CN113645466B - Image deblocking algorithm based on random probability - Google Patents
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- CN113645466B CN113645466B CN202110733139.7A CN202110733139A CN113645466B CN 113645466 B CN113645466 B CN 113645466B CN 202110733139 A CN202110733139 A CN 202110733139A CN 113645466 B CN113645466 B CN 113645466B
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- 238000004422 calculation algorithm Methods 0.000 title claims abstract description 22
- 238000011156 evaluation Methods 0.000 claims abstract description 26
- 238000012545 processing Methods 0.000 claims abstract description 9
- 238000004364 calculation method Methods 0.000 claims description 5
- 230000000739 chaotic effect Effects 0.000 claims description 3
- 238000000034 method Methods 0.000 abstract description 12
- 230000000903 blocking effect Effects 0.000 abstract description 4
- 230000006835 compression Effects 0.000 abstract description 3
- 238000007906 compression Methods 0.000 abstract description 3
- 230000002194 synthesizing effect Effects 0.000 abstract description 2
- 238000004891 communication Methods 0.000 description 5
- 238000012216 screening Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 238000012805 post-processing Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 2
- 238000013139 quantization Methods 0.000 description 2
- 230000002411 adverse Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000019771 cognition Effects 0.000 description 1
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- 230000002123 temporal effect Effects 0.000 description 1
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- 230000016776 visual perception Effects 0.000 description 1
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
- H04N19/102—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or selection affected or controlled by the adaptive coding
- H04N19/12—Selection from among a plurality of transforms or standards, e.g. selection between discrete cosine transform [DCT] and sub-band transform or selection between H.263 and H.264
- H04N19/122—Selection of transform size, e.g. 8x8 or 2x4x8 DCT; Selection of sub-band transforms of varying structure or type
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
- H04N19/169—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding
- H04N19/17—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object
- H04N19/176—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a block, e.g. a macroblock
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
- H04N19/169—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding
- H04N19/182—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being a pixel
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/42—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation
- H04N19/423—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation characterised by memory arrangements
Abstract
Aiming at the problem that the blocking effect of an image after lossy compression is image blurring and the subjective experience and objective evaluation are relatively poor, the invention provides an image deblocking algorithm based on random probability. The algorithm processes after the image decoding, removes the blocking effect, and simultaneously retains more image details, thereby improving the subjective and objective quality sum of the image. The method mainly comprises the following steps: dividing an image block processing unit; calculating evaluation values of different DC compensations; determining an optimal compensation scheme; and synthesizing a final image. The invention solves the problems of image blurring and detail texture loss caused by the conventional deblocking algorithm, and compared with the conventional deblocking algorithm, the image processed by the deblocking algorithm is more natural and the detail texture is more abundant.
Description
Technical Field
The invention relates to the field of digital image post-processing, in particular to an image deblocking algorithm based on random probability.
Background
With the rapid development of computers and the internet, multimedia data communication technologies taking images and videos as important expression forms rapidly fly, and simple text and voice communication forms do not meet the daily demands of people. The multimedia communication is loved in various industries, is widely applied to the fields of remote education, teleconference, video telephone, security monitoring and the like, and changes living, learning and working modes of people. However, when multimedia comes from the world, the amount of data in communication is very large, and the original image video data cannot be transmitted in real time in a network with limited bandwidth or stored in a low-capacity storage medium, so that image codec has been developed. Common codecs such as h.264 or h.265 reduce the amount of data by removing redundant information in space and time and performing corresponding quantization and entropy coding on characteristic coefficients of different frequency domains. Typically video images can be efficiently compressed to tens or even hundreds of times, which greatly reduces the requirements of video images for communication bandwidth and storage medium capacity.
Many application scenes have higher requirements on the compression multiple of the image, and a higher compression ratio means that more image details are irreversibly lost, and a larger quantization coefficient can cause the image to have a blocky fracture phenomenon. The conventional deblocking algorithm solves the problem of image block breakage, but brings a new problem that the image becomes blurred. This is mainly due to the fact that the conventional deblocking algorithm mainly adopts a temporal interpolation filtering method to smooth the boundaries of the image coding blocks. Blurring of the image can reduce the user's visual experience. Therefore, it is an urgent need to develop an image deblocking algorithm that can preserve more details without blurring the image.
Disclosure of Invention
The invention mainly aims to provide an image deblocking algorithm based on random probability, which divides images into processing units which are not overlapped with each other, reduces image storage, reduces image processing time delay, processes image blocks in sequence, combines the random probability with image continuity, decides an optimal compensation scheme, and compensates the images.
In order to achieve the above object, the image deblocking algorithm based on random probability provided by the present invention comprises the following steps:
s1, dividing a decoded image into M-N image blocks which are not overlapped with each other according to the parameter setting of a coder-decoder;
s2, calculating evaluation values of various compensation schemes for the image blocks according to the image continuity;
s3, determining an optimal compensation scheme by combining the random number and the evaluation value;
s4, generating a final image block according to the optimal compensation scheme.
Preferably, N and M are each an integer multiple of 2 and 6 or less.
