical effects corresponding to the first aspect and any implementation thereof, or in the technical effects corresponding to the third aspect and any implementation thereof, and will not be repeated here.

[0377] In a twentieth aspect, embodiments of this application provide a system for distributing bitstreams. The system includes: at least one storage medium for storing at least one bitstream, wherein the at least one bitstream is generated according to the first aspect and any implementation thereof, or according to the third aspect and any implementation thereof; and a streaming media device for obtaining a target bitstream from the at least one storage medium and sending the target bitstream to an end-side device, wherein the streaming media device includes a content server or a content distribution server.

[0378] The twenty-eighth aspect and any implementation thereof correspond to the first aspect and any implementation thereof, or correspond to the third aspect and any implementation thereof. The technical effects corresponding to the twenty-eighth aspect and any implementation thereof can be found in the technical effects corresponding to the first aspect and any implementation thereof, or in the technical effects corresponding to the third aspect and any implementation thereof, and will not be repeated here.

[0379] It should be noted that the entropy encoding method and entropy decoding method of this application can be implemented by software or by hardware, and this application does not impose any restrictions on this.

[0380] It should be noted that this application does not limit the algorithms used for entropy encoding and entropy decoding. For example, they can be Huffman coding, arithmetic coding (AC), asymmetric number systems (ANS), etc. Attached Figure Description

[0381] Figure 1A A frame for implementing an example audio / video / image decoding system according to embodiments of the present invention;

[0382] Figure 1B A block diagram illustrating another example of an audio / video / image decoding system for implementing embodiments of the present invention;

[0383] Figure 2 A schematic diagram of an encoder as an example is shown;

[0384] Figure 3 A schematic diagram of a decoder as an example is shown;

[0385] Figure 4 This is a schematic diagram of the encoder structure as an example.

[0386] Figure 5 This is a schematic diagram of the decoder structure as an example.

[0387] Figure 6 This is a schematic diagram of the encoder structure as an example.

[0388] Figure 7 This is a schematic diagram of the decoder structure as an example.

[0389] Figure 8 A flowchart illustrating the entropy encoding process as an example;

[0390] Figure 9 A flowchart illustrating the entropy decoding process as an example;

[0391] Figure 10 This is a schematic diagram of an apparatus for entropy coding, as exemplarily shown.

[0392] Figure 11 This is a schematic diagram of an apparatus for entropy decoding, as exemplarily shown.

[0393] Figure 12 This is a schematic diagram of an apparatus for entropy coding, as exemplarily shown.

[0394] Figure 13 This is a schematic diagram of an apparatus for entropy decoding, as exemplarily shown.

[0395] Figure 14 This is a schematic diagram of the structure of an exemplary device. Detailed Implementation

[0396] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0397] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0398] The terms "first" and "second," etc., used in the specification and claims of this application are used to distinguish different objects, not to describe a specific order of objects. For example, "first target object" and "second target object," etc., are used to distinguish different target objects, not to describe a specific order of target objects.

[0399] In the embodiments of this application, the words "exemplarily" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplarily" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of the words "exemplarily" or "for example" is intended to present the relevant concepts in a specific manner.

[0400] In the description of the embodiments in this application, unless otherwise stated, "multiple" means two or more. For example, multiple processing units means two or more processing units; multiple systems means two or more systems.

[0401] For example, the entropy encoding and decoding method of this application can be applied to entropy encoding and decoding of audio, video, and images. This application uses video entropy encoding and decoding as an example for illustration.

[0402] For example, the entropy encoding and decoding method of this application can be applied to traditional encoding and decoding scenarios or AI encoding and decoding scenarios, and this application does not limit it.

[0403] Video coding generally refers to the processing of image sequences that form a video or video sequence. In the field of video coding, the terms "picture," "frame," or "image" can be used synonymously. Video coding (or commonly referred to as encoding) comprises two parts: video encoding and video decoding. Video encoding is performed on the source side and typically involves processing (e.g., compressing) the raw video image to reduce the amount of data required to represent that video image (thus enabling more efficient storage and / or transmission). Video decoding is performed on the destination side and typically involves inverse processing relative to the encoder to reconstruct the video image. The "encoding" of the video image (or commonly referred to as an image) mentioned in the embodiments should be understood as the "encoding" or "decoding" of the video image or video sequence. The encoding and decoding parts are also collectively referred to as codec (encoding and decoding, CODEC).

[0404] In lossless video coding, the original video image can be reconstructed, meaning the reconstructed video image has the same quality as the original (assuming no transmission loss or other data loss during storage or transmission). In lossy video coding, further compression is performed through quantization to reduce the amount of data required to represent the video image, and the decoder cannot completely reconstruct the video image, meaning the quality of the reconstructed video image is lower or worse than the quality of the original video image.

[0405] In the following embodiment of the decoding system 10, the encoder 20 and decoder 30 are based on Figures 1A to 7 Describe it.

[0406] Figure 1A This is an exemplary block diagram of a decoding system 10 according to an embodiment of this application, such as a video decoding system 10 (or simply decoding system 10) that can utilize the technology of this application. The video encoder 20 (or simply encoder 20) and video decoder 30 (or simply decoder 30) in the video decoding system 10 represent devices, etc., that can be used to perform various technologies according to the various examples described in this application.

[0407] like Figure 1A As shown, the decoding system 10 includes a source device 12, which provides encoded image data 21, such as encoded images, to a destination device 14 for decoding the encoded image data 21.

[0408] The source device 12 includes an encoder 20, and optionally may include an image source 16, a preprocessor (or preprocessing unit) 18 such as an image preprocessor, and a communication interface (or communication unit) 22.

[0409] Image source 16 may include or may be any type of image capture device for capturing real-world images, and / or any type of image generation device, such as a computer graphics processor for generating computer animation images or any type of device for acquiring and / or providing real-world images, computer-generated images (e.g., screen content, virtual reality (VR) images, and / or any combination thereof (e.g., augmented reality (AR) images). Image source may be any type of memory or storage device storing any of the images described above.

[0410] To distinguish the processing performed by the preprocessor (or preprocessing unit) 18, the image (or image data) 17 may also be referred to as the raw image (or raw image data) 17.

[0411] The preprocessor 18 receives the raw image data 17 and preprocesses it to obtain a preprocessed image (or preprocessed image data) 19. For example, the preprocessing performed by the preprocessor 18 may include cropping, color format conversion (e.g., from RGB to YCbCr), color correction, or noise reduction. It is understood that the preprocessing unit 18 may be an optional component.

[0412] Video encoder (or encoder) 20 is used to receive preprocessed image data 19 and provide encoded image data 21 (hereinafter referred to as...) Figure 2 , Figure 4 and Figure 6 (and so on, for further description).

[0413] The communication interface 22 in the source device 12 can be used to: receive encoded image data 21 and send encoded image data 21 (or other arbitrarily processed version) to another device such as the destination device 14 or any other device via the communication channel 13 for storage or direct reconstruction.

[0414] The target device 14 includes a decoder 30, and optionally may include a communication interface (or communication unit) 28, a post-processor (or post-processing unit) 32 and a display device 34.

[0415] The communication interface 28 in the destination device 14 is used to receive encoded image data 21 (or other processed versions) directly from the source device 12 or from any other source device such as a storage device, for example, the storage device is an encoded image data storage device, and to provide the encoded image data 21 to the decoder 30.

[0416] Communication interfaces 22 and 28 can be used to send or receive encoded image data (or encoded data 21) through a direct communication link between source device 12 and destination device 14, such as a direct wired or wireless connection, or through any type of network, such as a wired network, a wireless network or any combination thereof, any type of private network and public network or any combination thereof.

[0417] For example, the communication interface 22 can be used to encapsulate the encoded image data 21 into a suitable format such as a message, and / or process the encoded image data using any type of transmission encoding or processing, so as to transmit it on a communication link or communication network.

[0418] Communication interface 28 corresponds to communication interface 22. For example, it can be used to receive transmitted data and process the transmitted data using any type of corresponding transmission decoding or processing and / or decapsulation to obtain encoded image data 21.

[0419] Both communication interface 22 and communication interface 28 can be configured as follows: Figure 1AThe arrow pointing from the source device 12 to the corresponding communication channel 13 of the destination device 14 indicates a one-way or two-way communication interface, which can be used to send and receive messages, establish connections, acknowledge and exchange any other information related to the communication link and / or data transmission, such as encoded image data transmission, etc.

[0420] Video decoder (or decoder) 30 is used to receive encoded image data 21 and provide decoded image data (or decoded image data) 31 (hereinafter referred to as...). Figure 3 , Figure 5 or Figure 7 (and so on, for further description).

[0421] The post-processor 32 is used to post-process the decoded image data 31 (also known as the reconstructed image data) to obtain post-processed image data 33. The post-processing performed by the post-processing unit 32 may include, for example, color format conversion (e.g., from YCbCr to RGB), color adjustment, trimming or resampling, or any other processing to generate the decoded image data 31 for display by the display device 34, etc.

[0422] Display device 34 is used to receive post-processed image data 33 to display the image to a user or viewer. Display device 34 can be or includes any type of display for representing the reconstructed image, such as an integrated or external display screen or monitor. For example, the display screen may include a liquid crystal display (LCD), an organic light emitting diode (OLED) display, a plasma display, a projector, a micro LED display, a liquid crystal on silicon (LCoS), a digital light processor (DLP), or any other type of display screen.

