CN103475881B - The image JND threshold value computational methods of view-based access control model attention mechanism in DCT domain - Google Patents
The image JND threshold value computational methods of view-based access control model attention mechanism in DCT domain Download PDFInfo
- Publication number
- CN103475881B CN103475881B CN201310413594.4A CN201310413594A CN103475881B CN 103475881 B CN103475881 B CN 103475881B CN 201310413594 A CN201310413594 A CN 201310413594A CN 103475881 B CN103475881 B CN 103475881B
- Authority
- CN
- China
- Prior art keywords
- image
- threshold value
- block
- jnd
- factor
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
Landscapes
- Compression Or Coding Systems Of Tv Signals (AREA)
- Image Processing (AREA)
Abstract
The image JND threshold value computational methods of view-based access control model attention mechanism in a kind of DCT domain.The present invention proposes two kinds of schemes significance and block sort combined, a kind of is that the masking factor of the visual attention masking factor with a single point and block sort combines according to point-to-point mode, another kind is to represent the significance of whole piece with the average significance of each piece, is then combined according to the mode of block to block by the masking factor of visual attention masking factor based on each piece and block sort.Use the comprehensive calculated value of contrast masking sensitivity function that traditional JND threshold value is modulated, finally give JND threshold value more accurately.Both of which can be effectively improved the accuracy of JND threshold value, so that JND threshold value and human visual system are more mated.The model that the image JND threshold value computational methods that the present invention proposes realize can accommodate more noise, and in terms of PSNR, model averagely can improve 0.54DB.
Description
Technical field
The present invention relates to image/video coding technical field.
Technical background
Traditional image/video coding technology is carried out mainly for spatial domain redundancy, time domain redundancy and statistical redundancy
Compressed encoding, but seldom in view of human visual system's characteristic and psychological effect, therefore a large amount of visual redundancy data are encoded also
Transmission, in order to improve the efficiency of coding further, research worker has started the research being devoted to remove visual redundancy.Mesh is previous
The effective ways of characterization of visual redundancy are namely based on the minimum of psychology and physiology and perceive distortion model, are called for short JND model,
The most discernable imperceptible change of distortion model, i.e. human eye, due to the various screen effects of human eye, human eye can only
Perceive the noise exceeding a certain threshold value, this threshold value be exactly human eye just can perceive distortion, represent the visual redundancy degree in image.
JND model is commonly used to guide image or the perceptual coding of video and process, such as pretreatment, adaptive quantizing, bite rate control, motion
Estimate.
Existing discernable distortion (JND) model can be roughly divided into two classes: the first kind is pixel domain JND model, its base
Present principles is to model by characterizing brightness self adaptation effect and texture masking effect mostly, such as document 1 (see X.Yang,
W.Lin,Z.Lu,E.P.Ong,and S.Yao,“Just-noticeable-distortion profile with
nonlinear additivity model for perceptual masking color images”,IEEE
Trans.Circuits Syst.Video Technol., vol.15, no.6, pp742-752, Jun.2005) in propose base
In the coloured image JND model in spatial domain, but owing to it cannot be well by contrast sensitivity function (Contrast
Sensitive Function, CSF) integrate, therefore this class model has no idea to obtain accurate JND value, frequently as meter
The fast method calculating JND threshold value uses.
Equations of The Second Kind JND model is subband JND model, and this class model is to calculate in the transform domain as illustrated, such as DCT domain, little
Wave zone, CONTOURLET territory etc..Owing to (such as JPEG, H.261/3/ most of image/video coding standards are all based on DCT domain
4, MPEG-1/2/4), therefore JND model based on DCT domain has obtained the concern of Many researchers, such as document 2 and (has seen
Z.Wei and K.N.Ngan,“Spatial just noticeable distortion profile for image in
DCT domain, " In Proc.IEEE Int.Conf.Multimeda and Expo, pp.925-928,2008.) middle combination
The brightness adaptive characteristic of image, spatial contrast degree effect and contrast shielding effect based on block sort, but this model
The not visual attention mechanism of the consideration human eye impact on JND model, therefore computational accuracy needs to be improved further.
Summary of the invention
On the basis of the model of WEI, the present invention combines visual attention mechanism and proposes image in a new DCT domain
JND model modeling method, by considering vision attention stress effect and contrast masking sensitivity effect have devised a kind of comprehensive
The method that spatial contrast degree sensitivity function is modulated together with brightness self adaptation effect by modulation function.
To this end, the present invention provides technical scheme enforcement step it is:
A kind of image method for computing perceptible distortion based on DCT domain, adopts the following technical scheme that, including following step
Rapid:
Step S1: selected image is carried out the dct transform of 8x8, by its by spatial transform to DCT domain.
