Block noise parameter determination method and device based on autocorrelation function

Abstract

The invention relates to the technical field of image denoising, and discloses a block noise parameter determination method and device based on an autocorrelation function, and the method comprises the steps: carrying out the gradient accumulation operation of each pixel based on a target gradient parameter corresponding to each pixel in a target image, and obtaining a target gradient accumulated value corresponding to each pixel; based on the target gradient accumulated value corresponding to each pixel, performing an autocorrelation calculation operation on each pixel to obtain a target autocorrelation parameter corresponding to each pixel so as to determine a block noise intensity parameter of the target image; and determining a block noise boundary size parameter of the target image according to the block noise intensity parameter and the block noise boundary size parameter, and determining a block noise parameter of the target image according to the block noise intensity parameter and the block noise boundary size parameter, so that the block noise parameter of the target image is determined by analyzing the block noise level of each pixel. The method improves the determination reliability and accuracy of the block noise parameter of the target image, facilitates the improvement of the subsequent denoising effect of the target image, and meets the impression experience of audiences.

Description

Technical Field

[0001] The present invention relates to the technical field of image denoising, and in particular to a method and device for determining block noise parameters based on an autocorrelation function. Background Art

[0002] In order to reduce the amount of data required to store or transmit images, images are usually compressed. If a higher compression rate and lower bit rate are required, the quantization accuracy of the image needs to be reduced. In this case, the image will have a large quantization error, which is manifested as discontinuous boundaries between adjacent blocks. This unnatural block pattern is called block noise.

[0003] In order to eliminate block noise, it is generally detected and the corresponding block noise parameters are determined, and then the image is filtered through a low-pass filter to reduce the visual impact of block noise on the audience. However, it is found through practice that the existing block noise parameters are usually determined for the entire picture, but in fact, the block noise level of each pixel is different. If the block noise parameters for the entire picture are used to configure the filter strength, then the filter strength will be insufficient in places where the block noise is strong, thereby affecting the denoising effect of the image. It can be seen that it is particularly important to provide a method that can improve the accuracy of determining the block noise parameters of the image. Summary of the invention

[0004] The present invention provides a method and device for determining block noise parameters based on an autocorrelation function, which improves the reliability and accuracy of determining the block noise parameters of a target image, and is beneficial to improving the subsequent denoising effect on the target image to satisfy the viewing experience of the audience.

[0005] In order to solve the above technical problem, the first aspect of the present invention discloses a method for determining block noise parameters based on an autocorrelation function, the method comprising:

[0006] Determine a target gradient parameter corresponding to each pixel in the target image to be detected, and perform a gradient accumulation operation on each pixel based on the target gradient parameter corresponding to each pixel to obtain a target gradient accumulation value corresponding to each pixel; the target gradient parameter includes a horizontal gradient parameter and a vertical gradient parameter, and the target gradient accumulation value includes a horizontal gradient accumulation value and a vertical gradient accumulation value;

[0007] Based on the target gradient accumulated value corresponding to each pixel, an autocorrelation calculation operation is performed on each pixel to obtain a target autocorrelation parameter corresponding to each pixel, and a block noise intensity parameter of the target image is determined according to the target autocorrelation parameters corresponding to all the pixels; the target autocorrelation parameters include a horizontal gradient autocorrelation parameter and a vertical gradient autocorrelation parameter;

[0008] A block noise boundary size parameter of the target image is determined, and a block noise parameter of the target image is determined according to the block noise intensity parameter and the block noise boundary size parameter.

[0009] As an optional implementation, in the first aspect of the present invention, the target gradient parameter corresponding to each pixel is determined by the following formula:

[0010] gradHoz(x,y)_trans=Y(x+1,y)+Y(x-1,y)-2Y(x,y);

[0011] gradVer(x,y)_trans=Y(x,y+1)+Y(x,y-1)-2Y(x,y);

[0012]

[0013] Among them, gradHoz(x,y) is the horizontal gradient parameter corresponding to the pixel (x,y) in the target image, gradVer(x,y) is the vertical gradient parameter corresponding to the pixel (x,y) in the target image, Y is the brightness channel parameter of the target image, gradTh1 is the preset horizontal gradient threshold corresponding to the pixel (x,y), and gradTh2 is the preset vertical gradient threshold corresponding to the pixel (x,y).

[0014] As an optional implementation manner, in the first aspect of the present invention, before performing a gradient accumulation operation on each pixel based on the target gradient parameter corresponding to each pixel to obtain a target gradient accumulation value corresponding to each pixel, the method further includes:

[0015] For each of the pixels, determining whether there is a gradient parameter to be set to zero in the target gradient parameters corresponding to the pixel, and if so, determining the pixel as a target pixel;

[0016] Performing a zeroing operation on the to-be-zeroed gradient parameters corresponding to all the target pixels to obtain the zeroed gradient parameters corresponding to all the target pixels;

[0017] According to the zeroed gradient parameters corresponding to all the target pixels, the target gradient parameters corresponding to all the pixels are updated, and the operation of performing a gradient accumulation operation on each pixel based on the target gradient parameter corresponding to each pixel to obtain a target gradient accumulation value corresponding to each pixel is triggered.

[0018] As an optional implementation manner, in the first aspect of the present invention, the determining whether there is a gradient parameter to be set to zero in the target gradient parameter corresponding to the pixel, includes:

[0019] Determine whether the horizontal gradient parameter corresponding to the pixel satisfies a preset horizontal gradient continuity comparison relationship, and determine whether the vertical gradient parameter corresponding to the pixel satisfies a preset vertical gradient continuity comparison relationship; the horizontal gradient continuity comparison relationship includes a first relationship, a second relationship, and a third relationship, and the vertical gradient continuity comparison relationship includes a fourth relationship, a fifth relationship, and a sixth relationship;

[0020] When it is determined that the horizontal gradient parameter corresponding to the pixel does not satisfy the horizontal gradient continuity comparison relationship and / or the vertical gradient parameter corresponding to the pixel does not satisfy the vertical gradient continuity comparison relationship, determine that there is a gradient parameter to be set to zero in the target gradient parameter corresponding to the pixel;

[0021] Among them, the first relationship is: |gradHoz(x,y)-gradHoz(x,y - 1)| < continuityTh_Hoz1;

[0022] The second relationship is: |gradHoz(x,y)-gradHoz(x,y + 1)| < continuityTh_Hoz2;

[0023] The third relationship is: |gradHoz(x,y)-gradHoz(x,y - 2)| < continuityTh_Hoz3, or

[0024] |gradHoz(x,y)-gradHoz(x,y + 2)| < continuityTh_Hoz4;

[0025] continuityTh_Hoz1 is a preset first horizontal gradient continuity threshold, continuityTh_Hoz2 is a preset second horizontal gradient continuity threshold, continuityTh_Hoz3 is a preset third horizontal gradient continuity threshold, and continuityTh_Hoz4 is a preset fourth horizontal gradient continuity threshold;

[0026] Among them, the fourth relationship is: |gradVer(x,y)-gradVer(x - 1,y)| < continuityTh_Ver1;

[0027] The fifth relationship is: |gradVer(x,y)-gradVer(x + 1,y)| < continuityTh_Ver2;

[0028] The sixth relationship is: |gradVer(x,y)-gradVer(x - 2,y)| < continuityTh_Ver3, or

[0029] |gradVer(x,y)-gradVer(x+2,y)| <continuityTh_Ver4;

[0030] continuityTh_Ver1 is a preset first vertical gradient continuity threshold, continuityTh_Ver2 is a preset second vertical gradient continuity threshold, continuityTh_Ver3 is a preset third vertical gradient continuity threshold, and continuityTh_Ver4 is a preset fourth vertical gradient continuity threshold.

[0031] As an optional implementation, in the first aspect of the present invention, the horizontal gradient accumulation value corresponding to each pixel is calculated by the following formula:

[0032]

[0033] Wherein, gradAccHoz(x) ​​is the accumulated value of the horizontal gradient corresponding to the pixel (x, y), height is the height parameter of the target image, and i is the index parameter associated with the height parameter;

[0034] And, the vertical gradient accumulation value corresponding to each pixel is calculated by the following formula:

[0035]

[0036] Among them, gradAccVer(y) is the vertical gradient accumulation value corresponding to the pixel (x, y), width is the width parameter of the target image, and j is the index parameter associated with the width parameter.

[0037] As an optional implementation, in the first aspect of the present invention, the horizontal gradient autocorrelation parameter corresponding to each pixel is calculated by the following formula:

[0038]

[0039] Wherein, corrHoz(x, s1) is the horizontal gradient autocorrelation parameter corresponding to the pixel (x, y), r1 and -r1 are respectively the signal upper limit parameter and the signal lower limit parameter in the preset first signal parameter range of the pixel, m is the index parameter associated with the first signal parameter range, s1 is the first translation variable parameter of the pixel, and s1 is in the preset first translation range;

[0040] And, the vertical gradient autocorrelation parameter corresponding to each pixel is calculated by the following formula:

[0041]

[0042] Among them, corrVer(y,s2) is the vertical gradient autocorrelation parameter corresponding to the pixel (x,y), r2 and -r2 are respectively the signal upper limit parameter and the signal lower limit parameter in the preset second signal parameter range of the pixel, n is the index parameter associated with the second signal parameter range, s2 is the second translation variable parameter of the pixel, and s2 is in the preset second translation range.

