A method and device for determining video compression quality

Abstract

The present invention provides a method and device for determining the quality of video compression, the method comprising: calculating an error sequence based on the acquired original video picture and the compressed video picture; performing segmentation operation on the error sequence to obtain multiple subsequences; obtaining the statistical distribution of the error sequence based on the subsequences; and determining the quality of video compression based on the statistical distribution of the error sequence through an information entropy calculation formula. The present application uses a compression stability index for specific measurement, the better the stability, the better the video compression quality, and vice versa. Through the method provided by the present application, since a segmented batch processing method is used, the video file analysis consumption is small, and since the statistical error is low, the phase space of the data is converged during the statistical process, so that the data will be characterized relatively concentratedly within a fixed range, and the information entropy and other methods themselves have a certain stability, so the statistical results will be relatively limited to a level.

Description

Technical Field

[0001] The present application belongs to the technical field of video compression processing, and in particular, relates to a method and device for determining video compression quality. Background Art

[0002] The amount of original video data is very large, which is not conducive to video storage and transmission. Therefore, the video needs to be compressed. This process is usually called encoding. The purpose of video compression encoding is to reduce the amount of video data to a reasonable range.

[0003] In the field of video compression coding, lossy coding is used in most cases. For lossy compression, the higher the compression rate, the smaller the compressed file, but the greater the loss. In some cases, the specific degree of loss cannot be perceived by human eyes, so an objective indicator is needed to evaluate the quality of video compression. The current mainstream methods are VMAF evaluation method, PSNR evaluation method, SSIM evaluation method, etc. However, the disadvantages of such methods are that the calculation process is relatively complicated, and there is inconsistency between the results obtained by objective calculation and the results obtained by subjective observation of the human eye. Summary of the invention

[0004] The present application provides a method and device for determining video compression quality, so as to at least solve the problem that the degree of loss in current video lossy compression cannot be objectively measured.

[0005] According to one aspect of the present application, a method for determining video compression quality is provided, comprising:

[0006] Calculating an error sequence based on the acquired original video picture and the compressed video picture;

[0007] Perform segmentation operation on the error sequence to obtain multiple subsequences;

[0008] Obtain the statistical distribution of the error sequence based on the subsequences;

[0009] The quality of video compression is determined based on the statistical distribution of the error sequence using the information entropy calculation formula.

[0010] In one embodiment, a segmentation operation is performed on the error sequence to obtain a plurality of subsequences, including:

[0011] Determine the data with the same number from the error sequence as the input sequence;

[0012] The input sequence is segmented according to a preset fixed length to obtain multiple subsequences of the same fixed length.

[0013] In one embodiment, obtaining a statistical distribution of an error sequence according to a subsequence includes:

[0014] Count the number of occurrences of a single fixed number in a subsequence;

[0015] According to the definition of fixed length value, multiple subsequences are combined to obtain the statistical distribution of the first measure of the error sequence;

[0016] Count the number of occurrences of double-digit fixed numbers in the subsequence;

[0017] According to the definition of the fixed length value, a plurality of subsequences are combined to obtain the statistical distribution of the second measure of the error sequence.

[0018] In one embodiment, the quality of video compression is determined according to the statistical distribution of the error sequence using an information entropy calculation formula, including:

[0019] Input the statistical distribution of the error sequence into the information entropy calculation formula, and calculate the information entropy values ​​of the first measure and the second measure respectively;

[0020] The quality of video compression is determined based on the information entropy value.

[0021] According to another aspect of the present application, a device for determining video compression quality is also provided, including:

[0022] An error sequence calculation unit, used for calculating an error sequence according to the acquired original video picture and the compressed video picture;

[0023] A subsequence segmentation unit, used for performing segmentation operation on the error sequence to obtain multiple subsequences;

[0024] A statistical distribution unit, used for obtaining a statistical distribution of an error sequence according to a subsequence;

[0025] The information entropy calculation unit is used to determine the quality of video compression according to the statistical distribution of the error sequence through the information entropy calculation formula.

[0026] In one embodiment, the subsequence segmentation unit includes:

[0027] An input sequence module is used to determine data with the same number from the error sequence as an input sequence;

[0028] The segmentation module is used to segment the input sequence according to a preset fixed length to obtain multiple subsequences of the same fixed length.

