CN109525847A - A kind of just discernable distortion model threshold value calculation method - Google Patents

A kind of just discernable distortion model threshold value calculation method Download PDF

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CN109525847A
CN109525847A CN201811345416.1A CN201811345416A CN109525847A CN 109525847 A CN109525847 A CN 109525847A CN 201811345416 A CN201811345416 A CN 201811345416A CN 109525847 A CN109525847 A CN 109525847A
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block
dct
threshold value
calculation method
value
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CN109525847B (en
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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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Huaqiao University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/60—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using transform coding
    • H04N19/625—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using transform coding using discrete cosine transform [DCT]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/134—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or criterion affecting or controlling the adaptive coding
    • H04N19/136—Incoming video signal characteristics or properties
    • H04N19/137—Motion inside a coding unit, e.g. average field, frame or block difference
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/134—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or criterion affecting or controlling the adaptive coding
    • H04N19/136—Incoming video signal characteristics or properties
    • H04N19/14—Coding unit complexity, e.g. amount of activity or edge presence estimation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/169—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding
    • H04N19/17—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object
    • H04N19/176—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a block, e.g. a macroblock

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Abstract

The present invention relates to a kind of just discernable distortion model threshold value calculation methods comprising: dct transform is carried out to original image, calculates corresponding brightness adaptation module value and spatial contrast sensitivity function module value；Using the frequency energy characteristic distributions of 8 × 8DCT block, more careful classification is carried out to the texture block of image, the contrast masking sensitivity factor is obtained, calculates contrast masking sensitivity module value；The textural characteristics that current image block is extracted using the spatial frequency distribution of DCT coefficient calculate the texture difference between two different masses, obtain the visual perception Dynamic gene of different masses；Above-mentioned module is integrated, final JND threshold value is obtained.The algorithm that the present invention is mentioned, under the premise of guaranteeing visual quality, mentioned JND model can accommodate more noises.The model can be widely used for perceptual image/Video coding, watermark and quality evaluation etc..

