CN109345552A - Stereo image quality evaluation method based on region weight - Google Patents
Stereo image quality evaluation method based on region weight Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/50—Image enhancement or restoration by the use of more than one image, e.g. averaging, subtraction
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/168—Segmentation; Edge detection involving transform domain methods
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20212—Image combination
- G06T2207/20221—Image fusion; Image merging
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30168—Image quality inspection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30204—Marker
Abstract
The stereo image quality evaluation method based on region weight that the present invention relates to a kind of, comprising: handle left distortion map and right distortion map using Canny edge detection method, obtain left detection figure and right detection figure；It is defined as binocular region and monocular region, while weight information is arranged to different zones in the present invention；For the region weight mass fraction Q on the basis of calculating image content-basedIW‑SSIM；For binocular region, the region weight mass fraction Q on the basis of stereoscopic vision is calculatedSW‑SSIM；Region weight mass fraction Q on the basis of comprehensive image content-basedIW‑SSIMWith the region weight mass fraction Q on the basis of stereoscopic visionSW‑SSIM, obtain final stereo image quality evaluation result.
Description
Technical field
The invention belongs to field of image processings, are related to stereo image quality evaluation method.
Background technique
The application range of stereo-picture is very extensive, and the development for fields such as 3D film, virtual realities is laid a good foundation.So
And stereo-picture will will receive the interference of factors in acquisition, transmission and playing process, so that data distortion is caused, shadow
Ring final experience perception.So how to evaluate stereo image quality is a problem to be solved.In passing research
In, stereo-picture evaluation can usually be summarized as subjective assessment and objectively evaluate two classifications.Current subjective evaluation method
Vulnerable to the interference of many factors, and it is time-consuming and laborious, evaluation result is also not sufficiently stable.Opposite subjective assessment, objectively evaluates with software
Mode evaluate the quality of image, while being not required to participant and a large amount of subjective test, it is easy to operate, and with subjective assessment height
Correlation, increasingly by the concern of correlative study person.
The research of three-dimensional image objective quality evaluation at present more to be focused in the selection of homing method, deep learning class method
Its application in terms of stereo image quality objectively evaluates research has been pushed in the success of other field.So depth conviction net
Network, convolutional neural networks are all used in this field from code machine and cyclic convolution neural network, and achieve centainly into
Exhibition.However, stereo-picture substantially depends on binocular parallax during display, can feature extraction simulate binocular parallax in people
Effect in eye stereoscopic vision access is very crucial.
Based on considerations above, the present invention it is quasi- in the way of traditional image procossing linearity test to the region of stereo-picture into
Row segmentation, thus to different zones use different feature extraction modes, and then improve save those be able to reflect binocular parallax
Correlated characteristic, stereo image quality is made and is objectively evaluated.
[1]Y.H.Lin,J.L.Wu.Quality assessment of stereoscopic 3D image
compression by binocularintegrationbehaviors.IEEETransactions
onImageProcessing,23(4):1527.2014.
[2]F.Shao,W.Tian,W.Lin.Learning sparse representation for no-
reference quality assessment of multiply-distorted stereoscopic images.IEEE
Transactions on Multimedia,19(8):1821-18362017.
[3]G.Yue,C.Hou,Q.Jiang.Blind Stereoscopic 3D Image Quality Assessment
viaAnalysis ofNaturalness,Structure,andBinocularAsymmetry.SignalProcessing,
150:204-214,2018.