[0029] In one embodiment, the statistical distribution unit includes:

[0030] The first statistical module is used to count the number of occurrences of a single fixed number in a subsequence;

[0031] A first measure statistical distribution module, used to obtain a statistical distribution of a first measure of the error sequence in combination with a plurality of subsequences according to a definition of a fixed length value;

[0032] The second statistical module is used to count the number of occurrences of double-digit fixed numbers in the subsequence;

[0033] The second measure statistical distribution module is used to obtain the statistical distribution of the second measure of the error sequence by combining multiple subsequences according to the definition of the fixed length value.

[0034] In one embodiment, the information entropy calculation unit includes:

[0035] An information entropy value calculation module is used to input the statistical distribution of the error sequence into the information entropy calculation formula to calculate the information entropy values ​​of the first measure and the second measure respectively;

[0036] The compression quality assessment module is used to determine the quality of video compression according to the information entropy value.

[0037] This application uses the compression stability index for specific measurement. The better the stability, the better the video compression quality, and vice versa. Through the method provided by this application, the video file analysis consumption is small due to the use of a segmented batch processing method, and because the statistical error is low, the phase space of the data is converged during the statistical process, so that the data will be relatively concentrated in a fixed range for characterization, and the information entropy and other methods themselves have a certain stability, so the statistical results will be relatively limited to a level. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 A method for determining video compression quality is provided in this application.

[0040] Figure 2 In the embodiment of the present application, a segmentation operation is performed on the error sequence to obtain multiple subsequences.

[0041] Figure 3 In the embodiment of the present application, the statistical distribution of the error sequence is obtained according to the subsequence.

[0042] Figure 4 In the embodiment of the present application, the quality of video compression is determined according to the statistical distribution of the error sequence through the information entropy calculation formula.

[0043] Figure 5 This is a system structure diagram of video compression quality evaluation in an embodiment of the present application.

[0044] Figure 6 It is a device for determining video compression quality in an embodiment of the present application.

[0045] Figure 7 4 is a structural block diagram of a subsequence segmentation unit in an embodiment of the present application.

[0046] Figure 8 4 is a structural block diagram of a statistical distribution unit in an embodiment of the present application.

[0047] Fig. 9 This is a structural block diagram of the information entropy calculation unit in an embodiment of the present application.

[0048] Fig.10 This is a specific implementation of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] Traditional video compression quality evaluation methods perform calculations and analysis by comparing the media information of the original video and the compressed video, and use pixel differences as a measurement basis, such as mean square error, peak signal-to-noise ratio, etc., which can reflect the degree of image distortion to a certain extent. The objective shortcomings of this method are that most video compression encoding algorithms have certain image edge optimizations, so there will be large differences in the pixels of the compressed edge images, which objectively affects the stability of the evaluation and is inconsistent with the results of subjective judgment by the human eye.

[0051] In order to solve such problems, this application uses quantitative indicators such as information entropy, introduces shift operations to amplify statistical distribution characteristics, and establishes an evaluation model for file security.

[0052] like Figure 1 As shown, the present application provides a method for determining video compression quality, comprising:

[0053] S101: Calculate an error sequence according to the acquired original video picture and the compressed video picture.

[0054] S102: Perform segmentation operation on the error sequence to obtain multiple subsequences.

[0055] S103: Obtain statistical distribution of the error sequence according to the subsequence.

[0056] S104: Determine the quality of video compression according to the statistical distribution of the error sequence using an information entropy calculation formula.

[0057] In a specific embodiment, the original video image and the compressed video image are used as input to calculate the error sequence. Then the error sequence is segmented to obtain multiple subsequences, and the compression stability index is proposed by using the subsequence statistics of the error sequence distribution characteristics to evaluate the video compression quality, avoid the negative impact of individual errors, and narrow the consistency between the objective evaluation method and the subjective feeling.

[0058] In one embodiment, the error sequence is segmented to obtain multiple subsequences, such as Figure 2 As shown, including:

[0059] S201: Determine data with the same number from the error sequence as the input sequence.

[0060] S202: Segment the input sequence according to a preset fixed length to obtain a plurality of subsequences of the same fixed length.