[0043] As an optional implementation manner, in the first aspect of the present invention, determining the block noise intensity parameter of the target image according to the target autocorrelation parameters corresponding to all the pixels includes:

[0044] For each pixel, according to the horizontal gradient autocorrelation parameter corresponding to the pixel, the horizontal average gradient autocorrelation parameter corresponding to the pixel is calculated, and according to the vertical gradient autocorrelation parameter corresponding to the pixel, the average vertical gradient autocorrelation parameter corresponding to the pixel is calculated;

[0045] For each of the pixels, determining a horizontal block noise intensity parameter corresponding to the pixel according to an average horizontal gradient autocorrelation parameter corresponding to the pixel and a maximum horizontal gradient autocorrelation parameter among the horizontal gradient autocorrelation parameters corresponding to the pixel, and determining a vertical block noise intensity parameter corresponding to the pixel according to an average vertical gradient autocorrelation parameter corresponding to the pixel and a maximum vertical gradient autocorrelation parameter among the vertical gradient autocorrelation parameters corresponding to the pixel;

[0046] Determine a target block noise intensity parameter corresponding to each pixel according to the horizontal block noise intensity parameter and the vertical block noise intensity parameter corresponding to each pixel, and determine the block noise intensity parameter of the target image according to the target block noise intensity parameters corresponding to all the pixels;

[0047] Among them, the horizontal horizontal gradient autocorrelation parameter corresponding to the pixel is:

[0048]

[0049] (u1, u2) is the first translation range, corrHozMax(x) is the maximum horizontal gradient autocorrelation parameter corresponding to the pixel (x, y);

[0050] The average vertical gradient autocorrelation parameter corresponding to the pixel is:

[0051]

[0052] (v1, v2) is the second translation range, corrVerMax(y) is the maximum vertical gradient autocorrelation parameter corresponding to the pixel (x, y);

[0053] Among them, the horizontal block noise intensity parameter corresponding to the pixel is:

[0054] blkNoiseStrHoz(x)=corrHozMax(x) / corrHozMean(x);

[0055] The vertical block noise intensity parameter corresponding to the pixel is:

[0056] blkNoiseStrVer(y)=corrVerMax(y) / corrVerMean(y).

[0057] As an optional implementation manner, in the first aspect of the present invention, determining the block noise boundary size parameter of the target image includes:

[0058] For each pixel, determine, from the first translation range corresponding to the pixel, a first translation variable parameter corresponding to when the horizontal gradient autocorrelation parameter corresponding to the pixel is the maximum horizontal gradient autocorrelation parameter, as the first target translation parameter corresponding to the pixel, and determine, from the second translation range corresponding to the pixel, a second translation variable parameter corresponding to when the vertical gradient autocorrelation parameter corresponding to the pixel is the maximum vertical gradient autocorrelation parameter, as the second target translation parameter corresponding to the pixel;

[0059] Determine the first target translation parameter with the highest occurrence frequency from among the first target translation parameters corresponding to all the pixels as the target translation parameter in the horizontal direction of the target image, and determine the second target translation parameter with the highest occurrence frequency from among the second target translation parameters corresponding to all the pixels as the target translation parameter in the vertical direction of the target image;

[0060] A block noise boundary size parameter of the target image is determined according to the horizontal target translation parameter and the vertical target translation parameter.

[0061] A second aspect of the present invention discloses a device for determining block noise parameters based on an autocorrelation function, the device comprising:

[0062] A determination module, used to determine a target gradient parameter corresponding to each pixel in the target image to be detected;

[0063] A gradient accumulation module, configured to perform a gradient accumulation operation on each pixel based on a target gradient parameter corresponding to each pixel, so as to obtain a target gradient accumulation value corresponding to each pixel; the target gradient parameter includes a horizontal gradient parameter and a vertical gradient parameter, and the target gradient accumulation value includes a horizontal gradient accumulation value and a vertical gradient accumulation value;

[0064] An autocorrelation calculation module is used to perform an autocorrelation calculation operation on each pixel based on the target gradient accumulation value corresponding to each pixel, so as to obtain a target autocorrelation parameter corresponding to each pixel; the target autocorrelation parameter includes a horizontal gradient autocorrelation parameter and a vertical gradient autocorrelation parameter;

[0065] The determination module is also used to determine the block noise intensity parameter of the target image according to the target autocorrelation parameters corresponding to all the pixels; determine the block noise boundary size parameter of the target image, and determine the block noise parameter of the target image according to the block noise intensity parameter and the block noise boundary size parameter.

[0066] As an optional implementation, in the second aspect of the present invention, the target gradient parameter corresponding to each pixel is determined by the following formula:

[0067] gradHoz(x,y)_trans=Y(x+1,y)+Y(x-1,y)-2Y(x,y);

[0068] gradVer(x,y)_trans=Y(x,y+1)+Y(x,y-1)-2Y(x,y);

[0069]

[0070] Among them, gradHoz(x,y) is the horizontal gradient parameter corresponding to the pixel (x,y) in the target image, gradVer(x,y) is the vertical gradient parameter corresponding to the pixel (x,y) in the target image, Y is the brightness channel parameter of the target image, gradTh1 is the preset horizontal gradient threshold corresponding to the pixel (x,y), and gradTh2 is the preset vertical gradient threshold corresponding to the pixel (x,y).

[0071] As an optional implementation, in the second aspect of the present invention, the device further includes:

[0072] A judgment module, configured to, before the gradient accumulation module performs a gradient accumulation operation on each pixel based on the target gradient parameter corresponding to each pixel to obtain a target gradient accumulation value corresponding to each pixel, for each pixel, judge whether there is a to-be-zeroed gradient parameter to be zeroed in the target gradient parameter corresponding to the pixel, and if so, determine the pixel as a target pixel;

[0073] A zeroing module, configured to perform a zeroing operation on the to-be-zeroed gradient parameters corresponding to all the target pixels to obtain zeroed gradient parameters corresponding to all the target pixels;

[0074] An update module, configured to update the target gradient parameters corresponding to all the pixels according to the zeroed gradient parameters corresponding to all the target pixels, and trigger the gradient accumulation module to perform the operation of performing a gradient accumulation operation on each pixel based on the target gradient parameter corresponding to each pixel to obtain a target gradient accumulation value corresponding to each pixel.

[0075] As an optional implementation manner, in the second aspect of the present invention, the manner in which the judgment module judges whether there is a to-be-zeroed gradient parameter to be zeroed in the target gradient parameter corresponding to the pixel specifically includes:

[0076] Judging whether the horizontal gradient parameter corresponding to the pixel satisfies a preset horizontal gradient continuity comparison relationship, and judging whether the vertical gradient parameter corresponding to the pixel satisfies a preset vertical gradient continuity comparison relationship; the horizontal gradient continuity comparison relationship includes a first relationship, a second relationship, and a third relationship, and the vertical gradient continuity comparison relationship includes a fourth relationship, a fifth relationship, and a sixth relationship;

[0077] When it is judged that the horizontal gradient parameter corresponding to the pixel does not satisfy the horizontal gradient continuity comparison relationship and / or the vertical gradient parameter corresponding to the pixel does not satisfy the vertical gradient continuity comparison relationship, it is determined that there is a to-be-zeroed gradient parameter to be zeroed in the target gradient parameter corresponding to the pixel;

[0078] Wherein, the first relationship is: |gradHoz(x,y)-gradHoz(x,y-1)|<continuityTh_Hoz1;

[0079] The second relationship is: |gradHoz(x,y)-gradHoz(x,y+1)|<continuityTh_Hoz2;

[0080] The third relationship is: |gradHoz(x,y)-gradHoz(x,y-2)|<continuityTh_Hoz3, or

[0081] |gradHoz(x,y) - gradHoz(x,y + 2)| < continuityTh_Hoz4;

[0082] continuityTh_Hoz1 is a preset first horizontal gradient continuity threshold, continuityTh_Hoz2 is a preset second horizontal gradient continuity threshold, continuityTh_Hoz3 is a preset third horizontal gradient continuity threshold, and continuityTh_Hoz4 is a preset fourth horizontal gradient continuity threshold;

[0083] Among them, the fourth relationship is: |gradVer(x,y) - gradVer(x - 1,y)| < continuityTh_Ver1;

[0084] The fifth relationship is: |gradVer(x,y) - gradVer(x + 1,y)| < continuityTh_Ver2;

[0085] The sixth relationship is: |gradVer(x,y) - gradVer(x - 2,y)| < continuityTh_Ver3, or

[0086] |gradVer(x,y) - gradVer(x + 2,y)| < continuityTh_Ver4;

[0087] continuityTh_Ver1 is a preset first vertical gradient continuity threshold, continuityTh_Ver2 is a preset second vertical gradient continuity threshold, continuityTh_Ver3 is a preset third vertical gradient continuity threshold, and continuityTh_Ver4 is a preset fourth vertical gradient continuity threshold.