Preferably, the specific steps of S2 are: and taking upper boundary pixels and left boundary pixels in the image block to respectively perform DC compensation under W conditions, and calculating reference pixels of the image block adjacent to the image block as a result to respectively obtain evaluation values of W DC compensation schemes.
Preferably, W should be an odd number greater than 1, the compensation value being an integer and the overall distribution being symmetrical about the origin. I.e., offset (1+n) +offset (W-n) =0, where n is an integer greater than or equal to 0 and less than W, where Offset (x) represents the DC compensation coefficient corresponding to the x-th compensation scheme.
Preferably, the left boundary and the upper boundary in the image block are taken for DC compensation, and the absolute sum of the difference is calculated with the boundary outside the image block to obtain an evaluation value Wherein P is in (i) Pixel value representing left or upper boundary in block, P out (i) Representing the pixel value of the left or upper boundary outside the block.
Preferably, the reference pixels should be from an image block before being processed.
Preferably, the evaluation value uses an absolute error sum, a difference square sum, an average absolute value, or an average square error as the evaluation reference value.
Preferably, a part of the compensation schemes are eliminated according to the random number, and the scheme with the smallest evaluation value in the rest of the compensation schemes is regarded as the optimal compensation scheme.
Preferably, the random number is generated by a linear feedback shift register or a chaotic system.
Preferably, the pixels of the different color channels of the image block are sequentially calculated to obtain the final image block. The calculation formula is as follows:
Pnew＝CLIP(Pold+Offset(best)，MIN，MAX)
where Pold and Pnew are the pixel values of the image block before the processing and the pixel values of the new image block obtained after the processing, respectively. CLIP is an upper and lower boundary constraint function that prevents the computation from exceeding the pixel value range. MIN and MAX are upper and lower boundary values, respectively, for pixel values of the image block.
The invention combines the random probability with the continuity of the image, effectively improves the problem of image blurring detail loss caused by the conventional deblocking algorithm, and is more in line with the cognition and perception of people on the image. Especially, the application scenes with high image definition are required at low code rate, such as video platforms, wireless image transmission and the like.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to the structures shown in these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of the steps of the present invention;
FIG. 2 is a schematic diagram of the calculation process of the compensation scheme evaluation value in the present invention;
FIG. 3 is a schematic diagram of a linear feedback shift register according to the present invention;
FIG. 4 is a diagram illustrating a complexity determination process according to the present invention;
the achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
The image deblocking algorithm based on random probability provided in this embodiment includes the following steps, as shown in fig. 1:
s1, dividing a decoded image into M-N image blocks which are not overlapped with each other according to the parameter setting of a coder-decoder;
s2, calculating evaluation values of various compensation schemes for the image blocks according to the image continuity;
s3, determining an optimal compensation scheme by combining the random number and the evaluation value;
s4, generating a final image block according to the optimal compensation scheme.
Specifically, N and M are each an integer multiple of 2 and 6 or less. The setting may be made according to the size of the smallest block in the image codec, but the optimal setting is that N and M are integer multiples of 2 and cannot be too large, preferably not more than 4. Otherwise, the adverse effect, i.e., the post-processing blocking effect, may be more pronounced.
Specifically, the specific steps of S2 are: and taking upper boundary pixels and left boundary pixels in the image block to respectively perform DC compensation under W conditions, and calculating reference pixels of the image block adjacent to the image block as a result to respectively obtain evaluation values of W DC compensation schemes. W should be an odd number greater than 1, the compensation value being an integer and the overall distribution being symmetrical about the origin. I.e., offset (1+n) +offset (W-n) =0, where n is an integer greater than or equal to 0 and less than W, where Offset (x) represents the DC compensation coefficient corresponding to the x-th compensation scheme.
Specifically, DC compensation is performed by taking the left boundary and the upper boundary in the image block, and the absolute sum of the difference is calculated by calculating with the boundary outside the image block to obtain an evaluation value Wherein P is in (i) Pixel value representing left or upper boundary in block, P out (i) Substitution ofThe pixel value of the left or upper boundary outside the table block.
In particular, the reference pixels should be from an image block before being processed.
Specifically, the evaluation value adopts an absolute error sum, a difference square sum, an average absolute value, or an average square error as an evaluation reference value.
Specifically, a part of compensation schemes are eliminated according to the random number, and the scheme with the smallest evaluation value in the rest compensation schemes is regarded as the optimal compensation scheme. The random number is generated by adopting a linear feedback shift register or a chaotic system. Firstly, screening out a part of schemes according to random numbers, wherein an LFSR linear feedback shift register is adopted for generating the random numbers, W bits are taken out from the random numbers to serve as enabling signals of W compensation schemes, if the enabling bit 1 is enabled, the scheme is effective, otherwise, the scheme is ineffective. And secondly, searching a compensation scheme with the minimum SAD (evaluation value) for the residual compensation scheme, namely, the optimal compensation scheme. Specifically, when no compensation scheme has remained after the random screening, a scheme with an Offset value of 0 is directly selected as the optimal scheme. The evaluation values of the different compensation schemes are calculated in order to provide a reference when determining the optimal compensation scheme.