Summary of the invention
It is an object of the invention to establish the stereo image quality evaluation method for fully considering sub-region right mode.This
The three-dimensional image objective quality evaluation method proposed is invented, divides the image into binocular region and monocular area using Hough transformation method
Domain, so that subregion carries out feature extraction.Traditional SSIM method is used in different regions, and then respectively in the area Liang Ge
SSIM value is obtained in domain.At the same time, the IW-SSIM towards the monocular region and SW-SSIM towards binocular region is counted respectively
It calculates, obtains the final result of stereo image quality evaluation eventually by the method in pond.At the same time, the present invention, which devises, meets
Human visual system is to the score convergence strategy of stereo-picture reception process, to make accurately and objective appraisal.Technical side
Case is as follows:
A kind of stereo image quality evaluation method based on region weight, each distorted image is to by left distortion mapWith
Right distortion mapComposition, and its corresponding reference picture to by left with reference to figureWith right with reference to figureComposition, resolution ratio are equal
For h*w, including the following steps:
1) left distortion map is handled using Canny edge detection method firstWith right distortion mapThen to two width figures
Non-zero region as in is marked, complete using k-means clustering method for the pixel in all labeled non-zero regions
At the selection of central point.If a central point is most adequately supported that is be individually closed is non-by the non-zero pixel of this non-zero region
Null range pixel number is greater than the 3% of whole pixel numbers, and to the center selected greater than 3%*min (h, w) a pixel away from
From then it will be retained；All candidate points are handled using the mode of straight line fitting, obtain final straight line
H (I is schemed in the left detection of testing resultleft) and right detection figure H (Iright)；
2) H (I is schemed with left detectionleft) and right detection figure H (Iright) based on, the straight line that is marked and with the linear pixel
All pixels of the distance less than 5%*min (h, w) are defined as binocular region, and other regional natures are monocular region, simultaneously
Weight information is arranged to different zones in the present invention；For binocular region, weight is arranged to each pixelFor monocular region, weight is arranged in each pixel
Wherein d is the linear pixel distance apart from nearest binocular region；
3) for monocular region, it is divided to or so two groups similarly to be handled, for left distortion mapWith left with reference to figure
It handles according to the following formula:
Wherein, l (x, y), c (x, y) and s (x, y) use formula (2) respectively, and the method for formula (3) and formula (4) carries out
It calculates, in addition, α=0.4, β=0.3, γ=0.3；
In this calculating process, C1, C2And C3It is set as non-zero value, thus according to left distortion mapWith left with reference to figureMeter
Calculate the score Q based on content weight towards left figureIW-SSIM1；Same method is used in right distortion mapWith right reference
FigureOn, obtain corresponding scores QIW-SSIM2；And the mean value of the two is defined the region weight matter on the basis of image content-based
Measure score QIW-SSIM；
4) for binocular region, left distortion map and right distortion map are merged, obtain distortion cyclopean figureSimultaneously will
It is left with reference to figure and right with reference to figure fusion, it obtains scheming with reference to cyclopeanBased on the region segmentation result in the first step, according to
3) method pairWithWeight SSIM calculating is carried out, the region weight mass fraction on the basis of stereoscopic vision is obtained
QSW-SSIM；
5) the region weight mass fraction Q on the basis of comprehensive image content-basedIW-SSIMWith on the basis of stereoscopic vision
Region weight mass fraction QSW-SSIM, obtain final stereo image quality evaluation result:
Q=ω QIW-SSIM+(1-ω)·QSW-SSIM (5)
Wherein, ω=0.6527.
Three-dimensional image objective quality evaluation method proposed by the invention fully considers image binocular region and monocular region
Difference, preferably simulate human visual system when receiving stereo-picture to the different approaches in two class regions.Meanwhile this hair
It is bright to extract different classes of feature for different zones, and then obtain final three-dimensional image objective quality using the method for weighting and comment
Valence result.Stereoscopic image processing method employed in the present invention is simple, have stronger practicability, can computing capability compared with
It is operated in low equipment.From the results of view, the method applied in the present invention can preferably predict three-dimensional image objective matter
Amount, maintains very high consistency with subjective evaluation result, meets functional need.
Detailed description of the invention
Fig. 1 overall flow figure of the present invention
Binocular region detection process schematic of the Fig. 2 based on Hough transformation
Fig. 3 IW-SSIM and IW-PSNR intermediate result figure
Specific embodiment
Stereo image quality evaluation method based on region weight of the invention, each distorted image is to by left distortion mapWith right distortion mapComposition, and its corresponding reference picture by left with reference to figureWith right with reference to figureComposition.Resolution ratio
It is h*w.Evaluation method the following steps are included:
Step 1: to left distortion mapWith right distortion mapThe straight-line detection based on Hough transformation H is used respectively, point
The left detection figure by straight-line detection is not obtainedScheme with right detectionCalculating process is as follows.