[0061] In a specific embodiment, all obtained 01 sequences are taken as input, and segmented according to a fixed length m to obtain multiple subsequences of length m.

[0062] In one embodiment, the statistical distribution of the error sequence is obtained based on the subsequence, such as Figure 3 As shown, including:

[0063] S301: Count the number of occurrences of a single fixed number in a subsequence.

[0064] S302: According to the definition of the fixed length value, multiple subsequences are combined to obtain a statistical distribution of a first measure of the error sequence.

[0065] S303: Count the number of occurrences of double-digit fixed numbers in the subsequence.

[0066] S304: According to the definition of the fixed length value, multiple subsequences are combined to obtain a statistical distribution of a second measure of the error sequence.

[0067] In a specific embodiment, statistics are performed on each subsequence, and the number of "1"s in the sequence is counted, which is recorded as p. i , according to the definition of m value, p i The phase space is restricted to 0≤p≤m, and multiple subsequences are combined to obtain the statistical distribution of the P measure of the sequence.

[0068] Count each subsequence and count the number of consecutive "01"s in the sequence, denoted as q i , according to the definition of m value, q iThe phase space is restricted to In , multiple subsequences are combined to obtain the statistical distribution of the Q measure of the sequence.

[0069] In one embodiment, the quality of video compression is determined according to the statistical distribution of the error sequence using an information entropy calculation formula, such as Figure 4 As shown, including:

[0070] S401: Input the statistical distribution of the error sequence into the information entropy calculation formula, and calculate the information entropy values ​​of the first measure and the second measure respectively.

[0071] S402: Determine the quality of video compression according to the information entropy value.

[0072] In a specific embodiment, the statistical distribution of P and Q measures is used as input and brought into the information entropy calculation formula to calculate the information entropy values ​​of P and Q measures respectively. Information entropy is a quantitative index used to reflect the complexity of a set. According to the definition of information entropy, the larger the information entropy, the more dispersed the overall error space distribution is. Conversely, the smaller the information entropy, the more convergent the error space is. Information entropy is used as a compression stability index. The lower the information entropy, the smoother the compression process is.

[0073] In a specific embodiment, Figure 5 The system structure diagram for video compression quality evaluation includes a difference sequence generation module 1, a sequence segmentation processing module 2, a segmentation P measurement statistics module 3, a segmentation Q measurement statistics module 4, and a compression stability index generation module 5.

[0074] The difference sequence generation module 1 generates a one-dimensional difference sequence with a length of n*n by calculating the difference between the original video picture (n*n) and the compressed video picture (n*n) pixels. The difference is quantized according to a 16-bit binary number to obtain a 01 sequence with a length of 16*n*n.

[0075] The file segmentation processing module 2 segments the 01 sequence obtained by the module 1 according to the fixed length m to obtain multiple subsequences of fixed length.

[0076] For binary data after binarization, the number of statistical signals "1" represents the distribution of the "valid" part of the data set, which is used to represent the static distribution characteristics of the data. The number of pulses of the statistical signal "01" represents the number of pulses from "no" to "yes", which is used to represent the dynamic distribution characteristics of the data.

[0077] The segmented P measure statistics module 3 counts the number of "1" occurrences in a fixed-length 01 sequence, which is recorded as pi (for example, in a sequence of length 32, the number of "1" occurrences is 12, then the sequence is p i=12), according to the definition of m value (given in module 1), p i The phase space is restricted to 0≤p≤m, and multiple subsequences are combined to obtain the statistical distribution of the sequence. Each subsequence obtains a basic measure P, and multiple subsequences form a probability statistical distribution of the P measure.

[0078] Segmented Q measure statistics module 4, for a fixed length 01 sequence, counts the number of consecutive "01" occurrences in the sequence, denoted as q i (For example, in a sequence of length 32, the number of occurrences of "01" is 5, so the sequence is q i =5), according to the definition of m value (given in module 1), q i The phase space is restricted to In , multiple subsequences are combined to obtain the statistical distribution of the sequence, each subsequence obtains a basic measure Q, and multiple subsequences form the probability statistical distribution of the Q measure.