Step S2: in DCT domain, calculates according to the product of spatial contrast degree effect threshold value and the brightness Adaptive Modulation factor and obtains
The basic perceptible distortion JND value obtained.
Step S3: utilize canny edge detector that image is carried out piecemeal, be divided into smooth block, edge block and texture block,
To based on the block structured contrast masking sensitivity factor.
Step S4: utilize visual attention model that image is carried out significance detection, obtains the notable figure of image.
Step S5: split image according to the notable figure of step S4 gained, is divided into marking area and non-aobvious by image
Write region, be then based on the saliency value of each point, obtain the contrast masking sensitivity factor of view-based access control model attention mechanism.
Or step S5 is: first divide the image into into marking area and non-significant region, then to step S4 gained
Notable figure piecemeal, replaces the saliency value of whole piece, and saliency value based on each piece obtains by the meansigma methods of the saliency value of each piece
The contrast masking sensitivity factor to view-based access control model attention mechanism.
Step S6: the salient region of image that step S5 is obtained and the segmentation result in non-significant region and step S3 gained
Block sort result combines, and image carries out more careful piecemeal, and by the contrast masking sensitivity of view-based access control model attention mechanism
The factor and combining according to linear relationship based on the block structured contrast masking sensitivity factor, obtains comprehensive contrast masking sensitivity modulation
Function,
Step S7: calculated to step S2 for calculated for the step S6 modulation function value basic threshold value of JND is carried out
Modulation, obtains final JND threshold value.
The key technology main points that technique scheme embodies:
1, just perceiving distortion model for traditional images and do not account for this problem of visual attention mechanism, the present invention carries
Go out the image of view-based access control model attention mechanism in two kinds of DCT domain and just perceived the modeling algorithm of distortion model, Yi Zhongshi, passed through
Calculate visual attention modulation factor based on image significance, and by this visual attention masking factor based on pixel and
Combine based on the block structured contrast masking sensitivity factor, be calculated comprehensive contrast masking sensitivity function, to traditional based on sky
Between the JND threshold value of contrast effect and brightness self adaptation effect be modulated；The second is, by calculating the notable figure of image,
And the significance factor of whole piece is replaced by the significance meansigma methods of each piece, then set up block-based visual attention and shelter
The factor and combining based on the block structured contrast masking sensitivity factor, obtains comprehensive contrast masking sensitivity function to basic JND threshold
Value is modulated.Both of which can be effectively improved the accuracy of JND threshold value, so that JND threshold value and human eye vision system
System more mates.
2, the present invention proposes the concept of visual attention masking effect, by visual saliency simulation human eye to image slices
The attention rate of vegetarian refreshments, thus set up the visual attention masking effect factor of view-based access control model significance.
3, the present invention proposes two kinds of schemes significance and block sort combined, and a kind of is with the vision note of a single point
The masking factor of meaning power masking factor and block sort combines according to point-to-point mode, and another kind is average aobvious with each piece
Work degree represents the significance of whole piece, then by visual attention masking factor based on each piece and the masking factor of block sort
Combine according to the mode of block to block.
4, by visual saliency and block sort being combined, image is carried out more fine and piecemeal accurately.
5, significant properties based on each piece and block structure characteristic, by arranging different significance modulation factors and agllutination
Structure modulation factor, thus visual attention masking factor and the block sort contrast masking sensitivity factor are combined, obtain comprehensive
Contrast masking sensitivity function.
Having the beneficial effect that of the inventive method uses the comprehensive calculated value of contrast masking sensitivity function to traditional
JND threshold value is modulated, and finally gives JND threshold value more accurately.On the premise of ensureing same vision subjective quality, this
The model that the image JND threshold value computational methods that invention proposes realize can accommodate more noise.
Accompanying drawing explanation
Fig. 1 be DCT domain of the present invention based on view-based access control model attention mechanism image perceptible distortion model framework chart.
Fig. 2 is present example Airplane image.
Fig. 3 is that present example Airplane image carries out the image after block sort.
Fig. 4 is that present example Airplane image carries out the image after significance classification.
Fig. 5 is that present example Airplane image synthesis considers significance and block structured careful piecemeal result.
Fig. 6 is the image JND threshold value computational methods flow chart of view-based access control model attention mechanism in DCT domain of the present invention.