First using Canny edge detection method to left distortion mapWith right distortion mapThen in two images
Non-zero region be marked, for the pixel in all labeled non-zero regions, completed using k-means clustering method
The selection of heart point.If a central point is most adequately supported that (regional area pixel number is greater than all by the non-zero pixel in sub-region
The 3% of pixel number), and the center selected to early period is greater than 3%*min (h, w) a pixel distance, then it will be protected
It stays.Finally, the mode using straight line fitting handles all candidate points, the final left inspection of straight-line detection result is obtained
Mapping H (Ileft) and right detection figure H (Iright)。
Step 2: scheming H (I with left detectionleft) and right detection figure H (Iright) based on, the straight line that is marked and straight with this
All pixels of the line pixel distance less than 5%*min (h, w) are defined as binocular region, and other regional natures are monocular area
Domain, while weight information is arranged to different zones in the present invention.For binocular region, weight is arranged to each pixelFor monocular region, weight is arranged in each pixel
Wherein d is the linear pixel distance apart from nearest binocular region.
Step 3: for monocular region, two groups will be divided to similarly to be handled or so, with left distortion mapWith left reference
FigureFor:
Wherein, l (x, y), c (x, y) and s (x, y) use formula (2) respectively, and the method for formula (3) and formula (4) carries out
It calculates, in addition, α=0.4, β=0.3, γ=0.3.
In this calculating process, ωiPixel region weighted value in step 2, and C1, C2And C3Setting
For non-zero value.It in this way can be according to left distortion mapWith left with reference to figureCalculate the score based on content weight towards left figure
QIW-SSIM1.Same method can be used in right distortion mapWith right with reference to figureOn, obtain corresponding scores QIW-SSIM2。
And the mean value of the two can be defined as QIW-SSIM。
Step 4: left distortion map and right distortion map is merged for binocular region, obtain distortion cyclopean figure
It is with reference to figure and right with reference to figure fusion by left simultaneously, it obtains scheming with reference to cyclopeanBased on the region segmentation knot in the first step
Fruit, it is rightWithCarry out weight SSIM calculating.
Wherein, l (x, y), c (x, y) and s (x, y) are still followed formula (2), the calculation of formula (3) and formula (4),
ωiPixel region weighted value in step 2, and C1, C2And C3Be set as non-zero value.Finally obtain this step
Final result QSW-SSIM。
Step 5: based on the basis of third step picture material region weight mass fraction and the 4th step stereoscopic vision be base
Quasi- region weight mass fraction, the present invention obtain final stereo image quality evaluation result for both comprehensive.
Q=ω QIW-SSIM+(1-ω)·QSW-SSIM (6)
By test, the present invention uses ω=0.6527 for coefficient in above formula.
Step 6: choosing database.For the stereo-picture Quality of experience objective quality scores for proving the method for the present invention acquisition
There is very high consistency with subjective quality scores, the method for the present invention is tested on LIVE database.This database is divided
For two word banks of LIVEI and LIVEII, wherein the library LIVEI is symmetrical library, and the library LIVEII is asymmetric library.Specifically, with
Based on 20 different stereo scenes, the library LIVEI shares 365 symmetrical distortion stereo pairs (i.e. left and right figure type of distortion
It is consistent with degree), the library LIVEII then has 360 asymmetric distorted images to (i.e. left and right figure type of distortion and degree are inconsistent).
It altogether include five seed type of JPEG, JP2K, WN, FF and Blur from type of distortion.
Take 4 in the world commonly measure Objective image quality evaluation algorithms index evaluation the method for the present invention performance, 4
A index be respectively Pearson's linearly dependent coefficient (Pearson linear correlation coefficient, PLCC),
Spearman sequence related coefficient (Spearman rank-order correlation coefficient, SRCC), Ken Deer
Rank related coefficient (Kendallrank-order correlation coefficient, KROCC) and root-mean-square error
(Root Mean SquaredError,RMSE).For the value of three above related coefficient closer to 1, RMSE value is smaller, illustrates to calculate
Method is more accurate.
