CN109525847B - Just noticeable distortion model threshold calculation method - Google Patents

Just noticeable distortion model threshold calculation method Download PDF

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CN109525847B
CN109525847B CN201811345416.1A CN201811345416A CN109525847B CN 109525847 B CN109525847 B CN 109525847B CN 201811345416 A CN201811345416 A CN 201811345416A CN 109525847 B CN109525847 B CN 109525847B
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曾焕强
曾志鹏
陈婧
朱建清
蔡灿辉
马凯光
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Abstract

The invention relates to a just noticeable distortion model threshold calculation method, which comprises the following steps: performing DCT transformation on the original image, and calculating corresponding brightness self-adaptive module values and spatial contrast sensitivity function module values; the texture blocks of the image are classified more finely by using the frequency energy distribution characteristics of 8 multiplied by 8DCT blocks, a contrast masking factor is obtained, and a contrast masking module value is calculated; extracting texture features of a current image block by using spatial frequency distribution of DCT coefficients, and calculating texture difference between two different blocks to obtain visual perception adjustment factors of the different blocks; and integrating the modules to obtain a final JND threshold value. According to the algorithm provided by the invention, the JND model can accommodate more noises on the premise of ensuring the visual quality. The model can be widely used for perception image/video coding, watermarking, quality evaluation and the like.