[0079] The compression stability index generation module 5 inputs the statistical distribution of P and Q measures and calculates the information entropy formula

[0080]

[0081] The corresponding information entropy can be obtained as an indicator of compression stability.

[0082] Based on the same inventive concept, the embodiments of the present application also provide a device for determining video compression quality, which can be used to implement the method described in the above embodiments, as described in the following embodiments. Since the principle of solving the problem by the device for determining video compression quality is similar to the method for determining video compression quality, the implementation of the device for determining video compression quality can refer to the implementation of the method for determining video compression quality, and the repeated parts will not be repeated. As used below, the terms "unit" or "module" can be a combination of software and / or hardware that implements predetermined functions. Although the system described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0083] According to another aspect of the present application, a device for determining video compression quality is also provided. Figure 6 As shown, including:

[0084] The error sequence calculation unit 601 is used to calculate the error sequence according to the acquired original video picture and the compressed video picture;

[0085] A subsequence segmentation unit 602, configured to segment the error sequence to obtain a plurality of subsequences;

[0086] A statistical distribution unit 603, configured to obtain a statistical distribution of an error sequence according to the subsequence;

[0087] The information entropy calculation unit 604 is used to determine the quality of video compression according to the statistical distribution of the error sequence through the information entropy calculation formula.

[0088] In a specific embodiment, the original video image and the compressed video image are used as input to calculate the error sequence. Then the error sequence is segmented to obtain multiple subsequences, and the compression stability index is proposed by using the subsequence statistics of the error sequence distribution characteristics to evaluate the video compression quality, avoid the negative impact of individual errors, and narrow the consistency between the objective evaluation method and the subjective feeling.

[0089] In one embodiment, if Figure 7 As shown, the subsequence segmentation unit 602 includes:

[0090] An input sequence module 701 is used to determine data with the same number from the error sequence as an input sequence;

[0091] The segmentation module 702 is used to segment the input sequence according to a preset fixed length to obtain a plurality of subsequences of the same fixed length.

[0092] In a specific embodiment, all obtained 01 sequences are taken as input, and segmented according to a fixed length m to obtain multiple subsequences of length m.

[0093] In one embodiment, if Figure 8 As shown, the statistical distribution unit 603 includes:

[0094] A first counting module 801 is used to count the number of occurrences of a single fixed number in a subsequence;

[0095] A first measure statistical distribution module 802, configured to obtain a statistical distribution of a first measure of an error sequence by combining multiple subsequences according to the definition of a fixed length value;

[0096] The second counting module 803 is used to count the number of occurrences of double-digit fixed numbers in the subsequence;

[0097] The second measure statistical distribution module 804 is used to obtain the statistical distribution of the second measure of the error sequence by combining multiple subsequences according to the definition of the fixed length value.

[0098] In a specific embodiment, statistics are performed on each subsequence, and the number of "1"s in the sequence is counted, which is recorded as p. i , according to the definition of m value, p i The phase space is restricted to 0≤p≤m, and multiple subsequences are combined to obtain the statistical distribution of the P measure of the sequence.

[0099] Count each subsequence and count the number of consecutive "01"s in the sequence, denoted as q i , according to the definition of m value, q i The phase space is restricted to In , multiple subsequences are combined to obtain the statistical distribution of the Q measure of the sequence.

[0100] In one embodiment, if Fig. 9 As shown, the information entropy calculation unit 604 includes:

[0101] An information entropy value calculation module 901 is used to input the statistical distribution of the error sequence into the information entropy calculation formula, and calculate the information entropy values ​​of the first measure and the second measure respectively;

[0102] The compression quality evaluation module 902 is used to determine the quality of video compression according to the information entropy value.

[0103] In a specific embodiment, the statistical distribution of P and Q measures is used as input and brought into the information entropy calculation formula to calculate the information entropy values ​​of P and Q measures respectively. Information entropy is a quantitative index used to reflect the complexity of a set. According to the definition of information entropy, the larger the information entropy, the more dispersed the overall error space distribution is. Conversely, the smaller the information entropy, the more convergent the error space is. Information entropy is used as a compression stability index. The lower the information entropy, the smoother the compression process is.