[0423] although Figure 1A The source device 12 and destination device 14 are shown as independent devices, but device embodiments may also include both source device 12 and destination device 14, or the functions of both source device 12 and destination device 14, that is, simultaneously including source device 12 or its corresponding functions and destination device 14 or its corresponding functions. In these embodiments, source device 12 or its corresponding functions and destination device 14 or its corresponding functions may be implemented using the same hardware and / or software, or by separate hardware and / or software, or any combination thereof.

[0424] According to the description, Figure 1AThe presence and (accurate) division of different units or functions in the source device 12 and / or destination device 14 shown may vary depending on the actual device and application, which is obvious to those skilled in the art.

[0425] Encoder 20 (e.g., video encoder 20) or decoder 30 (e.g., video decoder 30) or both can be transmitted via, for example, Figure 1B The processing circuitry shown can be implemented as, for example, one or more microprocessors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), discrete logic, hardware, video encoding dedicated processors, or any combination thereof. Encoder 20 can be implemented via processing circuitry 46 to include reference... Figure 2 Encoder 20 refers to various modules discussed herein and / or any other encoder system or subsystem described herein. Decoder 30 may be implemented via processing circuitry 46 to include references. Figure 3 Decoder 30 comprises various modules discussed herein and / or any other decoder system or subsystem described herein. Processing circuitry 46 can be used to perform various operations discussed below. Figure 5 As shown, if some of the technology is implemented in software, the device can store the software instructions in a suitable non-transitory computer-readable storage medium and execute the instructions in hardware using one or more processors, thereby performing the technology of this application. One of the video encoder 20 and video decoder 30 can be integrated into a single device as part of a combined codec (encoder / decoder, CODEC), such as... Figure 1B As shown.

[0426] Source device 12 and destination device 14 may include any of a variety of devices, including any type of handheld or fixed device, such as a laptop or notebook computer, mobile phone, smartphone, tablet or tablet computer, camera, desktop computer, set-top box, television, display device, digital media player, video game console, video streaming device (e.g., content service server or content distribution server), broadcast receiving device, broadcast transmitting device, etc., and may or may not use an operating system of any type. In some cases, source device 12 and destination device 14 may be equipped with components for wireless communication. Therefore, source device 12 and destination device 14 may be wireless communication devices.

[0427] In some cases, Figure 1AThe video decoding system 10 shown is merely exemplary. The technology provided in this application can be applied to video encoding setups (e.g., video encoding or video decoding) that do not necessarily include any data communication between the encoding and decoding devices. In other examples, data is retrieved from local memory, sent over a network, etc. The video encoding device may encode data and store it in memory, and / or the video decoding device may retrieve data from memory and decode it. In some examples, encoding and decoding are performed by devices that do not communicate with each other but simply encode data into memory and / or retrieve and decode data from memory.

[0428] Figure 1B This is an exemplary block diagram of the video decoding system 40 according to an embodiment of this application, such as... Figure 1B As shown, the video decoding system 40 may include an imaging device 41, a video encoder 20, a video decoder 30 (and / or a video encoder / decoder implemented by processing circuitry 46), an antenna 42, one or more processors 43, one or more memory storage devices 44, and / or a display device 45.

[0429] like Figure 1B As shown, the imaging device 41, antenna 42, processing circuitry 46, video encoder 20, video decoder 30, processor 43, memory storage 44, and / or display device 45 are capable of communicating with each other. In different instances, the video decoding system 40 may contain only the video encoder 20 or only the video decoder 30.

[0430] In some instances, antenna 42 can be used to transmit or receive encoded bitstreams of video data. Additionally, in some instances, display device 45 can be used to present video data. Processing circuitry 46 can include application-specific integrated circuit (ASIC) logic, a graphics processor, a general-purpose processor, etc. Video decoding system 40 can also include an optional processor 43, which similarly can include application-specific integrated circuit (ASIC) logic, a graphics processor, a general-purpose processor, etc. Furthermore, memory storage 44 can be any type of memory, such as volatile memory (e.g., static random access memory (SRAM), dynamic random access memory (DRAM), etc.) or non-volatile memory (e.g., flash memory, etc.). In a non-limiting example, memory storage 44 can be implemented using high-speed cache memory. In other instances, processing circuitry 46 can include memory (e.g., cache, etc.) for implementing image buffers, etc.

[0431] In some instances, the video encoder 20 implemented via logic circuitry may include (e.g., implemented via processing circuitry 46 or memory storage 44) an image buffer and (e.g., implemented via processing circuitry 46) a graphics processing unit. The graphics processing unit may be communicatively coupled to the image buffer. The graphics processing unit may include the video encoder 20 implemented via processing circuitry 46 to implement reference... Figure 2 (or Figure 4 or Figure 6 This includes, and / or any other encoder system or subsystem described herein, and various modules thereof. Logic circuits may be used to perform the various operations discussed herein.

[0432] In some instances, the video decoder 30 can be implemented in a similar manner via the processing circuitry 46 to implement the reference. Figure 3 (or Figure 5 or Figure 7 The video decoder 30 and / or any other decoder system or subsystem described herein may be referred to as various modules. In some instances, the logic circuit-implemented video decoder 30 may include an image buffer (implemented via processing circuitry 46 or memory storage 44) and a graphics processing unit (e.g., implemented via processing circuitry 46). The graphics processing unit may be communicatively coupled to the image buffer. The graphics processing unit may include the video decoder 30 implemented via processing circuitry 46 to implement the reference... Figure 3And / or the various modules discussed in any other decoder system or subsystem described herein.

[0433] In some instances, antenna 42 can be used to receive encoded bitstreams of video data. As discussed herein, the encoded bitstream may contain data related to encoded video frames, indicators, index values, mode selection data, etc., such as data related to code segmentation (e.g., transform coefficients or quantized transform coefficients, optional indicators, and / or data defining code segmentation). Video decoding system 40 may also include a video decoder 30 coupled to antenna 42 for decoding the encoded bitstream. Display device 45 is used to display the video frames.

[0434] It should be understood that, for the examples described with reference to video encoder 20 in this application embodiment, video decoder 30 can be used to perform the reverse process. Regarding signaling syntax elements, video decoder 30 can be used to receive and parse such syntax elements, and accordingly decode the associated video data. In some examples, video encoder 20 can entropy-encode syntax elements into an encoded video bitstream. In such instances, video decoder 30 can parse such syntax elements and accordingly decode the associated video data.

[0435] For ease of description, embodiments of this application are described with reference to the Universal Video Coding (VVC) reference software or the High-Efficiency Video Coding (HEVC) developed by the ITU-T Video Coding Experts Group (VCEG) and the Joint Collaboration Team on Video Coding (JCT-VC) of the ISO / IEC Moving Picture Experts Group (MPEG). Those skilled in the art will understand that embodiments of this application are not limited to HEVC or VVC.

[0436] Figure 2 A schematic diagram of an encoder is shown as an example.

[0437] Reference Figure 2 For example, encoder 20 can be entropy encoder 20. Entropy encoder 20 can be used to determine the threshold value and quantization probability group corresponding to the first data 41 according to probability distribution index 42; then, entropy encoding is performed on the first data 41 according to the threshold value and quantization probability group corresponding to the first data 41 to obtain bitstream 43 (an example of encoding image data 21).

[0438] Figure 3A schematic diagram of a decoder is shown as an example.

[0439] Reference Figure 3 For example, decoder 30 can be entropy decoder 30. Entropy decoder 30 can be used to determine the critical value and quantization probability group corresponding to the second data in bitstream 43 according to probability distribution index 42; then, according to the critical value and quantization probability group corresponding to the second data in bitstream 43, entropy decoding is performed on the second data in bitstream 43 to obtain entropy decoded data 44 (an example of decoded image data 31).

[0440] Figure 4 This is a schematic diagram of the encoder structure as an example.

[0441] Reference Figure 4 For example, encoder 20 may include a first processing module 201, a second processing module 202 and an entropy encoder 203.

[0442] For example, the first processing module 201 can be used to process the raw data 45 (which may be...) Figure 1A The original image data 17 or preprocessed image data 19 in the image are processed to obtain the first data 41.

[0443] For example, the second processing module 202 can be used to generate the probability distribution index 42 of the first data.

[0444] In one possible approach, the original data 45 can be input into the second processing module 202, which processes the original data 45 to obtain the probability distribution index 42 of the first data.

[0445] In one possible approach, prior information (such as encoded data) can be input into the second processing module 202, which processes the prior information to obtain the probability distribution index 42 of the first data.

[0446] In one possible approach, the features of the original data 45 can be input into the second processing module 202, which processes the features of the original data 45 to obtain the probability distribution index 42 of the first data.

[0447] It should be understood that this application does not limit the way the second processing module 202 generates the probability distribution index 42 of the first data.

[0448] For example, the entropy coding module 203 can be used to determine the critical value and quantization probability group corresponding to the first data 41 according to the probability distribution index 42; then, according to the critical value and quantization probability group corresponding to the first data 41, the first data 41 is entropy encoded to obtain the bit stream 43.