[0088] As an optional implementation manner, in the second aspect of the present invention, the horizontal gradient accumulation value corresponding to each pixel is calculated by the following formula:

[0089]

[0090] Among them, gradAccHoz(x) is the horizontal gradient accumulation value corresponding to the pixel (x,y), height is the height parameter of the target image, and i is an index parameter associated with the height parameter;

[0091] And, the vertical gradient accumulation value corresponding to each pixel is calculated by the following formula:

[0092]

[0093] Among them, gradAccVer(y) is the vertical gradient accumulation value corresponding to the pixel (x, y), width is the width parameter of the target image, and j is the index parameter associated with the width parameter.

[0094] As an optional implementation, in the second aspect of the present invention, the horizontal gradient autocorrelation parameter corresponding to each pixel is calculated by the following formula:

[0095]

[0096] Wherein, corrHoz(x, s1) is the horizontal gradient autocorrelation parameter corresponding to the pixel (x, y), r1 and -r1 are respectively the signal upper limit parameter and the signal lower limit parameter in the preset first signal parameter range of the pixel, m is the index parameter associated with the first signal parameter range, s1 is the first translation variable parameter of the pixel, and s1 is in the preset first translation range;

[0097] And, the vertical gradient autocorrelation parameter corresponding to each pixel is calculated by the following formula:

[0098]

[0099] Among them, corrVer(y,s2) is the vertical gradient autocorrelation parameter corresponding to the pixel (x,y), r2 and -r2 are respectively the signal upper limit parameter and the signal lower limit parameter in the preset second signal parameter range of the pixel, n is the index parameter associated with the second signal parameter range, s2 is the second translation variable parameter of the pixel, and s2 is in the preset second translation range.

[0100] As an optional implementation, in the second aspect of the present invention, the determination module determines the block noise intensity parameter of the target image according to the target autocorrelation parameters corresponding to all the pixels in a manner specifically including:

[0101] For each pixel, according to the horizontal gradient autocorrelation parameter corresponding to the pixel, the horizontal average gradient autocorrelation parameter corresponding to the pixel is calculated, and according to the vertical gradient autocorrelation parameter corresponding to the pixel, the average vertical gradient autocorrelation parameter corresponding to the pixel is calculated;

[0102] For each of the pixels, determining a horizontal block noise intensity parameter corresponding to the pixel according to an average horizontal gradient autocorrelation parameter corresponding to the pixel and a maximum horizontal gradient autocorrelation parameter among the horizontal gradient autocorrelation parameters corresponding to the pixel, and determining a vertical block noise intensity parameter corresponding to the pixel according to an average vertical gradient autocorrelation parameter corresponding to the pixel and a maximum vertical gradient autocorrelation parameter among the vertical gradient autocorrelation parameters corresponding to the pixel;

[0103] Determine a target block noise intensity parameter corresponding to each pixel according to the horizontal block noise intensity parameter and the vertical block noise intensity parameter corresponding to each pixel, and determine the block noise intensity parameter of the target image according to the target block noise intensity parameters corresponding to all the pixels;

[0104] Among them, the horizontal horizontal gradient autocorrelation parameter corresponding to the pixel is:

[0105]

[0106] (u1, u2) is the first translation range, corrHozMax(x) is the maximum horizontal gradient autocorrelation parameter corresponding to the pixel (x, y);

[0107] The average vertical gradient autocorrelation parameter corresponding to the pixel is:

[0108]

[0109] (v1, v2) is the second translation range, corrVerMax(y) is the maximum vertical gradient autocorrelation parameter corresponding to the pixel (x, y);

[0110] Among them, the horizontal block noise intensity parameter corresponding to the pixel is:

[0111] blkNoiseStrHoz(x)=corrHozMax(x) / corrHozMean(x);

[0112] The vertical block noise intensity parameter corresponding to the pixel is:

[0113] blkNoiseStrVer(y)=corrVerMax(y) / corrVerMean(y).

[0114] As an optional implementation, in the second aspect of the present invention, the manner in which the determination module determines the block noise boundary size parameter of the target image specifically includes:

[0115] For each pixel, determine, from the first translation range corresponding to the pixel, a first translation variable parameter corresponding to when the horizontal gradient autocorrelation parameter corresponding to the pixel is the maximum horizontal gradient autocorrelation parameter, as the first target translation parameter corresponding to the pixel, and determine, from the second translation range corresponding to the pixel, a second translation variable parameter corresponding to when the vertical gradient autocorrelation parameter corresponding to the pixel is the maximum vertical gradient autocorrelation parameter, as the second target translation parameter corresponding to the pixel;

[0116] Determine the first target translation parameter with the highest occurrence frequency from among the first target translation parameters corresponding to all the pixels as the target translation parameter in the horizontal direction of the target image, and determine the second target translation parameter with the highest occurrence frequency from among the second target translation parameters corresponding to all the pixels as the target translation parameter in the vertical direction of the target image;

[0117] A block noise boundary size parameter of the target image is determined according to the horizontal target translation parameter and the vertical target translation parameter.

[0118] The third aspect of the present invention discloses another device for determining block noise parameters based on an autocorrelation function, the device comprising:

[0119] A memory storing executable program code;

[0120] a processor coupled to the memory;

[0121] The processor calls the executable program code stored in the memory to execute the block noise parameter determination method based on the autocorrelation function disclosed in the first aspect of the present invention.

[0122] The fourth aspect of the present invention discloses a computer storage medium, wherein the computer storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the block noise parameter determination method based on the autocorrelation function disclosed in the first aspect of the present invention.

[0123] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0124] In the embodiment of the present invention, based on the target gradient parameter corresponding to each pixel in the target image, a gradient accumulation operation is performed on each pixel to obtain a target gradient accumulation value corresponding to each pixel; based on the target gradient accumulation value corresponding to each pixel, an autocorrelation calculation operation is performed on each pixel to obtain a target autocorrelation parameter corresponding to each pixel to determine a block noise intensity parameter of the target image; a block noise boundary size parameter of the target image is determined, and the block noise parameter of the target image is determined according to the block noise intensity parameter and the block noise boundary size parameter. In this way, the block noise parameter of the target image is determined by analyzing the block noise level of each pixel, thereby improving the reliability and accuracy of determining the block noise parameter of the target image, which is beneficial to improving the subsequent denoising effect on the target image to meet the viewing experience of the audience. BRIEF DESCRIPTION OF THE DRAWINGS

[0125] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. 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 creative work.

[0126] Figure 1 It is a flow chart of a method for determining block noise parameters based on an autocorrelation function disclosed in an embodiment of the present invention;

[0127] Figure 2 It is a flow chart of another method for determining block noise parameters based on autocorrelation function disclosed in an embodiment of the present invention;

[0128] Figure 3 It is a structural schematic diagram of a block noise parameter determination device based on an autocorrelation function disclosed in an embodiment of the present invention;

[0129] Figure 4 It is a structural schematic diagram of another device for determining block noise parameters based on autocorrelation function disclosed in an embodiment of the present invention;

[0130] Figure 5 It is a structural schematic diagram of another device for determining block noise parameters based on autocorrelation function disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0131] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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.

[0132] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or end including a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units inherent to these processes, methods, products or ends.

[0133] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0134] The present invention discloses a method and device for determining block noise parameters based on an autocorrelation function, which improves the reliability and accuracy of determining the block noise parameters of a target image, and is beneficial to improving the subsequent denoising effect on the target image to satisfy the viewing experience of the audience.

[0135] Embodiment 1

[0136] See also Figure 1 , Figure 1 is a flow chart of a method for determining block noise parameters based on an autocorrelation function disclosed in an embodiment of the present invention. Figure 1 The described block noise parameter determination method based on the autocorrelation function can be applied to perform block noise parameter determination operations on display images (such as video images, static images, etc.) of various types of display devices, such as televisions, smart computers, smart phones, tablets, etc., and the embodiments of the present invention do not limit this. Optionally, the method can be implemented by a block noise parameter determination device, and the block noise parameter determination device can be integrated into the aforementioned display device. When the block noise parameter determination device exists independently, it can also be a local server or cloud server for processing the block noise parameter determination process of the display image, and the embodiments of the present invention do not limit this. Figure 1 As shown, the block noise parameter determination method based on the autocorrelation function may include the following operations:

[0137] 101. Determine a target gradient parameter corresponding to each pixel in the target image to be detected, and based on the target gradient parameter corresponding to each pixel, perform a gradient accumulation operation on each pixel to obtain a target gradient accumulation value corresponding to each pixel.

[0138] In the embodiment of the present invention, the target gradient parameter includes a horizontal gradient parameter and a vertical gradient parameter, and the target gradient accumulated value includes a horizontal gradient accumulated value and a vertical gradient accumulated value.

[0139] Furthermore, the target gradient parameter corresponding to each pixel is determined by the following formula:

[0140] gradHoz(x,y)_trans=Y(x+1,y)+Y(x-1,y)-2Y(x,y);

[0141] gradVer(x,y)_trans=Y(x,y+1)+Y(x,y-1)-2Y(x,y);

[0142]

[0143] Among them, gradHoz(x,y) is the horizontal gradient parameter corresponding to the pixel (x,y) in the target image, gradVer(x,y) is the vertical gradient parameter corresponding to the pixel (x,y) in the target image, Y is the brightness channel parameter of the target image, gradTh1 is the preset horizontal gradient threshold corresponding to the pixel (x,y), and gradTh2 is the preset vertical gradient threshold corresponding to the pixel (x,y). It should be noted that this gradient zeroing method can reduce the influence of the real edge in the process of determining the block noise parameters.