[0104] In a specific embodiment, the system structure of video compression quality evaluation includes a difference sequence generation module 1, a sequence segmentation processing module 2, a segmentation P measurement statistics module 3, a segmentation Q measurement statistics module 4, and a compression stability index generation module 5.

[0105] The difference sequence generation module 1 generates a one-dimensional difference sequence with a length of n*n by calculating the difference between the original video picture (n*n) and the compressed video picture (n*n) pixels. The difference is quantized according to a 16-bit binary number to obtain a 01 sequence with a length of 16*n*n.

[0106] The file segmentation processing module 2 segments the 01 sequence obtained by the module 1 according to the fixed length m to obtain multiple subsequences of fixed length.

[0107] For binary data after binarization, the number of statistical signals "1" represents the distribution of the "valid" part of the data set, which is used to represent the static distribution characteristics of the data. The number of pulses of the statistical signal "01" represents the number of pulses from "no" to "yes", which is used to represent the dynamic distribution characteristics of the data.

[0108] Segmented P measure statistics module 3, for a fixed length 01 sequence, count the number of "1" in the sequence, denoted as p i (For example, in a sequence of length 32, the number of occurrences of "1" is 12, then the sequence is p i =12), according to the definition of m value (given in module 1), p i The phase space is restricted to 0≤p≤m, and multiple subsequences are combined to obtain the statistical distribution of the sequence. Each subsequence obtains a basic measure P, and multiple subsequences form a probability statistical distribution of the P measure.

[0109] Segmented Q measure statistics module 4, for a fixed length 01 sequence, counts the number of consecutive "01" occurrences in the sequence, denoted as q i (For example, in a sequence of length 32, the number of occurrences of "01" is 5, so the sequence is q i =5), according to the definition of m value (given in module 1), q i The phase space is restricted to In , multiple subsequences are combined to obtain the statistical distribution of the sequence, each subsequence obtains a basic measure Q, and multiple subsequences form the probability statistical distribution of the Q measure.

[0110] The compression stability index generation module 5 inputs the statistical distribution of P and Q measures and calculates the information entropy formula

[0111]

[0112] The corresponding information entropy can be obtained as an indicator of compression stability.

[0113] The embodiments of the present application also provide a specific implementation of an electronic device that can implement all the steps in the method in the above embodiments, see Fig.10 , the electronic device specifically includes the following contents:

[0114] Processor 1001, memory 1002, communication interface 1003, bus 1004 and non-volatile memory 1005;

[0115] The processor 1001, memory 1002, and communication interface 1003 communicate with each other via the bus 1004;

[0116] The processor 1001 is used to call the computer program in the memory 1002 and the non-volatile memory 1005. When the processor executes the computer program, all the steps in the method in the above embodiment are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0117] S101: Calculate an error sequence according to the acquired original video picture and the compressed video picture.

[0118] S102: Perform segmentation operation on the error sequence to obtain multiple subsequences.

[0119] S103: Obtain statistical distribution of the error sequence according to the subsequence.

[0120] S104: Determine the quality of video compression according to the statistical distribution of the error sequence using an information entropy calculation formula.

[0121] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps of the method in the above embodiments. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, all the steps of the method in the above embodiments are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0122] S101: Calculate an error sequence according to the acquired original video picture and the compressed video picture.

[0123] S102: Perform segmentation operation on the error sequence to obtain multiple subsequences.

[0124] S103: Obtain statistical distribution of the error sequence according to the subsequence.

[0125] S104: Determine the quality of video compression according to the statistical distribution of the error sequence using an information entropy calculation formula.