Detailed description of the invention
The invention will be further described to combine accompanying drawing with instantiation below:
The example employing MATLAB7 that the present invention provides is as Simulation Experimental Platform, with the bmp gray level image of 512 × 512
Airplane as selected test image, describes this example in detail below in conjunction with each step:
Step (1), the bmp gray level image of selected 512 × 512, as the image of input test, is carried out the DCT of 8 × 8
Conversion, by its by spatial transform to DCT domain；
Step (2), in DCT domain, calculates according to the product of the basic threshold value of spatial contrast degree and the brightness Adaptive Modulation factor
Acquisition can perceive distortion JND value, and its computing formula is as follows:
TJND(n, i, j)=TBasic(n,i,j)×Flum(n) (1)
Wherein, TBasic(n, i j) represent spatial contrast degree sensitivity threshold, FlumN () represents the brightness self-adaptative adjustment factor, n
For the index of DCT block,W is the width of image, and H is the height of image, and i, j are the rope of coefficient in DCT block
Draw, 1≤i≤64,1≤j≤64.General DCT block is all 8x8 size, and so this test image is just divided into
Individual DCT block, n takes numerical value between 1 to 4096, and i, j take numerical value between 1 to 64.
Basic threshold value T in above formula (l)Basic(n, i, j) adopt and calculate acquisition with the following method:
The frequency of DCT subband can be expressed as follows:
Wherein θx=θy=2 arctan (γ/2 l) are the horizontal and vertical visual angles of a pixel, and l is the observation of image
Distance, in the invention, l is 3 times of picture traverse, and γ represents on display width or the length that a pixel shows.By
Upper described, the spatial contrast degree sensitivity threshold of DCT block is:
In formula (3): behalf spatial aggregation effect, take 0.25 in the invention,Represent effect
Should, wherein r=0.6, Фi、ФjIt is DCT normalization coefficient,Represent the deflection of corresponding DCT coefficient:
Parameter a=l.33, b=0.11, c=0.005.
Brightness Adaptive Modulation factor F in above formula (1)lumN () is adopted and is calculated acquisition with the following method:
In formula (6), I represents each 8 × 8 pieces of big average brightness values.
Then according to formula (1) is calculated traditional JND threshold value.
Step (3), carries out block sort to image, calculates contrast masking sensitivity factor F based on block sortcontrast(n,i,j)。
All 8 × 8 size DCT blocks in image are classified, is divided into texture, smooth and edge three class (such as Fig. 2 institute
Show)；The marginal information calculated in image is detected, by the edge pixel density in calculating each 8 × 8 pieces with canny operator
ρedgeClassifying block, detailed process is as follows:
ρedge=(∑8X8edge)/N2 (7)
Wherein, ρedgeRepresent the sum of the edge pixel obtained in each 8X8 block by canny operator；N is that DCT block is big
Little, take N=8 in this example；
The sorting technique of block is:
From the DCT block type of 8 × 8, between block, the masking effect factor is:
It is also contemplated that the masking effect between adjacent sub-bands, traditional contrast masking sensitivity function expression based on block sort is such as
Under:
Step (4), carries out significance detection to image, obtains the notable figure of image.
Utilize spectrum residual error method that image carries out significance detection in this example:
Wherein, the phase spectrum after P (f) and R (f) representative image Fourier transformation respectively and amplitude spectrum,Represent in Fu
Leaf inverse transformation, g (σ) represents Gaussian filter, and σ is filtering window size, and in this example, σ takes 0.32 times of picture traverse.
S (x) represents the notable figure of each image.
Step (5), if selection scheme one, then describes with reference to (a), if selection scheme two, then describes with reference to (b):
(a) notable figure based on image, the significance contrast masking sensitivity factor of calculating image:
Fvs(n, i, j)=μ (Smax-S(n,i,j)) (12)
The notable angle value the biggest explanation human eye of image is the highest to the attention rate of this point, wherein SmaxRepresent notable figure normalization
Maximum afterwards, (n, i j) represent after notable figure normalization correspondence position (i, significance j) in each piece at each to S.
μ represents modulation factor, takes μ=1.0 in this example.
B () notable figure based on image, represents the significance of whole piece by the significance meansigma methods of each piece.And based on often
The significance of individual block, the significance contrast masking sensitivity factor of calculating image:
Fvs(n)=μ (Smax(n)-S(n)) (13)
Wherein, SmaxN () represents the maximum in the significance of all pieces, S (n) represents the significance of each piece.μ represents
Modulation factor, takes μ=1.0 in this example.FvsN () represents the significance masking factor of each piece.
Step (6), comprehensively in terms of block structure angle and significance angle two, image is carried out more careful dividing
Class.
It is first according to the significance of image and image is divided into notable district and non-significant region (as shown in Figure 3):
Wherein T represents segmentation threshold, and T is the average of notable figure in this example.
In conjunction with significance segmentation result and block sort result, image is carried out finer classification, as shown in Figure 4.