Step 7: analysis and comparison algorithm performance.The verifying present invention for VR video quality evaluation specific aim and have
Effect property, the present invention refer to several effective objective evaluation method for quality of stereo images contrast verification in LIVE database,
Experimental result on LIVEI database is as shown in table 1, and its experimental result on LIVEII database is as shown in table 2.From
From the point of view of experimental result, the objective evaluation method for quality of stereo images that the present invention is calculated can be with human subject's evaluation quality
It is consistent, there is availability.
Performance of 1 distinct methods of table on LIVEI
Performance of 2 distinct methods of table on LIVEII
[1]Benoit A,Callet P L,Campisi P,et al.Using disparity for quality
assessment of stereoscopicimages,.15th IEEE International ConferenceonImage
Processing,2008:389-392.
[2]Bensalma R,Iarabi C.A stereoscopic quality metric based on
binocular perception.Information Sciences Signal Processing&Their
Applications International Confe,2010:41-44.
[3]Chen M J,Su C C,Kwon D K,et al.Full-reference quality assessment
of stereopairsaccounting for rivalry.Signal Processing Image Communication,
2013,28(9):1143-1155.
Claims (1)
1. a kind of stereo image quality evaluation method based on region weight, each distorted image is to by left distortion mapWith right mistake
True figureComposition, and its corresponding reference picture to by left with reference to figureWith right with reference to figureComposition, resolution ratio is h*
W, including the following steps:
1) left distortion map is handled using Canny edge detection method firstWith right distortion mapThen in two images
Non-zero region is marked, and for the pixel in all labeled non-zero regions, completes center using k-means clustering method
The selection of point.If a central point is most adequately supported by the non-zero pixel of this non-zero region, i.e., the non-zero region being individually closed
Pixel number is greater than the 3% of whole pixel numbers, and is greater than 3%*min (h, w) a pixel distance to the center selected, then
It will be retained；All candidate points are handled using the mode of straight line fitting, obtain final straight-line detection knot
H (I is schemed in the left detection of fruitleft) and right detection figure H (Iright)。
2) H (I is schemed with left detectionleft) and right detection figure H (Iright) based on, the straight line that is marked and with the linear pixel distance
All pixels less than 5%*min (h, w) are defined as binocular region, and other regional natures are monocular region, while the present invention
Weight information is arranged to different zones；For binocular region, weight is arranged to each pixel
For monocular region, weight is arranged in each pixelWherein d is apart from nearest binocular
The linear pixel distance in region；
3) for monocular region, it is divided to or so two groups similarly to be handled, for left distortion mapWith left with reference to figureAccording to
Following equation processing:
Wherein, l (x, y), c (x, y) and s (x, y) use formula (2) respectively, and the method for formula (3) and formula (4) is calculated,
In addition, α=0.4, β=0.3, γ=0.3；
In this calculating process, C1, C2And C3It is set as non-zero value, thus according to left distortion mapWith left with reference to figureIt calculates
The score Q based on content weight towards left figureIW-SSIM1；Same method is used in right distortion mapWith right with reference to figureOn, obtain corresponding scores QIW-SSIM2；And the mean value of the two is defined the region weight quality on the basis of image content-based
Score QIW-SSIM；
4) for binocular region, left distortion map and right distortion map are merged, obtain distortion cyclopean figureSimultaneously by left ginseng
It examines figure and right with reference to figure fusion, obtains scheming with reference to cyclopeanBased on the region segmentation result in the first step, according to 3)
Method pairWithWeight SSIM calculating is carried out, the region weight mass fraction Q on the basis of stereoscopic vision is obtainedSW-SSIM；
5) the region weight mass fraction Q on the basis of comprehensive image content-basedIW-SSIMIt is weighed with the region on the basis of stereoscopic vision
Weight mass fraction QSW-SSIM, obtain final stereo image quality evaluation result:
Q=ω QIW-SSIM+(1-ω)·QSW-SSIM (5)
Wherein, ω=0.6527.
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Application publication date: 20190215 |