[0449] Figure 5 This is a schematic diagram of the decoder structure as an example.

[0450] Reference Figure 5 For example, the decoder 30 may include a fourth processing module 204, a third processing module 206, and an entropy decoder 205.

[0451] For example, the fourth processing module 204 can be used to generate the probability distribution index 42 of the second data.

[0452] In one possible approach, prior information (such as decoded data) can be input to the fourth processing module 204, which processes the prior information to obtain the probability distribution index 42 of the second data. In this case, the second processing module 202 in the encoder 20 and the fourth processing module 204 in the decoder 30 can be the same module (or modules with the same function).

[0453] In one possible approach, the bitstream can be input to the fourth processing module 204, which then parses the probability distribution index 42 of the second data from the bitstream.

[0454] In one possible approach, the bitstream can be input into the entropy decoder 205 to obtain the feature corresponding to the probability distribution index 42 of the second data; the feature corresponding to the probability distribution index 42 of the second data can be input into the fourth processing module 204 for processing to obtain the probability distribution index 42 of the second data.

[0455] It should be understood that this application does not limit the way the fourth processing module 204 generates the probability distribution index 42 of the second data.

[0456] For example, the entropy decoder 205 can be used to determine the critical value and quantization probability group corresponding to the second data 46 in the bitstream 43 according to the probability distribution index 42; then, according to the critical value and quantization probability group corresponding to the second data 46 in the bitstream 43, the second data 46 in the bitstream 43 is entropy decoded to obtain entropy decoded data 47.

[0457] For example, the third processing module 206 can be used to process the entropy-decoded data 47 to obtain reconstructed data 48 (an example of decoded image data 31).

[0458] It should be noted that the first processing module 201, the second processing module 202, the third processing module 206 and the fourth processing module 204 can be AI models or traditional probability models, and this application does not impose any restrictions on them.

[0459] Figure 6 This is a schematic diagram of the encoder structure as an example. Figure 6 The encoder in the code is AI encoder 20.

[0460] Reference Figure 6 For example, the AI ​​encoder 20 may include an encoding network 401, a super-encoding network 4021, a super-decoding network 4022, and an entropy encoder 403. The encoding network 401 is an example of the first processing module 201, and the super-prior network is an example of the second processing module 202. The super-prior network includes the super-encoding network 4021 and the super-decoding network 4022.

[0461] Reference Figure 6 The encoding process for image 51 (an example of the original data 45) can be as follows:

[0462] For example, image 51 is input into encoding network 401, where feature extraction is performed to obtain a first feature map 52. Then, the first feature map 52 can be input into supercoding network 4021.

[0463] For example, the super-coding network 4021 can extract features from the first feature map 52 to obtain the second feature map 53. On one hand, the second feature map 53 is input to the super-decoding network 4022, which processes the second feature map 53 to obtain the third feature map 54 and the probability distribution index 55 of the first feature map. On the other hand, the second feature map 53 is input to the entropy encoder 403, which entropy encodes the second feature map 53 to obtain the bitstream 2 (58).

[0464] For example, the difference between the first feature map 52 and the third feature map 54 can be calculated to obtain the residual 56 (an example of the first data 41).

[0465] For example, the probability distribution index 55 of the first feature map (also known as the probability distribution index 55 of the residual) and the residual 56 can be input into the entropy encoder 403; the entropy encoder 403 determines the critical value and quantization probability group corresponding to the residual 56 according to the probability distribution index 55 of the residual; then, the residual 56 is entropy encoded according to the critical value and quantization probability group corresponding to the residual 56 to obtain the code stream 1 (57).

[0466] Figure 7 This is a schematic diagram of the decoder structure as an example. Figure 7 The decoder in the code is an AI decoder.

[0467] Reference Figure 7For example, the AI ​​decoder 30 may include a super-decoding network 501, an entropy decoder 502, and a decoding network 503. The decoding network 503 is an example of the third processing module 206, and the super-decoding network 401 is an example of the fourth processing module 204. The super-decoding network 4022 and the super-decoding network 501 are the same network.

[0468] Reference Figure 7 The decoding process can be as follows:

[0469] For example, bitstream 1 (57) and bitstream 2 (58) can be input to entropy decoder 502. Entropy decoder 502 can entropy decode bitstream 2 (58) to obtain second feature map 53, and input second feature map 53 to super decoding network 501.

[0470] For example, the super-decoding network 501 can process the second feature map 53 to obtain the probability distribution index 55 of the second data and the third feature map 54; and input the probability distribution index 55 of the second data into the entropy decoder 502.

[0471] For example, the entropy decoder 502 can determine the critical value and quantization probability group corresponding to the second data 46 in the bitstream 1 (57) according to the probability distribution index 55 of the second data; then, according to the critical value and quantization probability group corresponding to the second data 46 in the bitstream 1 (57), the entropy decoder 502 performs entropy decoding on the second data 46 in the bitstream 1 (57) to obtain the residual 56.

[0472] For example, the residual 56 and the third feature map 54 can be added together to obtain the first feature map 52; and the first feature map 52 can be input into the decoding network 503.

[0473] For example, the decoding network 503 can perform feature recovery on the first feature map 52 to obtain the reconstructed image 59 (an example of the reconstructed data 48).

[0474] The entropy encoding and entropy decoding processes are explained below.

[0475] For example, multiple probability distributions can be pre-defined, and for each probability distribution, a corresponding index (also called a probability distribution index) can be set; and for each probability distribution, a corresponding critical value and quantized probability set can be calculated. Then, the relationship between the probability distribution index, the critical value, and the quantized probability set can be established, and this relationship, the critical value, and the quantized probability set can be stored as preset information.

[0476] For example, assuming the number of pre-defined probability distributions is N (N is a positive integer), then N probability distribution indices can be set for these N probability distributions. For example, the N indices corresponding to the N probability distributions are: 0, 1, 2, ..., N-1.

[0477] Furthermore, based on the type of reference probability distribution, calculations can be performed with the goal of minimizing relative entropy to obtain N critical values ​​and N quantized probability sets.

[0478] For example, for the i-th probability distribution, a critical value bound_table_r[i] and a quantized probability group pdf_r[i] can be calculated based on the type of the reference probability distribution with the goal of minimizing the relative entropy.

[0479] It should be noted that this application does not restrict the type of reference probability distribution.

[0480] The following example uses a Gaussian distribution as the reference probability distribution to illustrate how to determine the critical value bound_table_r[i] corresponding to the i-th probability distribution and the quantized probability group pdf_r[i].

[0481] The reference probability distribution corresponding to the i-th probability distribution can be a Gaussian distribution with a mean of 0 and a variance of σ. The process of determining the critical value and quantized probability set corresponding to the i-th probability distribution is essentially the process of quantizing the reference probability distribution corresponding to the i-th probability distribution, ultimately yielding the i-th probability distribution (also called the i-th quantized probability distribution); where the mean of the i-th probability distribution is 0 and the variance is σ. Specifically:

[0482] First, calculate P(x) for each integer x from -16384 to 16383, which can be done by referring to the following formula (1):

[0483]

[0484] Next, minimize the objective function; whereby the objective function can be as shown in formula (2):

[0485]

[0486] Where j is an integer between -16384 and 16383; B is the critical value; Q is a quantized probability set; and both B and Q(j) (Q(j) is a value in the quantized probability set) are positive integers.

[0487] For example, the physical meaning of the objective function can be the expected number of bits added to the bitstream after encoding a symbol using INBOUND+OUTBOUND.

[0488] For example, minimizing the objective function yields the minimum critical value B (i.e., bound_table_r[i]) and a set of quantized probabilities Q (i.e., pdf_r[i]).

[0489] For example, the quantized probability group pdf_r[i] may include H values, where H can be determined based on the range of values ​​corresponding to the i-th probability distribution.

[0490] For example, the range of values ​​corresponding to the i-th probability distribution is: [-(bound_table_r[i]-1), (bound_table_r[i]-1)]; H is the sum of the number of integers in the range, and the step size of the integers in the range is M, where M is a positive integer.

[0491] Among them, the H values ​​in the quantized probability group pdf_r[i] correspond one-to-one with the H integers in the range of values.

[0492] For example, if bound_table_r[i] is 1, then the range of values ​​corresponding to the i-th probability distribution includes one integer, i.e., 0, and the quantized probability group corresponding to the i-th probability distribution includes one value (which can be either the quantized probability or the quantized probability numerator).

[0493] For example, if `bound_table_r[i]` is 2, then the range of values ​​corresponding to the i-th probability distribution is [-1, 1]. If M = 1, then the range of values ​​corresponding to the i-th probability distribution can include 3 integers: -1, 0, 1; the quantized probability set corresponding to the i-th probability distribution includes 3 values. If M = 2, then the range of values ​​can include 2 integers: -1, 1; the quantized probability set corresponding to the i-th probability distribution includes 2 values.

[0494] It should be noted that, in the process of minimizing the objective function, restrictions can be imposed on the quantization probability set Q:

[0495] (1) The quantization probability of the j-th value must be strictly greater than 0;

[0496] (2) The sum of the quantized probabilities of the H values ​​in the range corresponding to the i-th probability distribution must be equal to 1;

[0497] (3) For ease of calculation, the denominator of the quantification probability is usually an integer power of 2.