Step (7), for different block structures, arranges different significance Dynamic gene and block structure Dynamic gene, from
And calculate comprehensive modulation function:
In formula (15), according to scheme one, then FvsRepresent use formula (12) and calculate the significance masking factor of gained, if adopting
With scheme two, then FvsRepresent use formula (13) and calculate the significance masking factor of gained.Fcontrast(n, i, j) representative formula (10)
Calculated based on the block structured contrast masking sensitivity factor.Significance modulation factor α and the value base of block structure Dynamic gene β
In different block structures:
Step (8), based on the basic threshold value of step (2) calculated JND, utilizes step (7) to be calculated comprehensive tune
Function processed, threshold value basic to JND is modulated:
Comprehensive all above step is calculated the JND threshold value of image, and this threshold value has considered spatial contrast degree effect,
Brightness self adaptation effect, block sort contrast masking sensitivity effect and visual attention mechanism, so this threshold value and the vision of human eye
System is more identical, more accurately.
The innovative point of the present invention:
Propose and image significance is applied to improves image just perceives the thinking of distortion model.
Proposing visual attention masking factor based on image significance, this factor describes the human eye pass to image
Note degree.
Consider significant characteristics and the block structure feature of image, image is carried out more accurate and careful classification.
Significant properties based on different masses and block structure characteristic consider, and construct comprehensive modulation function to tradition
JND model be modulated, be calculated the JND threshold value of the visual system that more coincide.
Claims (1)
1. an image method for computing perceptible distortion based on DCT domain, comprises the following steps:
Step S1: selected image is carried out the dct transform of 8x8, by its by spatial transform to DCT domain；
Step S2: in DCT domain, calculate acquisition according to the product of spatial contrast degree effect threshold value and the brightness Adaptive Modulation factor
Basic perceptible distortion JND value；
Step S3: utilize canny edge detector that image is carried out piecemeal, be divided into smooth block, edge block and texture block, obtain base
In the block structured contrast masking sensitivity factor；
Step S4: utilize visual attention model that image is carried out significance detection, obtains the notable figure of image；
Step S5:
According to the notable figure of step S4 gained, image is split, image is divided into marking area and non-significant region, then
Saliency value based on each point, obtains the contrast masking sensitivity factor of view-based access control model attention mechanism；
Or step S5 is: first divide the image into into marking area and non-significant region, then notable to step S4 gained
Figure piecemeal, replaces the saliency value of whole piece by the meansigma methods of the saliency value of each piece, and saliency value based on each piece obtains base
The contrast masking sensitivity factor in visual attention mechanism；
Step S6: salient region of image step S5 obtained and the block of the segmentation result in non-significant region and step S3 gained divide
Class result combines, and image carries out more careful piecemeal, and by the contrast masking sensitivity factor of view-based access control model attention mechanism
Combine according to linear relationship with based on the block structured contrast masking sensitivity factor, obtain comprehensive contrast masking sensitivity modulation letter
Number；
Step S7: by calculated for the step S6 contrast masking sensitivity modulation function value basic threshold of JND calculated to step S2
Value is modulated, and obtains final JND threshold value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310413594.4A CN103475881B (en) | 2013-09-12 | 2013-09-12 | The image JND threshold value computational methods of view-based access control model attention mechanism in DCT domain |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310413594.4A CN103475881B (en) | 2013-09-12 | 2013-09-12 | The image JND threshold value computational methods of view-based access control model attention mechanism in DCT domain |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103475881A CN103475881A (en) | 2013-12-25 |
| CN103475881B true CN103475881B (en) | 2016-11-23 |
Family
ID=49800559
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201310413594.4A Expired - Fee Related CN103475881B (en) | 2013-09-12 | 2013-09-12 | The image JND threshold value computational methods of view-based access control model attention mechanism in DCT domain |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103475881B (en) |
Families Citing this family (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104219525B (en) * | 2014-09-01 | 2017-07-18 | 国家广播电影电视总局广播科学研究院 | Perception method for video coding based on conspicuousness and minimum discernable distortion |
| CN105491391A (en) * | 2014-09-15 | 2016-04-13 | 联想(北京)有限公司 | Image compression method and electronic equipment |