And synthesizing a final image by adopting an optimal compensation scheme, sequentially performing DC compensation on each pixel of the image block, and storing the final pixels into a buffer memory for waiting for display.
Specifically, pixels of different color channels of the image block are sequentially calculated to obtain a final image block. The calculation formula is as follows:
Pnew＝CLIP(Pold+Offset(best)，MIN，MAX)
where Pold and Pnew are the pixel values of the image block before the processing and the pixel values of the new image block obtained after the processing, respectively. CLIP is an upper and lower boundary constraint function that prevents the computation from exceeding the pixel value range. MIN and MAX are upper and lower boundary values, respectively, for pixel values of the image block.
The step 1 is to divide the decoded image into non-overlapping image blocks with the size of m×n according to the codec parameter setting. In this embodiment, the minimum coding block size of the codec is 8×8, and M and N are both 2. In the remaining steps, 2x2 image blocks are processed.
In step 2, for the current 2x2 image block, in the embodiment, w=5, offset (1) = -2, offset (2) = -1, offset (3) =0, offset (4) =1, and offset (5) =2. And taking pixel values of the left boundary and the upper boundary in the image block, respectively calculating the pixel values after 5 DC compensation, and then calculating the sum of absolute errors of adjacent pixels outside the image block to obtain SAD (1) -SAD (5).
In step 3, the random number is generated by using a linear feedback shift register method, as shown in fig. 3, and the polynomial formula is x 8 -x 2 +x+1, with an initial value of 0. The random number is updated once every time the image block is switched. The random number has 8 valid bits in total, and the lower 5 bits are taken as the enabling signals of 5 compensation schemes. When the enabling signal is 0, the corresponding compensation scheme is eliminated. If all compensation schemes are eliminated after random screening, then Offset (best) =0, ending screening; otherwise, searching SAD (min) in the residual compensation scheme, wherein the corresponding compensation scheme is the optimal scheme, and the value of the Offset (best) is the value of the Offset corresponding to the optimal scheme.
Wherein step 4 DC compensates the current image block. Traversing each pixel of the image block, compensating each pixel, and superimposing Offset
In this embodiment, the video before encoding is adaptively processed by using different texture characteristics, so that image texture details can be well protected, and image blocking effects can be removed. In post-processing applications of image codec, the human visual perception can be improved to a large extent.
Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by a computer program and related hardware, where the program may be stored on a computer readable storage medium, and the program may include processes in the embodiments of the methods described above when executed. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), or the like.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (7)
1. An image deblocking algorithm based on random probability, comprising the steps of:
s1, dividing a decoded image into M-N image blocks which are not overlapped with each other according to the parameter setting of a coder-decoder;
s2, calculating evaluation values of various compensation schemes for the image blocks according to the image continuity;
s3, determining an optimal compensation scheme by combining the random number and the evaluation value;
s4, generating a final image block according to the optimal compensation scheme;
the specific steps of S2 are as follows: taking upper boundary pixels and left boundary pixels in an image block to respectively perform DC compensation of W cases, and calculating reference pixels of the image block with the result adjacent to the image block to respectively obtain evaluation values of W DC compensation schemes; w is an odd number greater than 1, the compensation value is an integer and the whole distribution is symmetrical about the origin; namely, offset (1+n) +offset (W-n) =0, wherein n is an integer greater than or equal to 0 and less than W, wherein Offset (x) represents a DC compensation coefficient corresponding to the x-th compensation scheme;
and S3, eliminating a part of compensation schemes according to the random number, wherein the scheme with the smallest evaluation value in the rest compensation schemes is the optimal compensation scheme.
2. The image deblocking algorithm based on random probabilities of claim 1, wherein N and M are each an integer multiple of 2 and less than or equal to 6.
3. The image deblocking algorithm based on random probabilities of claim 2, wherein left and upper boundaries within an image block are taken for DC interpolationCompensating, and calculating absolute sum of difference with boundary outside image block to obtain evaluation valueWherein P is in (i) Pixel values representing the left or upper boundary within a block, P out (i) Representing the pixel value of the left or upper boundary outside the block.
4. A random probability based image deblocking algorithm according to claim 3, wherein the reference pixels are from image blocks prior to processing.
5. The image deblocking algorithm based on random probabilities of claim 4, wherein the evaluation value employs an absolute error sum, a difference sum of squares, an average absolute value, or an average square error as an evaluation reference value.
6. The image deblocking algorithm based on random probabilities of claim 5, wherein the random numbers are generated by a linear feedback shift register or a chaotic system.
7. The image deblocking algorithm based on random probabilities of claim 6, wherein pixels of different color channels of an image block are sequentially computed to obtain a final image block; the calculation formula is as follows:
Pnew＝CLIP(Pold+Offset(best)，MIN，MAX)
wherein, pold and Pnew are the pixel value of the unprocessed image block and the pixel value of the new image block obtained after processing respectively; the CLIP is an upper and lower boundary limiting function and is used for preventing the calculation result from exceeding the pixel value range; MIN and MAX are upper and lower boundary values of pixel values of the image block respectively; offset (best) is a coefficient corresponding to the optimal compensation scheme.
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