[0498] (4) The quantization probability of a long-tail symbol (or identifier) ​​is 1 / (an integer power of 2).

[0499] By determining the above method, N critical values ​​and N quantized probability groups corresponding to N probability distributions can be calculated. Among these N probability distributions, the relative entropy of n probability distributions is the optimal relative entropy; n is an integer between 1 and N, and n is greater than a preset value. The preset value can refer to the number of probability distributions among the N probability distributions of the existing technology whose relative entropy is the optimal relative entropy.

[0500] Ideally, n = N. That is, the relative entropy of N probability distributions is the optimal relative entropy; in this way, the length of the bitstream obtained by entropy coding can be shortened to the greatest extent.

[0501] The following shows the 35 critical values ​​and 35 quantization probability groups corresponding to the 35 probability distributions when the quantization probability denominator is 256, the quantization probability of the long-tail symbol (or identifier value) in each probability distribution is 1 / 256, N=35, and n=N:

[0502] The 35 probability distributions correspond to the following 35 critical values: 1, 1, 1, 2, 2, 2, 2, 3, 3, 4, 4, 5, 6, 7, 8, 9, 11, 13, 16, 18, 22, 26, 32, 38, 46, 55, 65, 77, 92, 106, 128, 128, 128, 128.

[0503] Suppose that the integers within the range corresponding to each probability distribution are ordered as follows: 0, -1, 1, -2, 2, ..., -

[0504] (bound_table_r[i]-1), (bound_table_r[i]-1); then the 35 quantized probability groups corresponding to the 35 probability distributions are as follows:

[0505] The first quantized probability group includes: 255;

[0506] The second quantized probability group includes: 255;

[0507] The third quantized probability group includes: 255;

[0508] The fourth quantization probability group includes: 251, 2, 2;

[0509] The fifth quantized probability group includes: 245, 5, 5;

[0510] The sixth quantization probability group includes: 231, 12, 12;

[0511] The seventh quantization probability group includes: 211, 22, 22;

[0512] The eighth quantization probability group includes: 188, 33, 34;

[0513] The ninth quantization probability group includes: 157, 48, 48, 1, 1;

[0514] The tenth quantized probability group includes: 134, 57, 56, 4, 4;

[0515] The eleventh quantized probability group includes: 111, 61, 61, 10, 10, 1, 1;

[0516] The twelfth quantized probability group includes: 94, 62, 61, 17, 17, 2, 2;

[0517] The thirteenth quantization probability group includes: 77, 58, 58, 24, 24, 6, 6, 1, 1;

[0518] The fourteenth quantized probability group includes: 63, 52, 52, 29, 29, 11, 11, 3, 3, 1, 1;

[0519] The fifteenth quantized probability group includes: 52, 46, 46, 31, 30, 16, 16, 6, 6, 2, 2, 1, 1;

[0520] The sixteenth quantized probability group includes: 43, 39, 39, 30, 30, 19, 19, 10, 10, 5, 5, 2, 2, 1, 1;

[0521] The seventeenth quantized probability group includes: 35, 33, 33, 28, 28, 21, 21, 13, 13, 8, 8, 4, 4, 2, 2, 1, 1;

[0522] The eighteenth quantized probability group includes: 29, 28, 28, 25, 25, 20, 20, 15, 15, 10, 10, 7, 7, 4, 4, 2, 2, 1, 1, 1, 1;

[0523] The nineteenth quantized probability group includes: 24, 23, 23, 21, 21, 19, 18, 15, 15, 12, 12, 9, 9, 6, 6, 4, 4, 3, 3, 2, 2, 1, 1, 1, 1;

[0524] The twentieth quantified probability group includes: 19, 19, 19, 18, 18, 17, 17, 15, 15, 12, 12, 10, 10, 8, 8, 6, 6, 4, 4, 3, 3, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1;

[0525] The twenty-first quantized probability group includes: 17, 16, 16, 16, 16, 15, 15, 13, 13, 12, 12, 10, 10, 9, 9, 7, 7, 6, 6, 4, 4, 3, 3, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1;

[0526] The twenty-second quantized probability group includes: 14, 13, 13, 13, 13, 12, 12, 12, 12, 11, 11, 10, 10, 9, 9, 8, 8, 6, 6, 5, 5, 4, 5, 4, 4, 3, 3, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1;

[0527] The twenty-third quantized probability group includes: 11, 11, 11, 11, 11, 10, 10, 10, 10, 10, 10, 9, 9, 8, 8, 7, 7, 7, 7, 6, 6, 5, 5, 5, 5, 4, 4, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1;

[0528] The twenty-fourth quantized probability group includes: 9, 9, 9, 9, 9, 9, 9, 8, 8, 8, 8, 8, 8, 7, 7, 7, 7, 6, 6, 6, 6, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1;

[0529] The twenty-fifth quantized probability group includes: 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 6, 6, 6, 6, 6, 6, 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1;

[0530] The twenty-sixth quantified probability group includes: 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, 6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1 ...

[0531] The twenty-seventh quantized probability group includes: 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1 ...

[0532] The twenty-eighth quantized probability group includes: 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2 ... ,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1;

[0533] The twenty-ninth quantified probability group includes: 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2 ... ,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1;

[0534] The thirtieth quantized probability group includes: 2, ...1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ,1 ...

[0535] The thirty-first quantized probability group includes: 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1 ... ,1 ...

[0536] The thirty-second quantized probability group includes: 1, ... 1, ... ,1 ... ,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1;

[0537] The thirty-third quantized probability group includes: 1, ... 1, ... ,1 ... ,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1;

[0538] The thirty-fourth quantized probability group includes: 1, ... 1, ... ,1 ... ,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1;

[0539] The thirty-fifth quantized probability group includes: 1, ... 1, ... ,1 ... ,1 ...

[0540] For the first, second, and third probability distributions:

[0541] The critical value is: 1;

[0542] The integer value within the range is 0;

[0543] The quantized probability set includes a single value: 255;

[0544] In other words, the integer 0 corresponds to a quantized probability numerator of 255.

[0545] For the fourth probability distribution:

[0546] The critical value is 2;

[0547] The integers within the range are: 0, -1, 1;

[0548] The quantized probability set includes three values: 251, 2, 2;

[0549] In other words, the quantization probability numerator for integer 0 is 251; the quantization probability numerator for integer -1 is 2; and the quantization probability numerator for integer 1 is 2.

[0550] And so on, without going into further detail here.

[0551] Based on the aforementioned 35 critical values ​​and 35 quantized probability groups, it can be seen that the critical values ​​of probability distributions 32 to 35 are the same, and their quantized probability groups are also the same. Therefore, the 35 probability distributions can be reduced to 32 (by removing probability distributions 33 to 35), and distributions 33 to 35 can be replaced by distribution 32. The 32 critical values ​​and 32 quantized probability groups corresponding to these 32 probability distributions are used to form the preset information. At this point, N = 32.

[0552] The preset information can be represented in the following ways:

[0553] bound_table_r=[1,1,1,2,2,2,2,2,3,3,4,4,5,6,7,8,9,11,13,16,18,22,26,32,38,46,55,65,77,92,106,128]

[0554] pdf_r = [

[255] ,

[255] ,

[255] , [251, 2, 2], [245, 5, 5], [231, 12, 12], [211, 22, 22], [188, 33, 34], [157, 48, 48, 1, 1], [134, 57, 56, 4, 4], [111, 61, 61, 1 0,10,1,1],[94,62,61,17,17,2,2],[77,58,58,24,24,6,6,1,1],[63,52,52,29,29,11,11,3,3,1,1],[52,46,46,31,30,16,16,6,6,2,2,1,1],[43 ,39,39,30,30,19,19,10,10,5,5,2,2,1,1],[35,33,33,28,28,21,21,13,13,8,8,4,4,2,2,1,1],[29,28,28,25,25,20,20,15,15,10,10,7,7,4,4, 2,2,1,1,1,1],[24,23,23,21,21,19,18,15,15,12,12,9,9,6,6,4,4,3,3,2,2,1,1,1,1],[19,19,19,18,18,17,17,15,15,12,12,10,10,8,8,6,6,4 ,4,3,3,2,2,1,1,1,1,1,1,1,1,1,1],[17,16,16,16,16,16,15,15,13,13,12,12,10,10,9,9,7,7,6,6,4,4,3,3,2,2,2,2,1,1,1,1,1,1,1,1,1],[14,13,13,13 ,13,12,12,12,12,11,11,10,10,9,9,8,8,6,6,5,5,4,5,4,4,3,3,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1],[11,11,11,11,11,10,10,10,10,10,10,9, 9,8,8,7,7,7,7,6,6,5,5,5,5,4,4,3,3,3,3,3,2,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1],[9,9,9,9,9,9,9,9,8,8,8,8,8,7,7,7,7,6,6,6,6,6,5,5 ,5,5,4,4,4,4,4,4,4,3,3,3,3,3,2,2,2,2,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1],[7,7,7,7,7,7,7,7,7,7,7,7,7,6,6,6,6,6,6,6,5,5,5，5，5，5，5，5，4，4，4，4，4，4，3，3，3，3，3，3，2，2，2，2，2，2，2，2，2，2，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1]，[6，6，6，6，6，6，6，6，6，5，6，5，5，5，5，5，5，5，5，5，5，5，5，4，4，4，4，4，4，4，4，4，4，3，3，3，3，3，3，3，3，3，3，2，2，2，2，2，2，2，2，2，2，2，2，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1]，[5，5，5，5，5，5，5，5，5，4，4，4，4，4，4，4，4，4，4，4，4，4，4，4，4，4，4，4，4，4，4，3，3，3，3，3，3，3，3，3，3，3，3，3，3，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1]，[4，4，4，4，4，4，4，4，4，4，4，4，4，4，4，4，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1]，[3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1]，[2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1]，[2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1]，[1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1, ... ,1 ...