| CN104754320B (en) * | 2015-03-27 | 2017-05-31 | 同济大学 | A kind of 3D JND threshold values computational methods |
| CN109525847B (en) * | 2018-11-13 | 2021-04-30 | 华侨大学 | Just noticeable distortion model threshold calculation method |
| CN109948699B (en) * | 2019-03-19 | 2020-05-15 | 北京字节跳动网络技术有限公司 | Method and device for generating feature map |
| CN109948700B (en) * | 2019-03-19 | 2020-07-24 | 北京字节跳动网络技术有限公司 | Method and device for generating feature map |
| CN109902763B (en) * | 2019-03-19 | 2020-05-15 | 北京字节跳动网络技术有限公司 | Method and device for generating feature map |
| CN110251076B (en) * | 2019-06-21 | 2021-10-22 | 安徽大学 | Method and device for detecting significance based on contrast and fusing visual attention |
| CN112437302B (en) * | 2020-11-12 | 2022-09-13 | 深圳大学 | JND prediction method and device for screen content image, computer device and storage medium |
| CN112435188B (en) * | 2020-11-23 | 2023-09-22 | 深圳大学 | JND prediction method and device based on direction weight, computer equipment and storage medium |
| CN114666619B (en) * | 2022-03-11 | 2024-05-03 | 平安国际智慧城市科技股份有限公司 | Watermarking method, device and equipment for video file and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101621708A (en) * | 2009-07-29 | 2010-01-06 | 武汉大学 | Method for computing perceptible distortion of color image based on DCT field |
| CN101710995A (en) * | 2009-12-10 | 2010-05-19 | 武汉大学 | Video coding system based on vision characteristic |
| CN102750706A (en) * | 2012-07-13 | 2012-10-24 | 武汉大学 | Depth significance-based stereopicture just noticeable difference (JND) model building method |
| CN102905130A (en) * | 2012-09-29 | 2013-01-30 | 浙江大学 | Multi-resolution JND (Just Noticeable Difference) model building method based on visual perception |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR100750138B1 (en) * | 2005-11-16 | 2007-08-21 | 삼성전자주식회사 | Method and apparatus for image encoding and decoding considering the characteristic of human visual system |
-
2013
- 2013-09-12 CN CN201310413594.4A patent/CN103475881B/en not_active Expired - Fee Related
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101621708A (en) * | 2009-07-29 | 2010-01-06 | 武汉大学 | Method for computing perceptible distortion of color image based on DCT field |
| CN101710995A (en) * | 2009-12-10 | 2010-05-19 | 武汉大学 | Video coding system based on vision characteristic |
| CN102750706A (en) * | 2012-07-13 | 2012-10-24 | 武汉大学 | Depth significance-based stereopicture just noticeable difference (JND) model building method |
| CN102905130A (en) * | 2012-09-29 | 2013-01-30 | 浙江大学 | Multi-resolution JND (Just Noticeable Difference) model building method based on visual perception |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103475881A (en) | 2013-12-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103475881B (en) | The image JND threshold value computational methods of view-based access control model attention mechanism in DCT domain | |
| Gao et al. | Image quality assessment based on multiscale geometric analysis | |
| Stamm et al. | Anti-forensics of digital image compression | |
| Wan et al. | A novel just noticeable difference model via orientation regularity in DCT domain | |
| Masry et al. | A scalable wavelet-based video distortion metric and applications | |
| CN101621709B (en) | Method for evaluating objective quality of full-reference image | |
| CN104378636B (en) | A kind of video encoding method and device | |
| CN108921800A (en) | Non-local mean denoising method based on form adaptive search window | |
| CN103313047B (en) | A kind of method for video coding and device | |
| Stamm et al. | Wavelet-based image compression anti-forensics | |
| US8260067B2 (en) | Detection technique for digitally altered images | |
| CN103200421A (en) | No-reference image quality evaluation method based on Curvelet transformation and phase coincidence | |
| CN106550244A (en) | The picture quality enhancement method and device of video image | |
| Cheng et al. | Image quality assessment using natural image statistics in gradient domain | |
| Li et al. | A natural image quality evaluation metric | |
| Baviskar et al. | Performance evaluation of high quality image compression techniques | |
| CN108648180A (en) | A kind of full reference picture assessment method for encoding quality of view-based access control model multiple characteristics depth integration processing | |
| Wu et al. | An improved model of pixel adaptive just-noticeable difference estimation | |
| Abd-Elhafiez | Image compression algorithm using a fast curvelet transform | |
| Albanesi et al. | An HVS-based adaptive coder for perceptually lossy image compression | |
| CN109300086B (en) | Image blocking method based on definition | |
| Zhang et al. | Local binary pattern statistics feature for reduced reference image quality assessment | |
| Kulkarni et al. | Multiresolution Analysisfor Medical Image Segmentation Using Wavelet Transform | |
| Taujuddin et al. | Image compression using wavelet-based thresholding technique for human visual system (HVS) improvement | |
| Al-Jawad et al. | Wavelet based image quality self measurements |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20161123 Termination date: 20190912 |