[0126] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the hardware + program embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. Although the embodiment of this specification provides the method operation steps as described in the embodiment or flow chart, more or less operation steps can be included based on conventional or non-creative means. The order of steps listed in the embodiment is only one way of executing the order of many steps, and does not represent the only execution order. When the device or terminal product in practice is executed, it can be executed in sequence or in parallel according to the method shown in the embodiment or the accompanying drawings (for example, a parallel processor or a multi-threaded processing environment, or even a distributed data processing environment). The term "include", "include" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, product or device including a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements inherent to such a process, method, product or device. In the absence of more restrictions, it is not excluded that there are other identical or equivalent elements in the process, method, product or device including the elements. For the convenience of description, the above device is described by functions divided into various modules and described separately. Of course, when implementing the embodiments of this specification, the functions of each module can be implemented in the same one or more software and / or hardware, or the module implementing the same function can be implemented by a combination of multiple sub-modules or sub-units. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point, the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The present invention is described with reference to the flowchart and / or block diagram of the method, device (system) and computer program product according to the embodiments of the present invention. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the process and / or box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the process Figure 1 A process or multiple processes and / or boxes Figure 1A device for specifying functions in a box or multiple boxes. It should be understood by those skilled in the art that the embodiments of this specification may be provided as methods, systems or computer program products. Therefore, the embodiments of this specification may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. Each embodiment in this specification is described in a progressive manner, and the same and similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts refer to the partial description of the method embodiment. In the description of this specification, the description of the reference term "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiment of this specification. In this specification, the schematic representation of the above terms does not necessarily target the same embodiment or example. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction. The above description is only an embodiment of the embodiment of this specification and is not intended to limit the embodiment of this specification. For those skilled in the art, the embodiment of this specification may have various changes and variations. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the embodiment of this specification shall be included in the scope of the claims of the embodiment of this specification.

Claims

1. A method for determining video compression quality, characterized in that: include: Calculating an error sequence based on the acquired original video picture and the compressed video picture; Performing segmentation operation on the error sequence to obtain multiple subsequences; Obtaining a statistical distribution of the error sequence according to the subsequence; Determining the quality of video compression according to the statistical distribution of the error sequence by using an information entropy calculation formula; The step of calculating the error sequence according to the acquired original video picture and the compressed video picture includes: Calculate the difference between the pixels of the original video picture and the compressed video picture; Generate a one-dimensional difference sequence as an error sequence according to the difference; The smaller the information entropy, the more convergent the error space is and the smoother the compression process is.

2. The method for determining video compression quality according to claim 1, characterized in that: The step of performing a segmentation operation on the error sequence to obtain a plurality of subsequences comprises: Determine data with the same number from the error sequence as an input sequence; The input sequence is segmented according to a preset fixed length to obtain a plurality of subsequences of the same fixed length.

3. The method for determining video compression quality according to claim 1, characterized in that: The obtaining the statistical distribution of the error sequence according to the subsequence comprises: Counting the number of occurrences of a single fixed number in the subsequence; According to the definition of the fixed length value, combining a plurality of the subsequences, a statistical distribution of a first measure of the error sequence is obtained; Counting the number of occurrences of double-digit fixed numbers in the subsequence; According to the definition of the fixed length value, a plurality of the subsequences are combined to obtain a statistical distribution of a second measure of the error sequence.

4. The method for determining video compression quality according to claim 1, characterized in that: The method of determining the quality of video compression according to the statistical distribution of the error sequence by using an information entropy calculation formula includes: Inputting the statistical distribution of the error sequence into the information entropy calculation formula, and calculating the information entropy values ​​of the first measure and the second measure respectively; The quality of video compression is determined according to the information entropy value.

5. The method for determining video compression quality according to claim 1, characterized in that: The step of performing a segmentation operation on the error sequence to obtain a plurality of subsequences comprises: quantizing the one-dimensional difference sequence according to 16-bit binary numbers to obtain a sequence with a length of 16×n×n; The sequence is segmented according to a fixed length m to obtain a plurality of subsequences of fixed length.

6. A device for determining video compression quality, characterized in that: include: An error sequence calculation unit, used for calculating an error sequence according to the acquired original video picture and the compressed video picture; A subsequence segmentation unit, used for performing a segmentation operation on the error sequence to obtain a plurality of subsequences; A statistical distribution unit, used for obtaining a statistical distribution of the error sequence according to the subsequence; An information entropy calculation unit, used to determine the quality of video compression according to the statistical distribution of the error sequence through an information entropy calculation formula; A difference sequence generation module generates a one-dimensional difference sequence by calculating the difference between the pixels of the original video picture and the compressed video picture, and generates a one-dimensional difference sequence as an error sequence according to the difference; The compression quality evaluation module is specifically used to determine that the smaller the information entropy, the more convergent the error space and the smoother the compression process.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the method for determining video compression quality according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for determining video compression quality according to any one of claims 1 to 5 are implemented.

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