[0555] Where bound_table_r represents the array used to store critical values, bound_table_r[i] represents the critical value corresponding to the i-th probability distribution; pdf_r represents the array used to store quantized probability groups, pdf_r[i] represents the quantized probability group corresponding to the i-th probability distribution, and i is the probability distribution index.

[0556] Another preset information can be as follows:

[0557] bound_table_r=[1,1,2,2,2,2,2,2,3,3,4,5,5,6,8,9,11,13,15,18,21,25,30,36,43,51,60,71,85,100,118,128]

[0558] pdf_r=[

[255] ,

[255] ,[253,1,1],[251,2,2],[245,5,5],[231,12,12],[211,22,22],[188,33,34],[157,48,48,1,1],[134,57,56,4,4],[107,61, 61,12,12,1,1],[89,60,60,19,19,3,3,1,1],[74,56,57,26,26,7,7,1,1],[61,51,51,30,30,12,12,3,3,1,1],[49,44,44,31,31,17,17,7,7,2,2, 1,1,1,1],[41,37,38,30,29,20,20,11,11,5,5,2,2,1,1,1,1,1],[33,32,32,27,27,20,20,14,14,8,8,5,5,2,2,1,1,1,1,1,1],[27,27,26,23,24,20 ,20,15,15,11,11,7,7,4,4,3,3,1,1,1,1,1,1,1,1,1,1],[22,22,21,20,20,18,18,15,15,12,12,9,9,7,7,5,5,3,3,2,2,1,1,1,1,1,1,1,1],[19,18,18 ,17,17,16,16,14,14,12,12,10,10,8,8,6,6,5,5,3,3,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1],[15,15,15,14,14,13,14,13,12,11,11,10,10,9,9,7,7,6 ,6,5,5,4,4,3,3,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1],[13,12,12,12,12,12,11,11,11,11,10,10,9,9,8,8,8,8,7,7,6,6,5,5,4,4,3,3,3,3,2,2,2,2,2 ,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1],[10,9,10,10,10,9,9,9,9,9,9,8,8,8,8,7,7,7,7,7,6,6,5,5,5,5,4,4,4,4,3,3,3,3,3,2,2,2,2,2,2,1,1,1,1,1, 1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1],[8,7,8,8,8,8,8,8,8,8,7,7,7,7,7,7,7,6,6,6,6,6,6,5,5,5,5,5,5,5,4,4,4,4,3,3,3,3,3,3,3,2,2,2,2,2,2,2,2,2,1,1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1]，[7，6，7，6，7，6，6，6，6，6，6，6，6，6，6，6，6，5，5，5，5，5，5，5，5，4，4，4，4，4，4，4，4，3，3，3，3，3，3，3，3，2，2，2，2，2，2，2，2，2，2，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1]，[5，5，5，5，5，5，5，5，5，5，5，5，5，5，5，5，5，5，5，4，4，4，4，4，4，4，4，4，4，4，4，4，4，3，3，3，3，3，3，3，3，3，3，3，3，2，2，2，2，2，2，2，2，2，2，2，2，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1]，[4，4，4，4，4，4，4，4，4，4，4，4，4，4，4，4，4，4，4，4，4，4，4，4，4，4，4，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1]，[4，3，4，3，4，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，3，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1]，[3，2，3，2，3，2，3，2，3，2，3，2，3，3，2，3，3，2，3，3，3，2，3，3，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1]，[2，1，2，1，2，1，2，1，2，1，2，1，2，1，2，1，2，1，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，2，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1]，[2，1，2，1，2，1，2，1，2，1，2，1，2，1，2，1，2，1，2，1，2，2，1，2，1，2，2，1，2，1，2，2，2，1，2，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1，1, ... ,1 ... 1, ... ,1 ...

[0559] This reduces redundancy in preset information and lowers the memory usage of preset information; in addition, it also reduces the number of bits required to represent the probability distribution index.

[0560] It should be understood that this application does not restrict the sorting order of the N quantized probability groups, nor does it restrict the sorting order of the N critical values. The position of the i-th critical value corresponding to the i-th probability distribution among the N critical values ​​is the same as the position of the i-th quantized probability group corresponding to the i-th probability distribution among the N quantized probability groups.

[0561] It should be understood that this application does not restrict the order of multiple values ​​in any quantized probability set.

[0562] It should be noted that, for the quantized probability group corresponding to the i-th probability distribution, if the c-th value "X1" corresponds to the integer "A" within the range of values ​​corresponding to the i-th probability distribution, and the g-th value "X2" corresponds to the integer "-A" within the range of values ​​corresponding to the i-th probability distribution, then the c-th value in the quantized probability group corresponding to the i-th probability distribution can be replaced with "X2", and the g-th value in the quantized probability group corresponding to the i-th probability distribution can be replaced with "X2" instead of "X1". c and g are positive integers less than H, and c is not equal to g.

[0563] For example, suppose the tenth quantized probability group includes: 134, 57, 56, 4, 4; if the integer corresponding to "57" is -1 and the integer corresponding to "56" is "1", then the tenth quantized probability group can be updated to: 134, 56, 57, 4, 4.

[0564] It should be understood that N can take other values, for example, N=16; then 16 critical values ​​can be selected from the above 32 critical values, and 16 quantization probability groups corresponding to the above 16 critical values ​​can be selected from the 32 quantization probability groups to form the preset information; that is to say, this application does not restrict the value of N.

[0565] It should be understood that M can take other data, such as M = 1 or 2, and this application does not limit this.

[0566] It should be understood that the denominator of the quantization probability corresponding to the quantization probability group can be other data, such as 512; in this case, the numerator of the quantization probability in the above 32 quantization probability groups can be doubled to obtain another quantization probability group. That is to say, this application does not restrict the value of the denominator of the quantization probability corresponding to the quantization probability group.

[0567] The step of establishing preset information can be performed by an entropy encoder.

[0568] Figure 8 The flowchart illustrates the entropy encoding process as an example. Figure 8 The entropy encoding process in [the context] can be performed by an entropy encoder.

[0569] S801, obtain the first data, the probability distribution index of the first data, and preset information. The preset information includes N critical values ​​corresponding to N probability distributions and N quantized probability groups. Each of the N probability distributions corresponds to a critical value and a quantized probability group.

[0570] In one possible approach, the first data could be the raw data 45 input to encoder 20.

[0571] In one possible approach, the first data could be the data obtained after processing the raw data 45 input to the encoder 20.

[0572] For example, the first data may include multiple symbols; for instance, a symbol may be the pixel value of a pixel in an image or the feature value of a pixel value; another example is that a symbol may be the amplitude of a frequency in an audio frame, and so on.

[0573] S802, based on the probability distribution index of the first data, find the critical value and quantized probability group corresponding to the first data from the preset information.

[0574] For example, based on the preset information shown above, assuming the probability distribution index of the first data is 11, then the bound_table_r

[11] = 4 can be found from the critical value array; and the pdf_r

[11] = [134, 57, 56, 4, 4] can be found from the array of quantized probability groups.

[0575] S803, based on the critical value and quantization probability group corresponding to the first data, entropy encoding is performed on the first data.

[0576] For example, S803 may include the following sub-steps:

[0577] S8031, determine whether the absolute value of the entropy-encoded symbol is less than the critical value corresponding to the entropy-encoded symbol.

[0578] S8032, when the absolute value of the symbol to be entropy encoded is greater than or equal to the critical value corresponding to the symbol to be entropy encoded, the first entropy encoding algorithm is used to entropy encode the symbol to be entropy encoded; the symbol to be entropy encoded is updated to the tag value; the second entropy encoding algorithm is used to entropy encode the updated symbol to be entropy encoded based on the quantization probability group corresponding to the symbol to be entropy encoded.

[0579] For example, suppose the probability distribution index of the symbol to be entropy encoded is 11, the critical value corresponding to the symbol to be entropy encoded is bound_table_r

[11] = 4, and the quantization probability group corresponding to the symbol to be entropy encoded is pdf_r

[11] = [134, 57, 56, 4, 4]. If the symbol to be entropy encoded is 5, the first entropy encoding algorithm can be used to entropy encode "5". Then, the symbol to be entropy encoded "5" is updated to the tag value (for example, -bound_table_r

[11] = -4). After that, the second entropy encoding algorithm can be used to entropy encode the updated symbol to be entropy encoded "-4" according to the quantization probability group corresponding to the symbol to be entropy encoded, pdf_r

[11] = [134, 57, 56, 4, 4].

[0580] For example, the first entropy coding algorithm can be the OUTBOUND algorithm, and the second entropy coding algorithm can be the INBOUND algorithm; it should be understood that this application does not limit the first entropy coding algorithm and the second entropy coding algorithm.