Description

A kind of just discernable distortion model threshold value calculation method
Technical field
The present invention relates to image/video coding fields, are related to a kind of visual redundancy estimation model of perception.
Background technique
With the fast development of multimedia technology, image/video is increasingly becoming the main carriers that people obtain information, still People also have to be faced with by challenges such as the large nuber of images/transmission of video data bring, storages.Therefore, how efficiently into Row image/video coding becomes the research hotspot of current academia and industry.Traditional image/video coding technology is mainly By removing spatially and temporally redundancy but the visual impression of human eye is often had ignored in this way to achieve the purpose that improve code efficiency By.In order to effectively further promote code efficiency, researcher starts to be dedicated to the visual redundancy estimation to image/video Research, just discernable distortion model (Just noticeable difference, JND) are exactly that a kind of energy Efficient Characterization vision is superfluous Remaining sensor model.
Which can be roughly divided into two types for the type of JND model: pixel domain JND model and DCT domain JND model.This patent stresses In the research to DCT domain JND model.Since traditional DCT domain JND model contrast masking sensitivity usually only takes into account smooth block, side Three kinds of block type bring visual masking effects of edge block and texture block ignore the influence that different complexity texture blocks generate, together When also have ignored contacting between the block of image and block.Therefore traditional DCT domain JND model is estimated visual redundancy accurate Degree needs to be further improved.
Summary of the invention
It is a primary object of the present invention to overcome drawbacks described above in the prior art, a kind of just discernable distortion model is proposed Threshold value calculation method further considers the possible different visual masking effects of the texture block of different complexities, and considers To contacting between image block and block, more accurate visual redundancy appraising model is proposed.
The present invention adopts the following technical scheme:
A kind of just discernable distortion model threshold value calculation method, which is characterized in that steps are as follows:
1) dct transform is carried out to the image of input；
2) DCT coefficient is operated, calculates brightness adaptation module value FLAWith spatial contrast sensitivity function module Value；
3) contrast is calculated to the type further division of texture block using 8 × 8DCT block frequency energy characteristic distributions Masking block value；
4) different frequency based on 8 × 8DCT block divides, and extracts the textural characteristics of current block, calculates current block and its The texture difference of his block acquires each piece of visual perception Dynamic gene；
5) brightness adaptation module value is integrated, spatial contrast sensitivity function module value, contrast masking sensitivity module value, and The visual perception Dynamic gene of different masses, obtains the JND threshold value of image.
The brightness adaptation module value and spatial contrast sensitivity function module value pass through following formula respectively and obtain:
Wherein, the value range of (i, j) expression 8 × 8DCT block coefficient positions, i and j are integer 0~7, FLARepresent brightness certainly Adapt to module value, FCSF(i, j) represents spatial contrast sensitivity function module value,Indicate the mean intensity of 8 × 8DCT block, φi And φjFor the normalization factor of dct transform, ωijRepresentation space frequency,For the orientation angle of corresponding DCT ingredient, a, b, c, R, s are constant.
According to the frequency distribution of 8 × 8DCT block coefficient, to the type further division of texture block, the contrast masking sensitivity mould Block is obtained by following formula:
Wherein ψ is the contrast masking sensitivity factor, C (n1,n2, i, j) be corresponding position DCT coefficient, (n1,n2) it is current The location index of DCT block in the picture, ε are set as 0.36.
The contrast masking sensitivity factor is obtained by following formula:
Wherein texture block ENERGY Et=E+H, E and H respectively indicate the sum of the region MF and the region HF DCT coefficient absolute value.
The visual perception Dynamic gene is obtained by following formula:
αk=1-Sk
Wherein SkFor k-th of DCT block shared weight size in the picture, the value range of k is integer Wherein W and H is respectively the width and height of input picture,Expression rounds up.
Shared weight is obtained the DCT block by following formula in the picture:
Wherein σ is Gauss model parameter, dklFor the Euclidean distance of k-th of DCT block and first of DCT block, DklIt is k-th The texture difference of DCT block and first of DCT block.
Texture difference DklIt is obtained by following formula:
Dkl=max (h (Tk,Tl),h(Tl,Tk))
Wherein T indicates the textural characteristics of DCT block, h (Tk,Tl) it is k-th of DCT block and first of DCT block textural characteristics Hausdorff distance.
The Hausdorff distance and DCT block textural characteristics of two difference DCT block textural characteristics are obtained by following formula:
T={ tLF,tMF,tHF}
Wherein tLF, tMFAnd tHFThe sum of the coefficient in the region respectively LF, MF and HF, | | | | indicate 2 norms.