[0144] Optionally, the gradient type of the target gradient parameter corresponding to each pixel can be a first-order gradient type or a second-order gradient type, and the specific gradient type can be determined based on the denoising rate requirement, denoising accuracy requirement, denoising type requirement, etc. of the target image. Further optionally, the gradTh1 and gradTh2 can be determined based on the denoising accuracy requirement, denoising type requirement, and audience visual requirements of the target image.

[0145] In the embodiment of the present invention, further, the horizontal gradient accumulation value corresponding to each pixel is calculated by the following formula:

[0146]

[0147] Among them, gradAccHoz(x) ​​is the accumulated value of the horizontal gradient corresponding to the pixel (x, y), height is the height parameter of the target image, and i is the index parameter associated with the height parameter;

[0148] And, the vertical gradient accumulation value corresponding to each pixel is calculated by the following formula:

[0149]

[0150] Among them, gradAccVer(y) is the vertical gradient accumulation value corresponding to the pixel (x, y), width is the width parameter of the target image, and j is the index parameter associated with the width parameter.

[0151] That is, gradAccHoz(x) ​​is a one-dimensional array whose length is equal to the width parameter of the target image, and gradAccVer(y) is a one-dimensional array whose length is equal to the height parameter of the target image.

[0152] 102. Based on the target gradient accumulation value corresponding to each pixel, an autocorrelation calculation operation is performed on each pixel to obtain a target autocorrelation parameter corresponding to each pixel, and a block noise intensity parameter of the target image is determined according to the target autocorrelation parameters corresponding to all pixels.

[0153] In the embodiment of the present invention, if block noise appears in the target image, then periodic peak patterns will appear in the gradient accumulation values ​​gradAccHoz(x) ​​and gradAccVer(y). According to these patterns, the present invention uses an autocorrelation function to calculate the size of the block noise, and uses the correlation size to determine the intensity of the block noise. Among them, the target autocorrelation parameters include horizontal gradient autocorrelation parameters and vertical gradient autocorrelation parameters.

[0154] Furthermore, the horizontal gradient autocorrelation parameter corresponding to each pixel is calculated by the following formula:

[0155]

[0156] Among them, corrHoz(x,s1) is the horizontal gradient autocorrelation parameter corresponding to the pixel (x,y), r1 and -r1 are the signal upper limit parameter and signal lower limit parameter in the preset first signal parameter range of the pixel, respectively, m is the index parameter associated with the first signal parameter range, s1 is the first translation variable parameter of the pixel, and s1 is in the preset first translation range.

[0157] And, the vertical gradient autocorrelation parameter corresponding to each pixel is calculated by the following formula:

[0158]

[0159] Among them, corrVer(y,s2) is the vertical gradient autocorrelation parameter corresponding to the pixel (x,y), r2 and -r2 are the signal upper limit parameter and signal lower limit parameter in the preset second signal parameter range of the pixel, n is the index parameter associated with the second signal parameter range, s2 is the second translation variable parameter of the pixel, and s2 is in the preset second translation range.

[0160] Furthermore, the first translation range and the second translation range can be understood as a preset block noise width range and a preset block noise height range, respectively. Optionally, the first translation range and the second translation range can be determined based on the block noise parameters of the historical display image of the corresponding display device, or the image type parameters of the target image, and the target gradient autocorrelation parameter corresponding to each pixel can be calculated by using the difference function in addition to the aforementioned multiplication function. The embodiment of the present invention does not limit the function type used for the target gradient autocorrelation parameter.

[0161] 103. Determine a block noise boundary size parameter of the target image, and determine a block noise parameter of the target image according to the block noise intensity parameter and the block noise boundary size parameter.

[0162] In an embodiment of the present invention, optionally, the block noise boundary size parameters and block noise intensity parameters of the target image can be directly determined as the block noise parameters of the target image, or the block noise boundary size parameters and / or block noise intensity parameters of the target image can be further parameter processed and then the processed block noise boundary size parameters and / or block noise intensity parameters of the target image can be determined as the block noise parameters of the target image.

[0163] It can be seen that the implementation of the embodiment of the present invention can determine the block noise parameters of the target image by analyzing the block noise level of each pixel (i.e., the block noise detection process at the pixel level), thereby improving the reliability and accuracy of determining the block noise parameters of the target image, which is beneficial to improving the subsequent denoising effect on the target image, thereby effectively reducing the block effect in the target image, thereby improving the visual effect of the target image and satisfying the audience's viewing experience.

[0164] Embodiment 2

[0165] See also Figure 2 , Figure 2 FIG. 1 is a flow chart of another method for determining block noise parameters based on an autocorrelation function disclosed in an embodiment of the present invention. Figure 2 The described block noise parameter determination method based on the autocorrelation function can be applied to perform block noise parameter determination operations on display images (such as video images, static images, etc.) of various types of display devices, such as televisions, smart computers, smart phones, tablets, etc., and the embodiments of the present invention do not limit this. Optionally, the method can be implemented by a block noise parameter determination device, and the block noise parameter determination device can be integrated into the aforementioned display device. When the block noise parameter determination device exists independently, it can also be a local server or cloud server for processing the block noise parameter determination process of the display image, and the embodiments of the present invention do not limit this. Figure 2 As shown, the block noise parameter determination method based on the autocorrelation function may include the following operations:

[0166] 201. Determine a target gradient parameter corresponding to each pixel in a target image to be detected.

[0167] 202. For each pixel, determine whether there is a gradient parameter to be set to zero in the target gradient parameters corresponding to the pixel. If yes, determine the pixel as a target pixel.

[0168] In the embodiment of the present invention, determining whether there is a gradient parameter to be set to zero in the target gradient parameter corresponding to the pixel can be understood as determining whether there is a discontinuous gradient in the horizontal / vertical direction of the pixel. If so, the pixel has a horizontal gradient parameter / vertical gradient parameter that needs to be set to zero.

[0169] 203. Perform a zeroing operation on the to-be-zeroed gradient parameters corresponding to all target pixels to obtain the zeroed gradient parameters corresponding to all target pixels.

[0170] In the embodiment of the present invention, by performing a zeroing operation on the to-be-zeroed gradient parameters corresponding to all target pixels, discontinuous gradients of the target pixels in the horizontal or vertical direction can be removed, so as to improve the noise judgment accuracy of the overall pixel.

[0171] 204. Update the target gradient parameters corresponding to all pixels according to the zeroed gradient parameters corresponding to all target pixels, and perform a gradient accumulation operation on each pixel based on the target gradient parameter corresponding to each pixel to obtain a target gradient accumulation value corresponding to each pixel.

[0172] In the embodiment of the present invention, it should be noted that, generally, if only the horizontal / vertical gradient of a single pixel is analyzed, it is difficult to determine whether the pixel is an edge or a block noise boundary. However, by accumulating the updated gradients gradHoz and gradVer by column and row respectively, it is relatively easy to find that the corresponding gradient accumulation values ​​have a periodic pattern.

[0173] 205. Based on the target gradient accumulation value corresponding to each pixel, an autocorrelation calculation operation is performed on each pixel to obtain a target autocorrelation parameter corresponding to each pixel, and a block noise intensity parameter of the target image is determined according to the target autocorrelation parameters corresponding to all pixels.

[0174] 206. Determine a block noise boundary size parameter of the target image, and determine a block noise parameter of the target image according to the block noise intensity parameter and the block noise boundary size parameter.

[0175] In the embodiment of the present invention, for other descriptions of step 201, step 205 and step 206, please refer to the detailed description of step 101 to step 103 in the first embodiment, and the embodiment of the present invention will not be repeated.

[0176] It can be seen that implementing the embodiments of the present invention can perform a zeroing operation on the gradient parameters to be zeroed corresponding to the target pixels in the target image, so as to update the target gradient parameters corresponding to all pixels, and then perform a gradient accumulation operation on all pixels. In this way, it is beneficial to improve the determination reliability and accuracy of the target gradient parameters corresponding to each pixel, and further beneficial to improve the execution reliability, accuracy and efficiency of the subsequent gradient accumulation operation on each pixel, thereby being beneficial to improve the calculation reliability and precision of the target autocorrelation parameters corresponding to each pixel, so as to achieve the precise determination process of the block noise parameters of the target image.

[0177] In an optional embodiment, the determination of whether there are gradient parameters to be zeroed that need to be zeroed among the target gradient parameters corresponding to the pixels in step 202 above includes:

[0178] Determine whether the horizontal gradient parameter corresponding to the pixel satisfies a preset horizontal gradient continuity comparison relationship, and determine whether the vertical gradient parameter corresponding to the pixel satisfies a preset vertical gradient continuity comparison relationship;

[0179] When it is determined that the horizontal gradient parameter corresponding to the pixel does not satisfy the horizontal gradient continuity comparison relationship and / or the vertical gradient parameter corresponding to the pixel does not satisfy the vertical gradient continuity comparison relationship, it is determined that there are gradient parameters to be zeroed that need to be zeroed among the target gradient parameters corresponding to the pixel.