[0581] S8033: When the absolute value of the symbol to be entropy encoded is less than the critical value corresponding to the symbol to be entropy encoded, the second entropy encoding algorithm is adopted to entropy encode the symbol according to the quantization probability group corresponding to the symbol to be entropy encoded.

[0582] For example, suppose the probability distribution index of the symbol to be entropy encoded is 11, the critical value corresponding to the symbol to be entropy encoded is bound_table_r

[11] = 4, and the quantization probability group corresponding to the symbol to be entropy encoded is pdf_r

[11] = [134, 57, 56, 4, 4]. If the symbol to be entropy encoded is 3, then the second entropy encoding algorithm can be used to entropy encode the symbol “3” according to the quantization probability group corresponding to the symbol to be entropy encoded, pdf_r

[11] = [134, 57, 56, 4, 4].

[0583] Figure 9 The flowchart illustrates the entropy decoding process as an example. Figure 9 The entropy decoding process in the code can be performed by the entropy decoder.

[0584] S901, receiving the bitstream.

[0585] S902, obtain the probability distribution index and preset information of the second data in the bitstream. The preset information includes N critical values ​​and N quantization probability groups corresponding to N probability distributions. Each of the N probability distributions corresponds to a critical value and a quantization probability group.

[0586] For example, the second data in the bitstream can be the data obtained by entropy encoding the first data as described above.

[0587] S903, based on the probability distribution index of the second data, find the critical value and quantized probability group corresponding to the second data from the preset information.

[0588] For example, S903 can be described with reference to the above description of S802, and will not be repeated here.

[0589] S904, based on the critical value corresponding to the first data and the quantization probability group corresponding to the first data, perform entropy decoding on the second data.

[0590] For example, S904 may include the following sub-steps:

[0591] S9041, the second entropy decoding algorithm is used to perform entropy decoding on the symbol to be entropy decoded based on the quantization probability group corresponding to the symbol to be entropy decoded in the second data, and the entropy decoded symbol is obtained;

[0592] S9042, determine whether the entropy-decoded symbol is a tag value.

[0593] S9043, when the entropy-decoded symbol is a tag value, the first entropy decoding algorithm is used to entropy decode the entropy-decoded symbol, and the entropy-decoded data is updated to the result of entropy decoding using the first entropy decoding algorithm.

[0594] For example, suppose the probability distribution index of the symbol to be entropy decoded is 11, the critical value corresponding to the second data is bound_table_r

[11] = 4, and the quantization probability group corresponding to the second data is pdf_r

[11] = [134, 57, 56, 4, 4]. If the second entropy decoding algorithm is used to entropy decode the symbol based on the quantization probability group corresponding to the symbol to be entropy decoded, the entropy decoded symbol is -4. Since "-4" is a marker value, the first entropy decoding algorithm can be used to entropy decode the entropy decoded symbol "-4" to obtain "5".

[0595] The entropy encoding / decoding method of this application and the entropy encoding / decoding method of the prior art were tested using the same data. The results are shown in Tables 1 and 2:

[0596] Table 1

[0597] Code length loss Existing entropy coding methods 0.6％ The entropy coding method of this application 0.5％

[0598] In Table 1, the first row shows that the average length of the bitstream obtained using the existing entropy coding method is 0.6% longer than the theoretical optimal length.

[0599] The second line states that the average length of the bitstream obtained using the entropy coding method described in this application is 0.5% longer than the theoretical optimal length.

[0600] As shown in Table 1, when entropy encoding is applied to the same data, the bitstream length obtained by entropy encoding in this application is smaller.

[0601] Table 2

[0602]

[0603] In Table 2, the bitstream length obtained by entropy coding using Model 1 based on the existing critical value table and quantization probability group is 0.67% longer than the theoretical optimal length. The bitstream length obtained by entropy coding using Model 2 based on the existing critical value table and quantization probability group is 0.62% longer than the theoretical optimal length. ...and so on.

[0604] In Table 2, the bitstream length obtained by entropy coding using Model 1 based on the critical value table and quantization probability group of this application is 0.46% longer than the theoretical optimal length. The bitstream length obtained by entropy coding using Model 2 based on the critical value table and quantization probability group of this application is 0.40% longer than the theoretical optimal length. ...and so on.

[0605] As shown in Table 2, for the same data, regardless of the entropy coding model used, the bitstream length obtained by entropy coding in this application is smaller.

[0606] Figure 10 This is a schematic diagram of an apparatus for entropy encoding, as exemplarily shown. The apparatus for entropy encoding can be used to perform the methods of the foregoing embodiments; therefore, the beneficial effects it achieves can be referred to in the beneficial effects of the corresponding methods provided above, and will not be repeated here.

[0607] Reference Figure 10 A device for entropy coding, comprising:

[0608] The first information acquisition module 1001 is used to acquire first data, the probability distribution index of the first data, and preset information. The preset information includes N critical values ​​and N quantized probability groups corresponding to N probability distributions. Each of the N probability distributions corresponds to a critical value and a quantized probability group. The relative entropy of the n probability distributions is the optimal relative entropy. The relative entropy of the first probability distribution is used to characterize the distance between the reference probability distribution corresponding to the first probability distribution and the first probability distribution itself. The first probability distribution is any one of the n probability distributions. N is a positive integer, and n is an integer between 1 and N, where n is greater than a preset value. Each of the N probability distributions corresponds to a probability distribution index.

[0609] The first information lookup module 1002 is used to look up the critical value and quantized probability group corresponding to the first data from preset information according to the probability distribution index of the first data.

[0610] The first entropy encoding module 1003 is used to entropy encode the first data according to the critical value and quantization probability group corresponding to the first data.

[0611] In addition, the apparatus for entropy coding may also include a module for generating preset information.

[0612] Figure 11 This is a schematic diagram of an apparatus for entropy decoding, as exemplarily shown. The apparatus for entropy decoding can be used to perform the methods of the foregoing embodiments; therefore, the beneficial effects it achieves can be referred to in the beneficial effects of the corresponding methods provided above, and will not be repeated here.

[0613] Reference Figure 11 A device for entropy decoding, comprising:

[0614] The first code stream receiving module 1101 is used to receive code streams;

[0615] The second information acquisition module 1102 is used to acquire the probability distribution index and preset information of the second data in the bitstream; wherein, the preset information includes N critical values ​​and N quantization probability groups corresponding to N probability distributions; each of the N probability distributions corresponds to a critical value and a quantization probability group; the relative entropy of the n probability distributions is the optimal relative entropy; the relative entropy of the first probability distribution is used to characterize the distance between the reference probability distribution corresponding to the first probability distribution and the first probability distribution, the first probability distribution being any one of the n probability distributions; N is a positive integer, n is an integer between 1 and N, and n is greater than the preset value; each of the N probability distributions corresponds to a probability distribution index;

[0616] The second information lookup module 1103 is used to look up the critical value and quantized probability group corresponding to the second data from preset information according to the probability distribution index of the second data.

[0617] The first entropy decoding module 1104 is used to perform entropy decoding on the second data based on the critical value and quantization probability group corresponding to the second data.

[0618] In addition, the apparatus for entropy decoding may also include a module for generating preset information.

[0619] Figure 12 This is a schematic diagram of an apparatus for entropy encoding, as exemplarily shown. The apparatus for entropy encoding can be used to perform the methods of the foregoing embodiments; therefore, the beneficial effects it achieves can be referred to in the beneficial effects of the corresponding methods provided above, and will not be repeated here.

[0620] Reference Figure 12 A device for entropy coding, comprising:

[0621] The third information acquisition module 1201 is used to acquire first data, the probability distribution index of the first data, and preset information; wherein, the preset information includes N critical values ​​and N quantized probability groups corresponding to N probability distributions, each of the N probability distributions corresponds to a critical value and a quantized probability group, and N is a positive integer; each of the N probability distributions corresponds to a probability distribution index;

[0622] The third information lookup module 1202 is used to look up the critical value and quantized probability group corresponding to the first data from the preset information according to the probability distribution index of the first data.

[0623] The second entropy encoding module 1203 is used to entropy encode the first data according to the critical value and quantization probability group corresponding to the first data;

[0624] Among them, the first critical value is 2;

[0625] The second critical value is: 2;

[0626] The third critical value is: 2;

[0627] The fourth critical value is 2;

[0628] The fifth critical value is: 2;

[0629] The sixth critical value is 3;

[0630] The seventh critical value is 3;

[0631] The first quantized probability group includes: 251, 2, 2;

[0632] The second quantized probability group includes: 245, 5, 5;

[0633] The third quantized probability group includes: 231, 12, 12;

[0634] The fourth quantization probability group includes: 211, 22, 22;

[0635] The fifth quantized probability group includes: 188, 33, 34;

[0636] The sixth quantized probability group includes: 157, 48, 48, 1, 1;

[0637] The seventh quantized probability group includes: 134, 57, 56, 4, 4.

[0638] In addition, the apparatus for entropy coding may also include a module for generating preset information.

[0639] Figure 13 This is a schematic diagram of an apparatus for entropy decoding, as exemplarily shown. The apparatus for entropy decoding can be used to perform the methods of the foregoing embodiments; therefore, the beneficial effects it achieves can be referred to in the beneficial effects of the corresponding methods provided above, and will not be repeated here.