Description

Just noticeable distortion model threshold calculation method
Technical Field
The invention relates to the field of image/video coding, in particular to a perceptual visual redundancy estimation model.
Background
With the rapid development of multimedia technology, images/videos gradually become the main carrier for people to acquire information, but people also have to face the challenges of transmission, storage and the like caused by massive image/video data. Therefore, how to efficiently perform image/video coding becomes a research hotspot in the current academic and industrial fields. The traditional image/video coding technology mainly achieves the purpose of improving the coding efficiency by removing spatial domain and temporal domain redundancies, but the visual perception of human eyes is often ignored. In order to further effectively improve the coding efficiency, researchers are dedicated to research on estimation of visual redundancy of images/videos, and a Just Noticeable Distortion (JND) model is a perceptual model capable of effectively representing the visual redundancy.
The types of JND models can be roughly classified into two types: a pixel domain JND model and a DCT domain JND model. This patent focuses on the study of the DCT domain JND model. Because the conventional DCT domain JND model contrast masking only considers the visual masking effect brought by three block types, namely a smooth block, an edge block and a texture block, the influence generated by the texture blocks with different complexity is ignored, and meanwhile, the relation between the blocks of the image is also ignored. Therefore, the accuracy of the conventional DCT domain JND model for visual redundancy estimation needs to be further improved.
Disclosure of Invention
The main purpose of the present invention is to overcome the above-mentioned drawbacks in the prior art, and to provide a method for calculating a just noticeable distortion model threshold, further considering different visual masking effects that may be caused by texture blocks of different complexity, and considering the relation between image blocks and blocks, and providing a more accurate visual redundancy estimation model.
The invention adopts the following technical scheme:
a just noticeable distortion model threshold calculation method is characterized by comprising the following steps:
1) performing DCT transformation on an input image;
2) operating DCT coefficient to calculate brightness self-adaptive module value FLAAnd a spatial contrast sensitivity function module value;
3) further dividing the types of texture blocks by using the frequency energy distribution characteristics of 8 multiplied by 8DCT blocks, and calculating a contrast masking module value;
4) extracting texture features of the current block based on different frequency division of 8 multiplied by 8DCT blocks, calculating texture difference values of the current block and other blocks, and solving visual perception adjustment factors of the blocks;
5) and integrating the brightness self-adaptive module value, the spatial contrast sensitivity function module value, the contrast masking module value and the visual perception adjustment factors of different blocks to obtain the JND threshold of the image.
The brightness adaptive module value and the spatial contrast sensitivity function module value are respectively obtained by the following formulas:
Figure GDA0002914649420000021
Figure GDA0002914649420000022
wherein, (i, j) represents the coefficient position of 8 multiplied by 8DCT block, the value range of i and j is integer 0-7, FLARepresenting the value of the luminance adaptation block, FCSF(i, j) represents the spatial contrast sensitivity function module value,
Figure GDA0002914649420000023
represents the average intensity, φ, of an 8 × 8DCT blockiAnd phijNormalization factor, omega, for DCT transformationijThe spatial frequency is represented by a representation of,
Figure GDA0002914649420000024
the angle of direction of the corresponding DCT component, a, b, c, r, s, is constant.
The type of the texture block is further divided according to the frequency distribution of the 8 × 8DCT block coefficients, and the contrast masking module is obtained by the following formula:
Figure GDA0002914649420000025
where ψ is the contrast masking factor, C (n)1,n2I, j) are the DCT coefficients of the corresponding position, (n)1,n2) ε is set to 0.36 for the position index of the current DCT block in the image.
The contrast masking factor is obtained by the following formula:
Figure GDA0002914649420000026
wherein texture block energy Et＝E+H1E and H1The sum of the absolute values of the DCT coefficients in the MF region and the HF region, respectively.
The visual perception adjustment factor is obtained by the following formula:
αk＝1-Sk
wherein SkThe value range of k is an integer for the weight of the kth DCT block in the image
Figure GDA0002914649420000031
Where W and H are the width and height of the input image respectively,
Figure GDA0002914649420000032
indicating rounding up.
The weight of the DCT blocks in the image is obtained by the following formula:
Figure GDA0002914649420000033
where σ is a Gaussian model parameter, dklIs the Euclidean distance, D, of the kth DCT block from the l DCT blockklThe texture difference between the kth DCT block and the l DCT block.
Texture difference DklObtained by the following formula:
Dkl＝max(h(Tk,Tl),h(Tl,Tk))
where T denotes the texture feature of the DCT block, h (T)k,Tl) The Hausdorff distance of the texture features of the kth DCT block and the l DCT block.
The Hausdorff distance and DCT block texture features of two different DCT block texture features are obtained by the following formulas:
Figure GDA0002914649420000034
T＝{tLF,tMF,tHF}
wherein t isLF，tMFAnd tHFThe sum of the coefficients of the LF, MF and HF regions, respectively, | | · | |, represents a 2 norm.
The JND threshold of the DCT domain is obtained by the following formula:
the JND threshold is the visual perception adjustment factor × spatial contrast sensitivity function module value × luminance adaptive module value × contrast masking module value.
As can be seen from the above description of the present invention, compared with the prior art, the present invention has the following advantages:
1. the method of the invention classifies texture blocks more finely according to the frequency energy of DCT blocks, so that the estimation of contrast masking is more in line with the visual characteristics of human eyes;
2. the method of the invention considers the relation between the image blocks, extracts the texture characteristics of the DCT blocks, obtains the visual perception adjustment factor of the image blocks by calculating the texture difference value of the current block and other blocks, and is beneficial to the accurate estimation of the visual redundancy.
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FIG. 1 is a main flow chart of the process of the present invention
Fig. 2 is a frequency distribution diagram of 8 × 8DCT blocks.
Detailed Description
The invention is further described below by means of specific embodiments.
The invention provides a method for accurately estimating visual redundancy and calculating a just noticeable distortion model threshold, which comprises the following specific implementation steps as shown in fig. 1:
1) the DCT transform is performed on the input image.
2) And operating the DCT coefficient to calculate a brightness self-adaptive module value and a spatial contrast sensitivity function module value.
3) And further dividing the types of the texture blocks by using the frequency energy distribution characteristics of the 8 multiplied by 8DCT blocks, and calculating a contrast masking module value.
4) Based on different frequency division of 8 multiplied by 8DCT blocks, extracting texture features of the current block, calculating texture difference values of the current block and other blocks, and solving visual perception adjustment factors of the blocks.
5) And integrating the brightness self-adaption module, the spatial contrast sensitivity function module, the contrast masking module and the weight of different blocks to obtain the JND threshold of the image.
Step 1), performing DCT transformation on the input image.
Step 2), calculating a brightness self-adaptive module value FLAAnd spatial contrast sensitivity function module value FCSF(i, j). The method comprises the following specific steps:
Figure GDA0002914649420000041
Figure GDA0002914649420000042
wherein (i, j) represents the position of 8 multiplied by 8DCT block coefficient, the value range of i and j is integer 0-7,
Figure GDA0002914649420000043
represents the average intensity, φ, of an 8 × 8DCT blockiAnd phijFor DCT transformationA normalization factor, omegaijThe spatial frequency is represented by a representation of,
Figure GDA0002914649420000044
the a, b, c, r, s are constants for the directional angles of the corresponding DCT components, and have values of 1.33,0.11,0.18, 0.6, and 0.25, respectively.
And 3) calculating a contrast masking module value.
Specifically, the contrast masking factor ψ is calculated as follows:
Figure GDA0002914649420000051
wherein texture block energy Et＝E+H1E and H1The sum of the absolute values of the DCT coefficients in the MF region and the HF region, respectively.
The contrast masking block value F is obtained by the following formulaCM。
Figure GDA0002914649420000052
Where ψ is the contrast masking factor, C (n)1,n2I, j) are the DCT coefficients of the corresponding position, (n)1,n2) Is the position index of the current DCT block in the image and (i, j) is the coordinate position of the DCT coefficient in the current DCT block, epsilon is set to 0.36.
And 4) calculating a perception adjustment factor of each image block.
Specifically, the Hausdorff distance between the texture feature T of the 8 × 8DCT block and the texture features of two different DCT blocks is extracted by the following formula:
T＝{tLF,tMF,tHF}
Figure GDA0002914649420000053
wherein t isLF，tMFAnd tHFThe sum of the coefficients of the LF, MF and HF regions, respectively,i | · | represents a 2 norm, and the value ranges of k and l are integers
Figure GDA0002914649420000054
Where W and H are the width and height of the input image respectively,
Figure GDA0002914649420000055
indicating rounding up.
Obtaining the texture difference value D by the following formulakl：
Dkl＝max(h(Tk,Tl),h(Tk,Tl))
Wherein h (T)k,Tl) The Hausdorff distance of the texture features of the kth DCT block and the l DCT block.
The weight of the DCT block in the image is obtained by the following formula:
Figure GDA0002914649420000056
wherein SkThe weight of the kth DCT block in the image is taken as the sigma is a Gaussian model parameter and is set as 5, dklIs the Euclidean distance, D, of the kth DCT block from the l DCT blockklThe texture difference between the kth DCT block and the l DCT block.
Based on the weight S occupied by DCT blocks in the imagekCalculating a visual perception adjustment factor of the current block, specifically as follows:
αk＝1-Sk
wherein SkIs the weight of the kth DCT block in the image. k is an integer
Figure GDA0002914649420000061
Where W and H are the width and height of the input image respectively,
Figure GDA0002914649420000062
indicating rounding up.
Step 5) obtaining the JND threshold value through the following formula:
the JND threshold is the visual perception adjustment factor × spatial contrast sensitivity function module value × luminance adaptive module value × contrast masking module value, that is:
JND(n1,n2,i,j)＝α(n1,n2)·FCSF(n1,n2,i,j)·FLA(n1,n2)·FCM(n1,n2,i,j)。
the above description is only an embodiment of the present invention, but the design concept of the present invention is not limited thereto, and any insubstantial modifications made by using the design concept should fall within the scope of infringing the present invention.