[0180] In this optional embodiment, the horizontal gradient continuity comparison relationship includes a first relationship, a second relationship and a third relationship, and the vertical gradient continuity comparison relationship includes a fourth relationship, a fifth relationship and a sixth relationship. Generally speaking, continuous gradients generally do not differ much in value. When the horizontal gradient parameter satisfies the first relationship, the second relationship and the third relationship at the same time, the horizontal gradient is determined to be continuous. That is, when the horizontal gradient parameter does not satisfy any one of the above three relationships, it can be determined that there is a horizontal gradient parameter (i.e., the gradient parameter to be zeroed) that needs to be zeroed among the target gradient parameters corresponding to the pixel. Among them, the first relationship is: |gradHoz(x,y)-gradHoz(x,y - 1)| < continuityTh_Hoz1;

[0181] The second relationship is: |gradHoz(x,y)-gradHoz(x,y + 1)| < continuityTh_Hoz2;

[0182] The third relationship is: |gradHoz(x,y)-gradHoz(x,y - 2)| < continuityTh_Hoz3, or

[0183] |gradHoz(x,y) - gradHoz(x,y + 2)| < continuityTh_Hoz4;

[0184] continuityTh_Hoz1 is a preset first horizontal gradient continuity threshold, continuityTh_Hoz2 is a preset second horizontal gradient continuity threshold, continuityTh_Hoz3 is a preset third horizontal gradient continuity threshold, and continuityTh_Hoz4 is a preset fourth horizontal gradient continuity threshold.

[0185] Similarly, when the vertical gradient parameters simultaneously satisfy the fourth relationship, the fifth relationship, and the sixth relationship, then the vertical gradient is determined to be continuous. That is, when the vertical gradient parameters do not satisfy any one of the above three relationships, it can be determined that there are vertical gradient parameters to be set to zero (i.e., the gradient parameters to be set to zero) in the target gradient parameters corresponding to the pixel. Among them, the fourth relationship is:

[0186] |gradVer(x,y) - gradVer(x - 1,y)| < continuityTh_Ver1;

[0187] The fifth relationship is: |gradVer(x,y) - gradVer(x + 1,y)| < continuityTh_Ver2;

[0188] The sixth relationship is: |gradVer(x,y) - gradVer(x - 2,y)| < continuityTh_Ver3, or

[0189] |gradVer(x,y) - gradVer(x + 2,y)| < continuityTh_Ver4;

[0190] continuityTh_Ver1 is a preset first vertical gradient continuity threshold, continuityTh_Ver2 is a preset second vertical gradient continuity threshold, continuityTh_Ver3 is a preset third vertical gradient continuity threshold, and continuityTh_Ver4 is a preset fourth vertical gradient continuity threshold.

[0191] It can be seen that this optional embodiment can determine whether the horizontal gradient parameter corresponding to the pixel satisfies the horizontal gradient continuity comparison relationship, and whether the vertical gradient parameter satisfies the vertical gradient continuity comparison relationship, and then when any gradient continuity comparison relationship is not satisfied, it is determined that there are gradient parameters to be set to zero in the target gradient parameters corresponding to the pixel. This is beneficial to improving the execution reliability and accuracy of the zeroing operation of the target gradient parameters corresponding to the pixel, and further beneficial to improving the subsequent accuracy and efficiency of determining the pixel block noise parameters, thereby helping to improve the denoising effect of the target image.

[0192] In another optional embodiment, determining the block noise intensity parameter of the target image according to the target autocorrelation parameters corresponding to all pixels in the above step 205 includes:

[0193] For each pixel, according to the horizontal gradient autocorrelation parameter corresponding to the pixel, the horizontal average gradient autocorrelation parameter corresponding to the pixel is calculated, and according to the vertical gradient autocorrelation parameter corresponding to the pixel, the average vertical gradient autocorrelation parameter corresponding to the pixel is calculated;

[0194] For each pixel, a horizontal block noise intensity parameter corresponding to the pixel is determined according to the average horizontal gradient autocorrelation parameter corresponding to the pixel and the maximum horizontal gradient autocorrelation parameter among the horizontal gradient autocorrelation parameters corresponding to the pixel, and a vertical block noise intensity parameter corresponding to the pixel is determined according to the average vertical gradient autocorrelation parameter corresponding to the pixel and the maximum vertical gradient autocorrelation parameter among the vertical gradient autocorrelation parameters corresponding to the pixel;

[0195] According to the horizontal block noise intensity parameter and the vertical block noise intensity parameter corresponding to each pixel, the target block noise intensity parameter corresponding to each pixel is determined, and according to the target block noise intensity parameters corresponding to all pixels, the block noise intensity parameters of the target image are determined.

[0196] In this optional embodiment, further, the horizontal horizontal gradient autocorrelation parameter corresponding to the pixel is:

[0197]

[0198] (u1,u2) is the first translation range, corrHozMax(x) is the maximum horizontal gradient autocorrelation parameter corresponding to the pixel (x,y), that is, corrHozMax(x)=max(corrHoz(x,s1)), s1∈[u1,u2].

[0199] And, the average vertical gradient autocorrelation parameter corresponding to the pixel is:

[0200]

[0201] (v1, v2) is the second translation range, corrVerMax(y) is the maximum vertical gradient autocorrelation parameter corresponding to the pixel (x, y), that is, corrVerMax(y) = max(corrVer(y, s2)), s2∈[v1, v2].

[0202] Furthermore, the horizontal block noise intensity parameter corresponding to the pixel is:

[0203] blkNoiseStrHoz(x)=corrHozMax(x) / corrHozMean(x).

[0204] And, the vertical block noise intensity parameter corresponding to the pixel is:

[0205] blkNoiseStrVer(y)=corrVerMax(y) / corrVerMean(y).

[0206] Optionally, the target block noise intensity parameter corresponding to the pixel (x, y) may be:

[0207] blkNoiseStr(x,y)=max(blkNoiseStrHoz(x),blkNoiseStrVer(y));

[0208] Alternatively, the average value between blkNoiseStrHoz(x) ​​and blkNoiseStrVer(y) may be taken. This embodiment does not limit the calculation method of the target block noise intensity parameter corresponding to the pixel.

[0209] It should be noted that when there is block noise in the target image, it can be found that the autocorrelation of the two statistical values ​​corrHoz(x,s1) and corrVer(y,s2) will be significantly enhanced under certain displacements, which is much greater than the autocorrelation under other displacements. Based on this feature, the size of the block noise and the length of the block boundary can be calculated.

[0210] It can be seen that this optional embodiment can calculate the horizontal block noise intensity parameter corresponding to the pixel according to the horizontal horizontal gradient autocorrelation parameter and the maximum horizontal gradient autocorrelation parameter corresponding to the pixel, and calculate the vertical block noise intensity parameter corresponding to the pixel according to the average vertical gradient autocorrelation parameter and the maximum vertical gradient autocorrelation parameter corresponding to the pixel, and then calculate the target block noise intensity parameter corresponding to the pixel, and determine the block noise intensity parameter of the target image. This is conducive to improving the calculation reliability and accuracy of the target block noise intensity parameter corresponding to the pixel, and further conducive to improving the analysis accuracy and efficiency of the block noise intensity parameter of the target image, so that a precise denoising process for the target image can be achieved.

[0211] In yet another optional embodiment, determining the block noise boundary size parameter of the target image in step 206 includes:

[0212] For each pixel, a first translation variable parameter corresponding to a horizontal gradient autocorrelation parameter corresponding to the pixel is determined from a first translation range corresponding to the pixel as a first target translation parameter corresponding to the pixel, and a second translation variable parameter corresponding to a vertical gradient autocorrelation parameter corresponding to the pixel is determined from a second translation range corresponding to the pixel as a second target translation parameter corresponding to the pixel;

[0213] Determine the first target translation parameter with the highest frequency of occurrence from the first target translation parameters corresponding to all pixels as the target translation parameter in the horizontal direction of the target image, and determine the second target translation parameter with the highest frequency of occurrence from the second target translation parameters corresponding to all pixels as the target translation parameter in the vertical direction of the target image;

[0214] The block noise boundary size parameter of the target image is determined according to the horizontal target translation amount parameter and the vertical target translation amount parameter.

[0215] In this optional embodiment, the occurrence frequency of each first / second target translation parameter in max(corrHoz(x,s1) and max(corrVer(y,s2)) corresponding to all pixels is counted, and the first / second target translation parameter with the highest occurrence frequency is determined as the horizontal target translation parameter and the vertical target translation parameter of the target image.

[0216] For example, if for all pixels corresponding to max(corrHoz(x,s1), the first target translation parameters are 3, 5, and 6, and their corresponding occurrence frequencies are 28, 40, and 88, respectively, then the horizontal target translation parameter is 6, and for all pixels corresponding to max(corrVer(y,s2), the second target translation parameters are 4, 5, and 6, and their corresponding occurrence frequencies are 58, 88, and 30, respectively, then the vertical target translation parameter is 5, so it can be determined that the block noise boundary width parameter of the target image is 6, and the block noise boundary height parameter is 5.