[0640] Reference Figure 13 A device for entropy decoding, comprising:

[0641] The second stream receiving module 1301 is used to receive streams;

[0642] The fourth information acquisition module 1302 is used to acquire the probability distribution index and preset information of the second data in the bitstream; wherein, the preset information includes N critical values ​​and N quantization probability groups corresponding to N probability distributions, each of the N probability distributions corresponds to a critical value and a quantization probability group, and N is a positive integer; each of the N probability distributions corresponds to a probability distribution index;

[0643] The fourth information lookup module 1303 is used to look up the critical value and quantized probability group corresponding to the second data from the preset information according to the probability distribution index of the second data.

[0644] The second entropy decoding module 1304 is used to perform entropy decoding on the second data based on the critical value and quantization probability group corresponding to the second data.

[0645] Among them, the first critical value is 2;

[0646] The second critical value is: 2;

[0647] The third critical value is: 2;

[0648] The fourth critical value is 2;

[0649] The fifth critical value is: 2;

[0650] The sixth critical value is 3;

[0651] The seventh critical value is 3;

[0652] The first quantized probability group includes: 251, 2, 2;

[0653] The second quantized probability group includes: 245, 5, 5;

[0654] The third quantized probability group includes: 231, 12, 12;

[0655] The fourth quantization probability group includes: 211, 22, 22;

[0656] The fifth quantized probability group includes: 188, 33, 34;

[0657] The sixth quantized probability group includes: 157, 48, 48, 1, 1;

[0658] The seventh quantized probability group includes: 134, 57, 56, 4, 4.

[0659] In addition, the apparatus for entropy decoding may also include a module for generating preset information.

[0660] In one example, Figure 14 A schematic block diagram illustrating an embodiment of the present application shows an apparatus 1400. The apparatus 1400 may include a processor 1401 and a transceiver / transceiver pin 1402, and optionally, a memory 1403.

[0661] The various components of device 1400 are coupled together via bus 1404, which includes a data bus, a power bus, a control bus, and a status signal bus. However, for clarity, all buses are referred to as bus 1404 in the figure.

[0662] Optionally, the memory 1403 can be used to store instructions from the foregoing method embodiments. The processor 1401 can be used to execute the instructions in the memory 1403, control the receive pin to receive signals, and control the transmit pin to transmit signals.

[0663] Device 1400 may be an electronic device or a chip of an electronic device in the above method embodiments.

[0664] All relevant content of each step involved in the above method embodiments can be referenced from the functional description of the corresponding functional module, and will not be repeated here.

[0665] This application also provides a chip, including one or more interface circuits and one or more processors; the one or more processors receive or send data through the one or more interface circuits, and when the one or more processors execute computer instructions, the steps of the above-described related method steps that implement the method in the above embodiments are executed. The interface circuit is a transceiver / transceiver pin 1402.

[0666] This embodiment also provides a computer-readable storage medium storing computer instructions. When the computer instructions are executed on an electronic device, the electronic device performs the aforementioned method steps to implement the methods described in the above embodiments.

[0667] This embodiment also provides a computer program product containing computer instructions that, when executed by a computer or processor, cause the computer to perform the aforementioned related steps to implement the methods described in the above embodiments.

[0668] In addition, embodiments of this application also provide an apparatus, which may specifically be a chip, component, or module. The apparatus may include a connected processor and a memory; wherein the memory is used to store computer execution instructions, and when the apparatus is running, the processor may execute the computer execution instructions stored in the memory to cause the chip to execute the methods in the above-described method embodiments.

[0669] In this embodiment, the electronic device, computer-readable storage medium, computer program product or chip are all used to execute the corresponding methods provided above. Therefore, the beneficial effects that can be achieved can be referred to the beneficial effects of the corresponding methods provided above, and will not be repeated here.

[0670] Through the above description of the embodiments, those skilled in the art will understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0671] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another apparatus, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0672] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units; that is, it can be located in one place or distributed in multiple different locations. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0673] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0674] Any content in the various embodiments of this application, as well as any content in the same embodiment, can be freely combined. Any combination of the above content is within the scope of this application.

[0675] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, in essence, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0676] The steps of the methods or algorithms described in conjunction with the embodiments of this application can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in random access memory (RAM), flash memory, read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium well known in the art. One exemplary embodiment couples a storage medium to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can reside in an ASIC.

[0677] Those skilled in the art will recognize that the functions described in the embodiments of this application in one or more of the above examples can be implemented using hardware, software, firmware, or any combination thereof. When implemented using software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or code on a computer-readable medium. Computer-readable media include computer-readable storage media and communication media, wherein communication media include any medium that facilitates the transmission of a computer program from one place to another. Storage media can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0678] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method of entropy decoding, characterized by, The method comprises: receiving a code stream; obtaining a probability distribution index and preset information of second data in the code stream; wherein the preset information comprises N critical values corresponding to N probability distributions and N quantized probability groups; each of the N probability distributions corresponds to a critical value and a quantized probability group; each of the N probability distributions corresponds to a probability distribution index; according to the probability distribution index of the second data, searching for the critical value and the quantized probability group corresponding to the second data from the preset information; according to the critical value and the quantized probability group corresponding to the second data, performing entropy decoding on the second data.

2. The method of claim 1, wherein, N＝32。 3. The method according to claim 1 or 2, characterized in that, The quantized probability group corresponding to the second probability distribution comprises H values, H is determined according to the critical value corresponding to the second probability distribution, H is a positive integer, and the second probability distribution is any one of the N probability distributions.

4. The method of claim 3, wherein, The value range corresponding to the second probability distribution is [-(bound_table_r[i]-1), (bound_table_r[i]-1)], H is the sum of the number of integers in the value range, and the value step of the integer in the value range is M, M is a positive integer; wherein, bound_table_r[i] is the critical value corresponding to the second probability distribution, i is the probability distribution index of the second probability distribution, and i is a positive integer.

5. The method according to any one of claims 1 to 4, characterized in that, Part of the N critical values are respectively: 2, 2, 2, 2, 2, 3, 3.

6. The method according to any one of claims 1 to 5, characterized in that, The N quantized probability groups comprise: The first quantized probability group comprises: 251, 2, 2; The second quantized probability group comprises: 245, 5, 5; The third quantized probability group comprises: 231, 12, 12; The fourth quantized probability group comprises: 211, 22, 22; The fifth quantized probability group comprises: 188, 33, 34; The sixth quantized probability group comprises: 157, 48, 48, 1, 1; The seventh quantized probability group comprises: 134, 57, 56, 4, 4.

7. The method according to any one of claims 1 to 6, characterized in that, When N=32, the N critical values are respectively: 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 4, 4, 5, 6, 7, 8, 9, 11, 13, 16, 18, 22, 26, 32, 38, 46, 55, 65, 77, 92, 106, 128.

8. The method according to any one of claims 1 to 7, characterized in that, When N=32, the N quantized probability groups comprise: The first quantized probability group comprises: 255; The second quantized probability group comprises: 255; The third quantized probability group comprises: 255; The fourth quantized probability group comprises: 251, 2, 2; The fifth quantized probability group comprises: 245, 5, 5; The sixth quantized probability group comprises: 231, 12, 12; The seventh quantized probability group comprises: 211, 22, 22; The eighth quantized probability group comprises: 188, 33, 34; The ninth quantized probability group comprises: 157, 48, 48, 1, 1; The tenth quantized probability group comprises: 134, 57, 56, 4, 4; The eleventh quantized probability group comprises: 111, 61, 61, 10, 10, 1, 1; The twelfth quantized probability group comprises: 94, 62, 61, 17, 17, 2, 2; A thirteenth set of quantization probabilities comprises: 77, 58, 58, 24, 24, 6, 6, 1, 1; A fourteenth set of quantization probabilities comprises: 63, 52, 52, 29, 29, 11, 11, 3, 3, 1, 1; A fifteenth set of quantization probabilities comprises: 52, 46, 46, 31, 30, 16, 16, 6, 6, 2, 2, 1, 1; A sixteenth set of quantization probabilities comprises: 43, 39, 39, 30, 30, 19, 19, 10, 10, 5, 5, 2, 2, 1, 1; A seventeenth set of quantization probabilities comprises: 35, 33, 33, 28, 28, 21, 21, 13, 13, 8, 8, 4, 4, 2, 2, 1, 1; An eighteenth set of quantization probabilities comprises: 29, 28, 28, 25, 25, 20, 20, 15, 15, 10, 10, 7, 7, 4, 4, 2, 2, 1, 1, 1, 1; A nineteenth set of quantization probabilities comprises: 24, 23, 23, 21, 21, 19, 18, 15, 15, 12, 12, 9, 9, 6, 6, 4, 4, 3, 3, 2, 2, 1, 1, 1, 1; A twentieth set of quantization probabilities comprises: 19, 19, 19, 18, 18, 17, 17, 15, 15, 12, 12, 10, 10, 8, 8, 6, 6, 4, 4, 3, 3, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1; A twenty-first set of quantization probabilities comprises: 17, 16, 16, 16, 16, 15, 15, 13, 13, 12, 12, 10, 10, 9, 9, 7, 7, 6, 6, 4, 4, 3, 3, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1; A twenty-second set of quantization probabilities comprises: 14, 13, 13, 13, 13, 12, 12, 12, 12, 11, 11, 10, 10, 9, 9, 8, 8, 6, 6, 5, 5, 4, 5, 4, 4, 3, 3, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1; A twenty-third set of quantization probabilities comprises: 11, 11, 11, 11, 11, 10, 10, 10, 10, 10, 10, 9, 9, 8, 8, 7, 7, 7, 7, 6, 6, 5, 5, 5, 5, 4, 4, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1; A twenty-fourth set of quantization probabilities comprises: 9, 9, 9, 9, 9, 9, 9, 8, 8, 8, 8, 8, 8, 7, 7, 7, 7, 6, 6, 6, 6, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1. a twenty-fifth set of quantized probabilities comprises: 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 6, 6, 6, 6, 6, 6, 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ​ ​ ​ The twenty ninth set of quantized probabilities comprises: 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ​ A thirty-first set of quantized probabilities comprises: 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ​ 9. An entropy coding method, characterized by, ​ obtaining first data, a probability distribution index of the first data and preset information; wherein the preset information comprises N critical values corresponding to N probability distributions and N quantization probability groups; each of the N probability distributions corresponds to a critical value and a quantization probability group; each of the N probability distributions corresponds to a probability distribution index; finding, according to the probability distribution index of the first data, the critical value and the quantization probability group corresponding to the first data from the preset information; performing entropy coding on the first data according to the critical value and the quantization probability group corresponding to the first data.