Claims (3)

1. A just noticeable distortion model threshold calculation method is characterized by comprising the following steps:
1) performing DCT transformation on an input image;
2) operating DCT coefficient to calculate brightness self-adaptive module value FLAAnd a spatial contrast sensitivity function module value;
3) the method comprises the following steps of further dividing the types of texture blocks by utilizing the frequency energy distribution characteristics of 8 multiplied by 8DCT blocks, calculating a contrast masking factor and further calculating a contrast masking module value, wherein the contrast masking factor is obtained by the following formula:
Figure FDA0002914649410000011
wherein texture block energy Et＝E+H1E and H1Respectively representing the sum of DCT coefficient absolute values of an MF area and an HF area, (i, j) represents the coefficient position of an 8 multiplied by 8DCT block, and the value range of i and j is an integer of 0-7;
4) extracting texture features of the current block based on different frequency division of 8 multiplied by 8DCT blocks, calculating texture difference values of the current block and other blocks, and solving visual perception adjustment factors of the blocks;
the visual perception adjustment factor is obtained by the following formula:
αk＝1-Sk
wherein SkThe value range of k is an integer for the weight of the kth DCT block in the image
Figure FDA0002914649410000012
Where W and H are the width and height of the input image respectively,
Figure FDA0002914649410000013
represents rounding up;
the weight of the DCT blocks in the image is obtained by the following formula:
Figure FDA0002914649410000014
where σ is a Gaussian model parameter, dklIs the Euclidean distance, D, of the kth DCT block from the l DCT blockklThe texture difference value of the kth DCT block and the l DCT block is obtained;
the texture difference value DklObtained by the following formula:
Dkl＝max(h(Tk,Tl),h(Tl,Tk))
where T denotes the texture feature of the DCT block, h (T)k,Tl) The Hausdorff distance of texture features of the kth DCT block and the l DCT block is obtained;
the Hausdorff distance and DCT block texture features of two different DCT block texture features are obtained by the following formulas:
Figure FDA0002914649410000021
T＝{tLF,tMF,tHF}
wherein t isLF，tMFAnd tHFIs the sum of the coefficients of the LF, MF and HF regions, respectively, | | · | | | represents a 2 norm;
5) integrating a brightness self-adaptive module value, a spatial contrast sensitivity function module value, a contrast masking module value and visual perception adjustment factors of different blocks to obtain a JND threshold value of the image;
wherein the JND threshold of the DCT domain is obtained by the following formula:
the JND threshold is the visual perception adjustment factor × spatial contrast sensitivity function module value × luminance adaptive module value × contrast masking module value.
2. The method of claim 1, wherein the luminance adaptive module value and the spatial contrast sensitivity function module value are obtained by the following formulas:
Figure FDA0002914649410000022
Figure FDA0002914649410000023
wherein, FLARepresenting the value of the luminance adaptation block, FCSF(i, j) represents the spatial contrast sensitivity function module value,
Figure FDA0002914649410000024
represents the average intensity, φ, of an 8 × 8DCT blockiAnd phijNormalization factor, omega, for DCT transformationijThe spatial frequency is represented by a representation of,
Figure FDA0002914649410000025
the angle of direction of the corresponding DCT component, a, b, c, r, s, is constant.
3. The method of claim 2, wherein the type of texture block is further divided according to the frequency distribution of 8 x 8DCT block coefficients, and the contrast masking module is obtained by the following formula:
Figure FDA0002914649410000026
where ψ is the contrast masking factor, C (n)1,n2I, j) are the DCT coefficients of the corresponding position, (n)1,n2) ε is set to 0.36 for the position index of the current DCT block in the image.
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