[0217] It can be seen that this optional embodiment is capable of determining the first target translation parameter corresponding to each pixel when the horizontal gradient autocorrelation parameter is maximum, and the second target translation parameter corresponding to the vertical gradient autocorrelation parameter is maximum, and then determining the first target translation parameter with the highest frequency in the first target translation parameters corresponding to all pixels and the second target translation parameter with the highest frequency in the corresponding second target translation parameters, thereby determining the block noise boundary size parameters of the target image. In this way, by utilizing the autocorrelation function calculation and the block noise level analysis of the pixels, the reliability and accuracy of determining the block noise boundary size parameters of the target image can be improved, thereby better guiding the filter to filter the block noise of the target image, effectively reducing the block effect in the target image, and thus improving the visual effect of the target image.

[0218] Embodiment 3

[0219] See also Figure 3 , Figure 3 Schematic diagram of a block noise parameter determination device based on autocorrelation function disclosed in an embodiment of the present invention. Figure 3 As shown, the block noise parameter determination device based on the autocorrelation function may include:

[0220] A determination module 301 is used to determine a target gradient parameter corresponding to each pixel in a target image to be detected;

[0221] A gradient accumulation module 302 is used to perform a gradient accumulation operation on each pixel based on a target gradient parameter corresponding to each pixel to obtain a target gradient accumulation value corresponding to each pixel;

[0222] An autocorrelation calculation module 303 is used to perform an autocorrelation calculation operation on each pixel based on the target gradient accumulation value corresponding to each pixel, so as to obtain a target autocorrelation parameter corresponding to each pixel;

[0223] The determination module 301 is also used to determine the block noise intensity parameter of the target image according to the target autocorrelation parameters corresponding to all pixels; determine the block noise boundary size parameter of the target image, and determine the block noise parameter of the target image according to the block noise intensity parameter and the block noise boundary size parameter.

[0224] In the embodiment of the present invention, the target gradient parameter includes a horizontal gradient parameter and a vertical gradient parameter, the target gradient accumulation value includes a horizontal gradient accumulation value and a vertical gradient accumulation value; the target autocorrelation parameter includes a horizontal gradient autocorrelation parameter and a vertical gradient autocorrelation parameter.

[0225] Furthermore, the target gradient parameter corresponding to each pixel is determined by the following formula:

[0226] gradHoz(x,y)_trans=Y(x+1,y)+Y(x-1,y)-2Y(x,y);

[0227] gradVer(x,y)_trans=Y(x,y+1)+Y(x,y-1)-2Y(x,y);

[0228]

[0229] Among them, gradHoz(x,y) is the horizontal gradient parameter corresponding to the pixel (x,y) in the target image, gradVer(x,y) is the vertical gradient parameter corresponding to the pixel (x,y) in the target image, Y is the brightness channel parameter of the target image, gradTh1 is the preset horizontal gradient threshold corresponding to the pixel (x,y), and gradTh2 is the preset vertical gradient threshold corresponding to the pixel (x,y).

[0230] Furthermore, the horizontal gradient accumulation value corresponding to each pixel is calculated by the following formula:

[0231]

[0232] Among them, gradAccHoz(x) ​​is the accumulated value of the horizontal gradient corresponding to the pixel (x, y), height is the height parameter of the target image, and i is the index parameter associated with the height parameter;

[0233] And, the vertical gradient accumulation value corresponding to each pixel is calculated by the following formula:

[0234]

[0235] Among them, gradAccVer(y) is the vertical gradient accumulation value corresponding to the pixel (x, y), width is the width parameter of the target image, and j is the index parameter associated with the width parameter.

[0236] Furthermore, the horizontal gradient autocorrelation parameter corresponding to each pixel is calculated by the following formula:

[0237]

[0238] Wherein, corrHoz(x,s1) is the horizontal gradient autocorrelation parameter corresponding to the pixel (x,y), r1 and -r1 are respectively the signal upper limit parameter and the signal lower limit parameter in the preset first signal parameter range of the pixel, m is the index parameter associated with the first signal parameter range, s1 is the first translation variable parameter of the pixel, and s1 is in the preset first translation range;

[0239] And, the vertical gradient autocorrelation parameter corresponding to each pixel is calculated by the following formula:

[0240]

[0241] Among them, corrVer(y,s2) is the vertical gradient autocorrelation parameter corresponding to the pixel (x,y), r2 and -r2 are the signal upper limit parameter and signal lower limit parameter in the preset second signal parameter range of the pixel, n is the index parameter associated with the second signal parameter range, s2 is the second translation variable parameter of the pixel, and s2 is in the preset second translation range.

[0242] It can be seen that implementation Figure 3 The described block noise parameter determination device based on autocorrelation function can determine the block noise parameters of the target image by analyzing the block noise level of each pixel (i.e., the pixel-level block noise detection process), thereby improving the reliability and accuracy of determining the block noise parameters of the target image, and is beneficial to improving the subsequent denoising effect of the target image, thereby effectively reducing the block effect in the target image, so as to enhance the visual effect of the target image and satisfy the audience's viewing experience.

[0243] In an optional embodiment, the device further comprises:

[0244] A determination module 304 is configured to determine, for each pixel, whether there is a gradient parameter to be set to zero in the target gradient parameters corresponding to each pixel, and if so, determine the pixel as a target pixel; before the gradient accumulation module 302 performs a gradient accumulation operation on each pixel to obtain a target gradient accumulation value corresponding to each pixel.

[0245] The zeroing module 305 is used to perform a zeroing operation on the gradient parameters to be zeroed corresponding to all target pixels to obtain the zeroed gradient parameters corresponding to all target pixels;

[0246] The updating module 306 is used to update the target gradient parameters corresponding to all pixels according to the zeroed gradient parameters corresponding to all target pixels, and trigger the gradient accumulation module 302 to perform a gradient accumulation operation on each pixel based on the target gradient parameter corresponding to each pixel to obtain the target gradient accumulation value corresponding to each pixel.

[0247] It can be seen that implementation Figure 4The described block noise parameter determination device based on the autocorrelation function can perform a zeroing operation on the zeroing gradient parameter corresponding to the target pixel in the target image to update the target gradient parameters corresponding to all pixels, and then perform a gradient accumulation operation on all pixels. In this way, it is beneficial to improve the determination reliability and accuracy of the target gradient parameters corresponding to each pixel, and further beneficial to improve the execution reliability, accuracy and efficiency of the subsequent gradient accumulation operation for each pixel, thereby facilitating the improvement of the calculation reliability and precision of the target autocorrelation parameters for each pixel, so as to achieve the precise determination process of the block noise parameters of the target image.

[0248] In another optional embodiment, the manner in which the determination module 304 determines whether there is a zeroing gradient parameter to be zeroed in the target gradient parameter corresponding to the pixel specifically includes:

[0249] Determine whether the horizontal gradient parameter corresponding to the pixel satisfies a preset horizontal gradient continuity comparison relationship, and determine whether the vertical gradient parameter corresponding to the pixel satisfies a preset vertical gradient continuity comparison relationship;

[0250] When it is determined that the horizontal gradient parameter corresponding to the pixel does not satisfy the horizontal gradient continuity comparison relationship and / or the vertical gradient parameter corresponding to the pixel does not satisfy the vertical gradient continuity comparison relationship, it is determined that there is a zeroing gradient parameter to be zeroed in the target gradient parameter corresponding to the pixel.

[0251] In this optional embodiment, the horizontal gradient continuity comparison relationship includes a first relationship, a second relationship, and a third relationship, and the vertical gradient continuity comparison relationship includes a fourth relationship, a fifth relationship, and a sixth relationship.

[0252] Furthermore, the first relationship is:

[0253] |gradHoz(x,y) - gradHoz(x,y - 1)| < continuityTh_Hoz1;

[0254] The second relationship is: |gradHoz(x,y) - gradHoz(x,y + 1)| < continuityTh_Hoz2;

[0255] The third relationship is: |gradHoz(x,y) - gradHoz(x,y - 2)| < continuityTh_Hoz3, or

[0256] |gradHoz(x,y) - gradHoz(x,y + 2)| < continuityTh_Hoz4;

[0257] continuityTh_Hoz1 is a preset first horizontal gradient continuity threshold, continuityTh_Hoz2 is a preset second horizontal gradient continuity threshold, continuityTh_Hoz3 is a preset third horizontal gradient continuity threshold, and continuityTh_Hoz4 is a preset fourth horizontal gradient continuity threshold;

[0258] Moreover, the fourth relationship is: |gradVer(x,y) - gradVer(x - 1,y)| < continuityTh_Ver1;

[0259] The fifth relationship is: |gradVer(x,y) - gradVer(x + 1,y)| < continuityTh_Ver2;

[0260] The sixth relationship is: |gradVer(x,y) - gradVer(x - 2,y)| < continuityTh_Ver3, or

[0261] |gradVer(x,y) - gradVer(x + 2,y)| < continuityTh_Ver4;

[0262] continuityTh_Ver1 is a preset first vertical gradient continuity threshold, continuityTh_Ver2 is a preset second vertical gradient continuity threshold, continuityTh_Ver3 is a preset third vertical gradient continuity threshold, and continuityTh_Ver4 is a preset fourth vertical gradient continuity threshold.