10. The method of claim 9, wherein, N＝32。 11. The method according to claim 9 or 10, characterized in that, The quantization probability group corresponding to the second probability distribution comprises H values, wherein H is determined according to the critical value corresponding to the second probability distribution, H is a positive integer, and the second probability distribution is any one of the N probability distributions.

12. The method of claim 11, wherein, The value range corresponding to the second probability distribution is [-(bound_table_r[i]-1), (bound_table_r[i]-1)], H is the sum of the number of integers in the value range, and the value step of the integer in the value range is M, M is a positive integer. Wherein, bound_table_r[i] is the critical value corresponding to the second probability distribution, i is the probability distribution index of the second probability distribution, and i is a positive integer.

13. The method according to any one of claims 9 to 12, characterized in that, Part of the N critical values are 2, 2, 2, 2, 2, 3, 3 respectively.

14. The method according to any one of claims 9 to 13, characterized in that, The N quantization probability groups comprise: The first quantization probability group comprises: 251, 2, 2; The second quantization probability group comprises: 245, 5, 5; The third quantization probability group comprises: 231, 12, 12; The fourth quantization probability group comprises: 211, 22, 22; The fifth quantization probability group comprises: 188, 33, 34; The sixth quantization probability group comprises: 157, 48, 48, 1, 1; The seventh quantization probability group comprises: 134, 57, 56, 4, 4.

15. The method according to any one of claims 9 to 14, characterized in that, When N=32, the N critical values are 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 4, 4, 5, 6, 7, 8, 9, 11, 13, 16, 18, 22, 26, 32, 38, 46, 55, 65, 77, 92, 106, 128 respectively.

16. The method according to any one of claims 9 to 15, characterized in that, When N=32, the N quantization probability groups comprise: The first quantization probability group comprises: 255; The second quantization probability group comprises: 255; The third quantization probability group comprises: 255; The fourth quantization probability group comprises: 251, 2, 2; The fifth quantization probability group comprises: 245, 5, 5; The sixth quantization probability group comprises: 231, 12, 12; The seventh quantization probability group comprises: 211, 22, 22; The eighth quantization probability group comprises: 188, 33, 34; The ninth quantization probability group comprises: 157, 48, 48, 1, 1; The tenth quantization probability group comprises: 134, 57, 56, 4, 4; The eleventh quantization probability group comprises: 111, 61, 61, 10, 10, 1, 1; The twelfth quantization probability group comprises: 94, 62, 61, 17, 17, 2, 2; A thirteenth set of quantization probabilities comprises: 77, 58, 58, 24, 24, 6, 6, 1, 1; A fourteenth set of quantization probabilities comprises: 63, 52, 52, 29, 29, 11, 11, 3, 3, 1, 1; A fifteenth set of quantization probabilities comprises: 52, 46, 46, 31, 30, 16, 16, 6, 6, 2, 2, 1, 1; A sixteenth set of quantization probabilities comprises: 43, 39, 39, 30, 30, 19, 19, 10, 10, 5, 5, 2, 2, 1, 1; A seventeenth set of quantization probabilities comprises: 35, 33, 33, 28, 28, 21, 21, 13, 13, 8, 8, 4, 4, 2, 2, 1, 1; An eighteenth set of quantization probabilities comprises: 29, 28, 28, 25, 25, 20, 20, 15, 15, 10, 10, 7, 7, 4, 4, 2, 2, 1, 1, 1, 1; A nineteenth set of quantization probabilities comprises: 24, 23, 23, 21, 21, 19, 18, 15, 15, 12, 12, 9, 9, 6, 6, 4, 4, 3, 3, 2, 2, 1, 1, 1, 1; A twentieth set of quantization probabilities comprises: 19, 19, 19, 18, 18, 17, 17, 15, 15, 12, 12, 10, 10, 8, 8, 6, 6, 4, 4, 3, 3, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1; A twenty-first set of quantization probabilities comprises: 17, 16, 16, 16, 16, 15, 15, 13, 13, 12, 12, 10, 10, 9, 9, 7, 7, 6, 6, 4, 4, 3, 3, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1; A twenty-second set of quantization probabilities comprises: 14, 13, 13, 13, 13, 12, 12, 12, 12, 11, 11, 10, 10, 9, 9, 8, 8, 6, 6, 5, 5, 4, 5, 4, 4, 3, 3, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1; A twenty-third set of quantization probabilities comprises: 11, 11, 11, 11, 11, 10, 10, 10, 10, 10, 10, 9, 9, 8, 8, 7, 7, 7, 7, 6, 6, 5, 5, 5, 5, 4, 4, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1; A twenty-fourth set of quantization probabilities comprises: 9, 9, 9, 9, 9, 9, 9, 8, 8, 8, 8, 8, 8, 7, 7, 7, 7, 6, 6, 6, 6, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1. The twenty-fifth quantized probability group includes: 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 6, 6, 6, 6, 6, 6, 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1; The twenty-sixth quantified probability group includes: 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, 6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1 ... The twenty-seventh quantized probability group includes: 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1 ... The twenty-eighth quantized probability group includes: 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2 ... ,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1; The twenty-ninth quantified probability group includes: 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2 ... ,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1; The thirtieth quantized probability group includes: 2, ...1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ,1 ... The thirty-first set of quantized probabilities comprises: 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ​ 17. An apparatus for entropy coding, the apparatus comprising: ​ The first information obtaining module is configured to obtain first data, a probability distribution index of the first data, and preset information; the preset information comprises N critical values corresponding to N probability distributions and N quantization probability groups; each of the N probability distributions corresponds to a critical value and a quantization probability group; and each of the N probability distributions corresponds to a probability distribution index. The first information searching module is configured to search, according to the probability distribution index of the first data, the critical value and the quantization probability group corresponding to the first data from the preset information. The first entropy encoding module is configured to perform entropy encoding on the first data according to the critical value and the quantization probability group corresponding to the first data.

18. An apparatus for entropy decoding, the apparatus comprising: The first code stream receiving module is configured to receive a code stream. The second information obtaining module is configured to obtain a probability distribution index of second data in the code stream and preset information; the preset information comprises N critical values corresponding to N probability distributions and N quantization probability groups; each of the N probability distributions corresponds to a critical value and a quantization probability group; and each of the N probability distributions corresponds to a probability distribution index. The second information searching module is configured to search, according to the probability distribution index of the second data, the critical value and the quantization probability group corresponding to the second data from the preset information. The first entropy decoding module is configured to perform entropy decoding on the second data according to the critical value and the quantization probability group corresponding to the second data. The entropy encoding method according to any one of claims 9-16.

19. An entropy encoder, characterized by The entropy decoding method according to any one of claims 1-8, or the entropy decoding method according to any one of claims 32-34.

20. An entropy decoder, characterized by The entropy encoder according to claim 19.

21. An encoder comprising: The entropy decoder according to claim 20.

22. A decoder, comprising: The electronic device comprises:

23. A decoder, characterized by a memory and a processor, which are coupled to each other; the memory stores program instructions, which, when executed by the processor, cause the electronic device to perform the entropy encoding method according to any one of claims 9-16. The electronic device comprises:

24. A decoder, characterized in that a memory and a processor, which are coupled to each other; the memory stores program instructions, which, when executed by the processor, cause the electronic device to perform the entropy decoding method according to any one of claims 1-8. The computer readable storage medium stores a computer program, which, when executed on a computer or a processor, causes the computer or the processor to perform the method according to any one of claims 1-8 or 9-16.

25. A computer-readable storage medium, characterized in that, The computer program product comprises computer instructions, which, when executed by a computer or a processor, cause the steps of the method according to any one of claims 1-8 or 9-16 to be performed.

26. A computer program product, characterised in that, ​

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