The JND threshold value of the DCT domain is obtained by following formula:
JND threshold value=visual perception Dynamic gene × spatial contrast sensitivity function module value × brightness adaptation module value × contrast masking sensitivity module value.
By the above-mentioned description of this invention it is found that compared with prior art, the invention has the following beneficial effects:
1, the method for the present invention carries out more careful classification to texture block according to the frequency energy of DCT block, covers contrast The estimation covered more meets human-eye visual characteristic；
2, the method for the present invention considers contacting between image block and block, is extracted the textural characteristics of DCT block, passes through calculating The texture difference of current block and other blocks obtains the visual perception Dynamic gene of image block, is conducive to accurately estimating for visual redundancy Meter.
Detailed description of the invention
Fig. 1 is the main flow chart of the method for the present invention
Fig. 2 is the frequency distribution of 8 × 8DCT block.
Specific embodiment
Below by way of specific embodiment, the invention will be further described.
The present invention is to carry out more accurate estimation to visual redundancy, provides a kind of just discernable distortion model threshold value meter Calculation method, as shown in Figure 1, specific implementation step is as follows:
1) dct transform is carried out to the image of input.
2) DCT coefficient is operated, calculates brightness adaptation module value and spatial contrast sensitivity function module value.
3) contrast is calculated to the type further division of texture block using 8 × 8DCT block frequency energy characteristic distributions Masking block value.
4) different frequency based on 8 × 8DCT block divides, and extracts the textural characteristics of current block, calculates current block and its The texture difference of his block acquires each piece of visual perception Dynamic gene.
5) brightness adaptation module, spatial contrast sensitivity function module, contrast masking sensitivity module and different masses are integrated Weight size, obtain the JND threshold value of image.
Step 1) carries out dct transform to the image of input.
Step 2) calculates brightness adaptation module value FLAWith spatial contrast sensitivity function module value FCSF(i,j).Tool Body is as follows:
Wherein, the value range of (i, j) expression 8 × 8DCT block coefficient positions, i and j are integers 0~7,Indicate 8 × 8DCT The mean intensity of block, φiAnd φjFor the normalization factor of dct transform, ωijRepresentation space frequency,For corresponding DCT ingredient Orientation angle, a, b, c, r, s is constant, and value may respectively be 1.33,0.11,0.18,0.6 and 0.25.
Step 3) calculates contrast masking sensitivity module value.
Specifically, contrast masking sensitivity factor ψ is calculated, it is specific as follows:
Wherein texture block ENERGY Et=E+H, E and H respectively indicate the sum of the region MF and the region HF DCT coefficient absolute value.
Contrast masking sensitivity module value F is obtained by following formulaCM。
Wherein ψ is the contrast masking sensitivity factor, C (n1,n2, i, j) be corresponding position DCT coefficient, (n1,n2) it is current The location index of DCT block in the picture, and (i, j) is coordinate position of the DCT coefficient in current DCT block, ε is set as 0.36.
Step 4) calculates the perception Dynamic gene of each image block.
Specifically, passing through following 8 × 8DCT of formulas Extraction block textural characteristics T and two difference DCT block textural characteristics Hausdorff distance:
T={ tLF,tMF,tHF}
Wherein tLF, tMFAnd tHFThe sum of the coefficient in the region respectively LF, MF and HF, | | | | 2 norms of expression, k and l's takes Value range is integerWherein W and H is respectively the width and height of input picture,Expression rounds up.
Texture difference D is obtained by following formulakl:
Dkl=max (h (Tk,Tl),h(Tk,Tl))
Wherein h (Tk,Tl) be k-th of DCT block and first of DCT block textural characteristics Hausdorff distance.
DCT block shared weight in the picture is obtained by following formula:
Wherein SkFor k-th of DCT block shared weight size in the picture, σ is Gauss model parameter, is set as 5, dklIt is The Euclidean distance of k DCT block and first of DCT block, DklFor the texture difference of k-th of DCT block and first of DCT block.
Based on DCT block shared weight S in the picturek, the visual perception Dynamic gene of current block is calculated, specific as follows:
αk=1-Sk
Wherein SkFor k-th of DCT block shared weight size in the picture.The value range of k is integer Wherein W and H is respectively the width and height of input picture,Expression rounds up.
Step 5) obtains JND threshold value by following formula:
JND threshold value=visual perception Dynamic gene × spatial contrast sensitivity function module value × brightness adaptation module value × contrast masking sensitivity module value, it may be assumed that
JND(n1,n2, i, j) and=α (n1,n2)·FCSF(n1,n2,i,j)·FLA(n1,n2)·FCM(n1,n2,i,j)。
The above is only a specific embodiment of the present invention, but the design concept of the present invention is not limited to this, all to utilize this Design makes a non-material change to the present invention, and should all belong to behavior that violates the scope of protection of the present invention.