[0263] It can be seen that implementing Figure 4 the described block noise parameter determination device based on the autocorrelation function can respectively determine whether the horizontal gradient parameters corresponding to the pixels satisfy the horizontal gradient continuity comparison relationship and whether the vertical gradient parameters satisfy the vertical gradient continuity comparison relationship. Then, when any gradient continuity comparison relationship is not satisfied, it is determined that there are zeroing gradient parameters to be zeroed in the target gradient parameters corresponding to the pixel. In this way, it is beneficial to improve the execution reliability and accuracy of the zeroing operation of the target gradient parameters corresponding to the pixel, and further beneficial to improve the determination accuracy and efficiency of the pixel block noise parameters, thereby being beneficial to enhancing the denoising effect of the target image.

[0264] In another optional embodiment, the manner in which the determination module 301 determines the block noise intensity parameter of the target image according to the target autocorrelation parameters corresponding to all pixels specifically includes:

[0265] For each pixel, according to the horizontal gradient autocorrelation parameter corresponding to the pixel, the horizontal average gradient autocorrelation parameter corresponding to the pixel is calculated, and according to the vertical gradient autocorrelation parameter corresponding to the pixel, the average vertical gradient autocorrelation parameter corresponding to the pixel is calculated;

[0266] For each pixel, a horizontal block noise intensity parameter corresponding to the pixel is determined according to the average horizontal gradient autocorrelation parameter corresponding to the pixel and the maximum horizontal gradient autocorrelation parameter among the horizontal gradient autocorrelation parameters corresponding to the pixel, and a vertical block noise intensity parameter corresponding to the pixel is determined according to the average vertical gradient autocorrelation parameter corresponding to the pixel and the maximum vertical gradient autocorrelation parameter among the vertical gradient autocorrelation parameters corresponding to the pixel;

[0267] According to the horizontal block noise intensity parameter and the vertical block noise intensity parameter corresponding to each pixel, the target block noise intensity parameter corresponding to each pixel is determined, and according to the target block noise intensity parameters corresponding to all pixels, the block noise intensity parameters of the target image are determined.

[0268] In this optional embodiment, the horizontal horizontal gradient autocorrelation parameter corresponding to the pixel is:

[0269]

[0270] (u1,u2) is the first translation range, corrHozMax(x) is the maximum horizontal gradient autocorrelation parameter corresponding to the pixel (x,y);

[0271] The average vertical gradient autocorrelation parameter corresponding to the pixel is:

[0272]

[0273] (v1, v2) is the second translation range, corrVerMax(y) is the maximum vertical gradient autocorrelation parameter corresponding to the pixel (x, y);

[0274] Among them, the horizontal block noise intensity parameter corresponding to the pixel is:

[0275] blkNoiseStrHoz(x)=corrHozMax(x) / corrHozMean(x);

[0276] The vertical block noise intensity parameter corresponding to the pixel is:

[0277] blkNoiseStrVer(y)=corrVerMax(y) / corrVerMean(y).

[0278] It can be seen that implementation Figure 4The described block noise parameter determination device based on the autocorrelation function can calculate the horizontal block noise intensity parameter corresponding to the pixel according to the horizontal horizontal gradient autocorrelation parameter and the maximum horizontal gradient autocorrelation parameter corresponding to the pixel, and calculate the vertical block noise intensity parameter corresponding to the pixel according to the average vertical gradient autocorrelation parameter and the maximum vertical gradient autocorrelation parameter corresponding to the pixel, and then calculate the target block noise intensity parameter corresponding to the pixel, and determine the block noise intensity parameter of the target image. This is conducive to improving the calculation reliability and accuracy of the target block noise intensity parameter corresponding to the pixel, and further conducive to improving the analysis accuracy and efficiency of the block noise intensity parameter of the target image, so that an accurate denoising process for the target image can be achieved.

[0279] In yet another optional embodiment, the manner in which the determination module 301 determines the block noise boundary size parameter of the target image specifically includes:

[0280] For each pixel, a first translation variable parameter corresponding to a horizontal gradient autocorrelation parameter corresponding to the pixel is determined from a first translation range corresponding to the pixel as a first target translation parameter corresponding to the pixel, and a second translation variable parameter corresponding to a vertical gradient autocorrelation parameter corresponding to the pixel is determined from a second translation range corresponding to the pixel as a second target translation parameter corresponding to the pixel;

[0281] Determine the first target translation parameter with the highest frequency of occurrence from the first target translation parameters corresponding to all pixels as the target translation parameter in the horizontal direction of the target image, and determine the second target translation parameter with the highest frequency of occurrence from the second target translation parameters corresponding to all pixels as the target translation parameter in the vertical direction of the target image;

[0282] The block noise boundary size parameter of the target image is determined according to the horizontal target translation amount parameter and the vertical target translation amount parameter.

[0283] It can be seen that implementation Figure 4The described block noise parameter determination device based on the autocorrelation function can determine the first target translation parameter corresponding to each pixel when the horizontal gradient autocorrelation parameter is maximum, and the second target translation parameter corresponding to the vertical gradient autocorrelation parameter is maximum, and then determine the first target translation parameter that appears most frequently among the first target translation parameters corresponding to all pixels and the second target translation parameter that appears most frequently among the corresponding second target translation parameters, so as to determine the block noise boundary size parameters of the target image. In this way, by utilizing the autocorrelation function calculation and the block noise level analysis of the pixels, the reliability and accuracy of determining the block noise boundary size parameters of the target image can be improved, and then the filter can be better guided to filter the block noise of the target image, effectively reducing the block effect in the target image, thereby improving the visual effect of the target image.

[0284] Embodiment 4

[0285] See also Figure 5 , Figure 5 FIG. 1 is a schematic diagram of the structure of another device for determining block noise parameters based on an autocorrelation function disclosed in an embodiment of the present invention. Figure 5 As shown, the block noise parameter determination device based on the autocorrelation function may include:

[0286] A memory 401 storing executable program codes;

[0287] a processor 402 coupled to the memory 401;

[0288] The processor 402 calls the executable program code stored in the memory 401 to execute the steps in the method for determining block noise parameters based on the autocorrelation function described in the first embodiment of the present invention or the second embodiment of the present invention.

[0289] Embodiment 5

[0290] An embodiment of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute the steps in the method for determining block noise parameters based on autocorrelation function described in embodiment 1 or embodiment 2 of the present invention.

[0291] Embodiment 6

[0292] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps in the method for determining block noise parameters based on the autocorrelation function described in Embodiment 1 or Embodiment 2.

[0293] The device embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, i.e., they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0294] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution can be essentially or partly contributed to the prior art in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0295] Finally, it should be noted that the method and device for determining block noise parameters based on autocorrelation function disclosed in the embodiment of the present invention only disclose the preferred embodiments of the present invention, which are only used to illustrate the technical scheme of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical schemes described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical schemes from the spirit and scope of the technical schemes of the embodiments of the present invention.

Claims

1. A method for determining block noise parameters based on autocorrelation function, characterized in that: The method comprises: Determine a target gradient parameter corresponding to each pixel in the target image to be detected, and perform a gradient accumulation operation on each pixel based on the target gradient parameter corresponding to each pixel to obtain a target gradient accumulation value corresponding to each pixel; the target gradient parameter includes a horizontal gradient parameter and a vertical gradient parameter, and the target gradient accumulation value includes a horizontal gradient accumulation value and a vertical gradient accumulation value; Based on the target gradient accumulated value corresponding to each pixel, an autocorrelation calculation operation is performed on each pixel to obtain a target autocorrelation parameter corresponding to each pixel, and a block noise intensity parameter of the target image is determined according to the target autocorrelation parameters corresponding to all the pixels; the target autocorrelation parameters include a horizontal gradient autocorrelation parameter and a vertical gradient autocorrelation parameter; A block noise boundary size parameter of the target image is determined, and a block noise parameter of the target image is determined according to the block noise intensity parameter and the block noise boundary size parameter.

2. The method for determining block noise parameters based on autocorrelation function according to claim 1, characterized in that: The target gradient parameter corresponding to each pixel is determined by the following formula: gradHoz(x,y)_trans=Y(x+1,y)+Y(x-1,y)-2Y(x,y); gradVer(x,y)_trans=Y(x,y+1)+Y(x,y-1)-2Y(x,y); Among them, gradHoz(x,y) is the horizontal gradient parameter corresponding to the pixel (x,y) in the target image, gradVer(x,y) is the vertical gradient parameter corresponding to the pixel (x,y) in the target image, Y is the brightness channel parameter of the target image, gradTh1 is the preset horizontal gradient threshold corresponding to the pixel (x,y), and gradTh2 is the preset vertical gradient threshold corresponding to the pixel (x,y).