Claims (9)

1. a kind of just discernable distortion model threshold value calculation method, which is characterized in that steps are as follows:
1) dct transform is carried out to the image of input；
2) DCT coefficient is operated, calculates brightness adaptation module value FLAWith spatial contrast sensitivity function module value；
3) contrast masking sensitivity is calculated to the type further division of texture block using 8 × 8DCT block frequency energy characteristic distributions Module value；
4) different frequency based on 8 × 8DCT block divides, and extracts the textural characteristics of current block, calculates current block and other blocks Texture difference, acquire each piece of visual perception Dynamic gene；
5) brightness adaptation module value, spatial contrast sensitivity function module value, contrast masking sensitivity module value, and difference are integrated The visual perception Dynamic gene of block, obtains the JND threshold value of image.
2. a kind of just discernable distortion model threshold value calculation method according to claim 1, which is characterized in that the brightness Adaptation module value and spatial contrast sensitivity function module value pass through following formula respectively and obtain:
Wherein, the value range of (i, j) expression 8 × 8DCT block coefficient positions, i and j are integer 0~7, FLAIt is adaptive to represent brightness Module value, FCSF(i, j) represents spatial contrast sensitivity function module value,Indicate the mean intensity of 8 × 8DCT block, φiAnd φj For the normalization factor of dct transform, ωijRepresentation space frequency,For the orientation angle of corresponding DCT ingredient, a, b, c, r, s For constant.
3. a kind of just discernable distortion model threshold value calculation method according to claim 2, which is characterized in that according to 8 × The frequency distribution of 8DCT block coefficient, to the type further division of texture block, the contrast masking sensitivity module passes through following formula It obtains:
Wherein ψ is the contrast masking sensitivity factor, C (n1,n2, i, j) be corresponding position DCT coefficient, (n1,n2) it is current DCT block Location index in the picture, ε are set as 0.36.
4. a kind of just discernable distortion model threshold value calculation method according to claim 3, which is characterized in that the comparison Degree masking factor is obtained by following formula:
Wherein texture block ENERGY Et=E+H, E and H respectively indicate the sum of the region MF and the region HF DCT coefficient absolute value.
5. a kind of just discernable distortion model threshold value calculation method according to claim 1, which is characterized in that the vision Perception Dynamic gene is obtained by following formula:
αk=1-Sk
Wherein SkFor k-th of DCT block shared weight size in the picture, the value range of k is integer Wherein W and H is respectively the width and height of input picture,Expression rounds up.
6. a kind of just discernable distortion model threshold value calculation method according to claim 5, which is characterized in that the DCT Shared weight is obtained block by following formula in the picture:
Wherein σ is Gauss model parameter, dklFor the Euclidean distance of k-th of DCT block and first of DCT block, DklFor k-th DCT block with The texture difference of first of DCT block.
7. a kind of just discernable distortion model threshold value calculation method according to claim 6, which is characterized in that texture difference DklIt is obtained by following formula:
Dkl=max (h (Tk,Tl),h(Tl,Tk))
Wherein T indicates the textural characteristics of DCT block, h (Tk,Tl) it is k-th of DCT block and first of DCT block textural characteristics Hausdorff distance.
8. a kind of just discernable distortion model threshold value calculation method according to claim 7, which is characterized in that two differences The Hausdorff distance and DCT block textural characteristics of DCT block textural characteristics are obtained by following formula:
T={ tLF,tMF,tHF}
Wherein tLF, tMFAnd tHFThe sum of the coefficient in the region respectively LF, MF and HF, | | | | indicate 2 norms.
9. a kind of just discernable distortion model threshold value calculation method according to claim 1, which is characterized in that the DCT The JND threshold value in domain is obtained by following formula:
JND threshold value=visual perception Dynamic gene × spatial contrast sensitivity function module value × brightness adaptation module value × right Than degree masking block value.
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CN112866820A (en) * 2020-12-31 2021-05-28 宁波大学科学技术学院 Robust HDR video watermark embedding and extracting method and system based on JND model and T-QR and storage medium
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CN109982022A (en) * 2019-04-17 2019-07-05 南京大学 The video refreshing method of minimum color difference can be examined based on human eye
CN110062234A (en) * 2019-04-29 2019-07-26 同济大学 A kind of perception method for video coding based on the just discernable distortion in region
CN112634278A (en) * 2020-10-30 2021-04-09 上海大学 Superpixel-based just noticeable distortion model
CN112634278B (en) * 2020-10-30 2022-06-14 上海大学 Super-pixel-based just noticeable distortion method
CN112437302A (en) * 2020-11-12 2021-03-02 深圳大学 JND prediction method and device for screen content image, computer device and storage medium
CN112584153A (en) * 2020-12-15 2021-03-30 深圳大学 Video compression method and device based on just noticeable distortion model
CN112866820A (en) * 2020-12-31 2021-05-28 宁波大学科学技术学院 Robust HDR video watermark embedding and extracting method and system based on JND model and T-QR and storage medium
CN112866820B (en) * 2020-12-31 2022-03-08 宁波大学科学技术学院 Robust HDR video watermark embedding and extracting method and system based on JND model and T-QR and storage medium
CN112967229A (en) * 2021-02-03 2021-06-15 杭州电子科技大学 Method for calculating just noticeable distortion threshold based on video perception characteristic parameter measurement
CN112967229B (en) * 2021-02-03 2024-04-26 杭州电子科技大学 Method for calculating just-perceived distortion threshold based on video perception characteristic parameter measurement
CN113192083A (en) * 2021-05-07 2021-07-30 宁波大学 Texture masking effect-based image just noticeable distortion threshold estimation method
CN113192083B (en) * 2021-05-07 2023-06-06 宁波大学 Image just noticeable distortion threshold estimation method based on texture masking effect

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