3. The method for determining block noise parameters based on autocorrelation function according to claim 2, characterized in that: Before performing a gradient accumulation operation on each pixel based on the target gradient parameter corresponding to each pixel to obtain a target gradient accumulation value corresponding to each pixel, the method further includes: For each of the pixels, determining whether there is a gradient parameter to be set to zero in the target gradient parameters corresponding to the pixel, and if so, determining the pixel as a target pixel; Performing a zeroing operation on the to-be-zeroed gradient parameters corresponding to all the target pixels to obtain the zeroed gradient parameters corresponding to all the target pixels; According to the zeroed gradient parameters corresponding to all the target pixels, the target gradient parameters corresponding to all the pixels are updated, and the operation of performing a gradient accumulation operation on each pixel based on the target gradient parameter corresponding to each pixel to obtain a target gradient accumulation value corresponding to each pixel is triggered.

4. The method for determining block noise parameters based on autocorrelation function according to claim 3, characterized in that: The determining whether there is a gradient parameter to be set to zero in the target gradient parameters corresponding to the pixel includes: Determine whether the horizontal gradient parameter corresponding to the pixel satisfies a preset horizontal gradient continuity comparison relationship, and determine whether the vertical gradient parameter corresponding to the pixel satisfies a preset vertical gradient continuity comparison relationship; the horizontal gradient continuity comparison relationship includes a first relationship, a second relationship, and a third relationship, and the vertical gradient continuity comparison relationship includes a fourth relationship, a fifth relationship, and a sixth relationship; When it is determined that the horizontal gradient parameter corresponding to the pixel does not satisfy the horizontal gradient continuity comparison relationship and / or the vertical gradient parameter corresponding to the pixel does not satisfy the vertical gradient continuity comparison relationship, determine that there is a to-be-zeroed gradient parameter that needs to be set to zero in the target gradient parameter corresponding to the pixel; Wherein, the first relationship is: |gradHoz(x,y)-gradHoz(x,y - 1)| < continuityTh_Hoz1; The second relationship is: |gradHoz(x,y)-gradHoz(x,y + 1)| < continuityTh_Hoz2; The third relationship is: |gradHoz(x,y)-gradHoz(x,y - 2)| < continuityTh_Hoz3, or |gradHoz(x,y)-gradHoz(x,y + 2)| < continuityTh_Hoz4; continuityTh_Hoz1 is a preset first horizontal gradient continuity threshold, continuityTh_Hoz2 is a preset second horizontal gradient continuity threshold, continuityTh_Hoz3 is a preset third horizontal gradient continuity threshold, and continuityTh_Hoz4 is a preset fourth horizontal gradient continuity threshold; Wherein, the fourth relationship is: |gradVer(x,y)-gradVer(x - 1,y)| < continuityTh_Ver1; The fifth relationship is: |gradVer(x,y)-gradVer(x + 1,y)| < continuityTh_Ver2; The sixth relationship is: |gradVer(x,y)-gradVer(x - 2,y)| < continuityTh_Ver3, or |gradVer(x,y)-gradVer(x + 2,y)| < continuityTh_Ver4; continuityTh_Ver1 is a preset first vertical gradient continuity threshold, continuityTh_Ver2 is a preset second vertical gradient continuity threshold, continuityTh_Ver3 is a preset third vertical gradient continuity threshold, and continuityTh_Ver4 is a preset fourth vertical gradient continuity threshold.

5. The method for determining block noise parameters based on autocorrelation function according to any one of claims 2 to 4, characterized in that: The horizontal gradient accumulation value corresponding to each pixel is calculated by the following formula: Wherein, gradAccHoz(x) ​​is the accumulated value of the horizontal gradient corresponding to the pixel (x, y), height is the height parameter of the target image, and i is the index parameter associated with the height parameter; And, the vertical gradient accumulation value corresponding to each pixel is calculated by the following formula: Among them, gradAccVer(y) is the vertical gradient accumulation value corresponding to the pixel (x, y), width is the width parameter of the target image, and j is the index parameter associated with the width parameter.

6. The method for determining block noise parameters based on autocorrelation function according to claim 5, characterized in that: The horizontal gradient autocorrelation parameter corresponding to each pixel is calculated by the following formula: Wherein, corrHoz(x, s1) is the horizontal gradient autocorrelation parameter corresponding to the pixel (x, y), r1 and -r1 are respectively the signal upper limit parameter and the signal lower limit parameter in the preset first signal parameter range of the pixel, m is the index parameter associated with the first signal parameter range, s1 is the first translation variable parameter of the pixel, and s1 is in the preset first translation range; And, the vertical gradient autocorrelation parameter corresponding to each pixel is calculated by the following formula: Among them, corrVer(y,s2) is the vertical gradient autocorrelation parameter corresponding to the pixel (x,y), r2 and -r2 are respectively the signal upper limit parameter and the signal lower limit parameter in the preset second signal parameter range of the pixel, n is the index parameter associated with the second signal parameter range, s2 is the second translation variable parameter of the pixel, and s2 is in the preset second translation range.

7. The method for determining block noise parameters based on autocorrelation function according to claim 6, characterized in that: The step of determining the block noise intensity parameter of the target image according to the target autocorrelation parameters corresponding to all the pixels comprises: For each pixel, according to the horizontal gradient autocorrelation parameter corresponding to the pixel, the horizontal average gradient autocorrelation parameter corresponding to the pixel is calculated, and according to the vertical gradient autocorrelation parameter corresponding to the pixel, the average vertical gradient autocorrelation parameter corresponding to the pixel is calculated; For each of the pixels, determining a horizontal block noise intensity parameter corresponding to the pixel according to an average horizontal gradient autocorrelation parameter corresponding to the pixel and a maximum horizontal gradient autocorrelation parameter among the horizontal gradient autocorrelation parameters corresponding to the pixel, and determining a vertical block noise intensity parameter corresponding to the pixel according to an average vertical gradient autocorrelation parameter corresponding to the pixel and a maximum vertical gradient autocorrelation parameter among the vertical gradient autocorrelation parameters corresponding to the pixel; Determine a target block noise intensity parameter corresponding to each pixel according to the horizontal block noise intensity parameter and the vertical block noise intensity parameter corresponding to each pixel, and determine the block noise intensity parameter of the target image according to the target block noise intensity parameters corresponding to all the pixels; Among them, the horizontal horizontal gradient autocorrelation parameter corresponding to the pixel is: (u1, u2) is the first translation range, corrHozMax(x) is the maximum horizontal gradient autocorrelation parameter corresponding to the pixel (x, y); The average vertical gradient autocorrelation parameter corresponding to the pixel is: (v1, v2) is the second translation range, corrVerMax(y) is the maximum vertical gradient autocorrelation parameter corresponding to the pixel (x, y); Among them, the horizontal block noise intensity parameter corresponding to the pixel is: blkNoiseStrHoz(x)=corrHozMax(x) / corrHozMean(x); The vertical block noise intensity parameter corresponding to the pixel is: blkNoiseStrVer(y)=corrVerMax(y) / corrVerMean(y).

8. The method for determining block noise parameters based on autocorrelation function according to claim 7, characterized in that: The step of determining a block noise boundary size parameter of the target image comprises: For each pixel, determine, from the first translation range corresponding to the pixel, a first translation variable parameter corresponding to when the horizontal gradient autocorrelation parameter corresponding to the pixel is the maximum horizontal gradient autocorrelation parameter, as the first target translation parameter corresponding to the pixel, and determine, from the second translation range corresponding to the pixel, a second translation variable parameter corresponding to when the vertical gradient autocorrelation parameter corresponding to the pixel is the maximum vertical gradient autocorrelation parameter, as the second target translation parameter corresponding to the pixel; Determine the first target translation parameter with the highest occurrence frequency from among the first target translation parameters corresponding to all the pixels as the target translation parameter in the horizontal direction of the target image, and determine the second target translation parameter with the highest occurrence frequency from among the second target translation parameters corresponding to all the pixels as the target translation parameter in the vertical direction of the target image; A block noise boundary size parameter of the target image is determined according to the horizontal target translation parameter and the vertical target translation parameter.

9. A block noise parameter determination device based on autocorrelation function, characterized in that: The device comprises: A determination module, used to determine a target gradient parameter corresponding to each pixel in the target image to be detected; A gradient accumulation module, configured to perform a gradient accumulation operation on each pixel based on a target gradient parameter corresponding to each pixel, so as to obtain a target gradient accumulation value corresponding to each pixel; the target gradient parameter includes a horizontal gradient parameter and a vertical gradient parameter, and the target gradient accumulation value includes a horizontal gradient accumulation value and a vertical gradient accumulation value; An autocorrelation calculation module is used to perform an autocorrelation calculation operation on each pixel based on the target gradient accumulation value corresponding to each pixel, so as to obtain a target autocorrelation parameter corresponding to each pixel; the target autocorrelation parameter includes a horizontal gradient autocorrelation parameter and a vertical gradient autocorrelation parameter; The determination module is also used to determine the block noise intensity parameter of the target image according to the target autocorrelation parameters corresponding to all the pixels; determine the block noise boundary size parameter of the target image, and determine the block noise parameter of the target image according to the block noise intensity parameter and the block noise boundary size parameter.

10. A block noise parameter determination device based on autocorrelation function, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the block noise parameter determination method based on the autocorrelation function as described in any one of claims 1-8.

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