CN107578406A - Based on grid with Wei pool statistical property without with reference to stereo image quality evaluation method - Google Patents
Based on grid with Wei pool statistical property without with reference to stereo image quality evaluation method Download PDFInfo
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Abstract
The as follows without reference stereo image quality evaluation method, step of statistical property is moored based on grid and Wei the present invention relates to a kind of：The disparity map and progress left view point diagram of calculated distortion stereo pairs and the fusion of right viewpoint figure；Left view point diagram, right viewpoint figure, composite diagram and disparity map to distortion stereo pairs carry out down-sampled twice；Extract left and right view, composite diagram and disparity map and it is carried out once, the feature in rear each figure down-sampled twice；By the left view point diagram of distortion stereo pairs, right viewpoint figure, composite diagram and disparity map and its carry out it is once down-sampled and down-sampled twice after each figure be transformed into gradient field, then the form parameter and scale parameter feature of Wei Bo distributions are extracted from each gradient field；Left view point diagram, right viewpoint figure, composite diagram from distortion stereo pairs and grid chart is extracted in each figure of disparity map, then grid intensity and grid regularity feature are extracted from each grid chart；Establish regression model, prognostic chart picture quality.
Description
Technical field
The invention belongs to the objective evaluation system of image processing field, specifically stereo image quality, it is related to using grid
The objective image evaluation method of feature and Wei pool statistical property on stereo-picture.
Background technology
With the arrival in multimedia messages epoch, people as the data signal active influence of representative and is changed using image and video
Live and work mode.The fast development of imaging technique and display device and the popularization of stereo-picture drastically increase use
The visual quality experience at family.But image can produce different degrees of and different types of mistake unavoidably in processing, storage and transmission
Very, understanding of the people to image has been had a strong impact on, while has also influenceed the extraction of image information, therefore, has established effective image matter
It is most important to measure evaluation mechanism.Objective image quality evaluation is divided into full reference image quality appraisement (FR-IQA), part reference chart
As quality evaluation (RR-IQA) and non-reference picture quality appraisement (NR-IQA), now without with reference to stereo image quality evaluation
In, most straightforward approach is exactly that left and right viewpoint figure is commented respectively using the statistical nature of plane picture quality evaluating method
Valency, the quality of mean prediction stereo-picture is finally taken, but such method does not have the visually-perceptible mechanism for considering that the mankind are complicated,
Therefore evaluation poor-performing.Composite diagram [1] incorporates the parallax information of visually-perceptible so that stereo image quality objective evaluation and master
It is higher to see evaluation uniformity.Therefore, the present invention proposes to extract feature on left and right view, disparity map and composite diagram respectively, carries out
Stereo image quality is evaluated.
[1]Chen M J,Su C C,Kwon D K,et al.Full-reference quality assessment
of stereopairs accounting for rivalry[J].Signal Processing:Image
Communication,2013,28(9):1143-1155.
The content of the invention
It is an object of the invention to for distortion stereo image quality evaluation problem, it is preferably vertical to propose that one kind can obtain
The nothing of body image quality evaluation effect examines stereo image quality evaluation method.The present invention propose first to left and right view, composite diagram and
Disparity map work is down-sampled twice, and the shape of Wei Bo distribution extraction images is used in each gradient map after down-sampled preceding and down-sampled
Parameter and scale parameter feature；And the grid chart of down-sampled preceding and down-sampled rear each figure is extracted, extract grid in each grid chart
Intensity and grid regularity feature, all features are finally inputted in SVR establish model, include asymmetric distorted image with prediction
Mixing distortion stereo image quality.The method for carrying out stereo image quality evaluation.Technical scheme is as follows：
It is a kind of that the as follows without reference stereo image quality evaluation method, step of statistical property is moored based on grid and Wei：
1) disparity map of calculated distortions stereo pairs and progress left view point diagram and the fusion of right viewpoint figure
To distortion stereo-picture, it is poor that left view is calculated by left view point diagram and right viewpoint figure, with left view point diagram and left view difference phase
Add to obtain disparity map, then respond to obtain image weights by normalizing Gabor filtered energies by left view point diagram and disparity map, according to figure
Composite diagram is synthesized as weight；
2) to the left view point diagrams of distortion stereo pairs, right viewpoint figure, composite diagram and disparity map are dropped twice adopts
Sample；
3) extracts left and right view, composite diagram and disparity map and it carried out once, the spy in rear each figure down-sampled twice
Sign
By the left view point diagram of distortion stereo pairs, right viewpoint figure, composite diagram and disparity map and its carry out once down-sampled
Each figure after down-sampled twice is transformed into gradient field, then the form parameter and yardstick of Wei Bo distributions are extracted from each gradient field
Parameter attribute, the Wei pool distribution shape and scale parameter of each width gradient map can be distributed the probability density in this gradient map by Wei Bo
Function obtains；Left view point diagram, right viewpoint figure, composite diagram from distortion stereo pairs and extract grid in each figure of disparity map
Figure, then extraction grid intensity and grid regularity feature, method are from each grid chart：Divide from the gray-scale map of each image
Other calculated level edge and vertical edge, then filtered processing generation horizontal grid and vertical grid, horizontal grid and vertical
Grid is added to obtain grid chart, and grid intensity is to assign different weights to the different images sensitizing range of grid chart and it is added
Power summation obtains, and grid regularity is to grid image weighted sum normalized by weighting function, then calculates its Fourier
The power spectrum of transform domain obtains, and it is 48 to extract characteristic altogether；
4) establishes regression model, prognostic chart picture quality
Image in three-dimensional distorted image data storehouse is divided into test set and training set, will be carried from training set distorted image
The features described above got and corresponding subjective scores value be input in support vector regression construction feature vector and subjective scoring value it
Between mapping relations be trained, obtain training pattern, the relation regression model for recycling to obtain is to test set distorted image
Quality is predicted, so as to realize image quality evaluation.
The inventive method has advantages below：
(1) the inventive method and subjective stereo image quality evaluation uniformity are high.Better than current most of main flow algorithms.
(2) different from plane picture quality evaluation, the present invention considers the distinctive parallax information of stereo-picture, proposes from conjunction
Feature is extracted into figure and disparity map, test result indicates that the present invention can reach very high stereo image quality forecasting accuracy.
Brief description of the drawings
Fig. 1 algorithm flow charts
Fig. 2 or so view fusion process
Embodiment
The present invention propose it is a kind of based on left and right view, composite diagram and the disparity map of distortion stereo-picture and its it is down-sampled after
Each figure on extract Wei pool statistical nature and grid search-engine non-reference picture quality appraisement method.To make the technical side of the present invention
Case is clearer, and the specific embodiment of the invention is further described through below.
1. disparity map calculates and the fusion of left and right viewpoint figure
Occur in various degree and after different type distortion, due to the presence of parallax information, the objective quality of stereo-picture without
Method only being worth to by left and right viewing quality, the stereo-picture for merging parallax information are different from plane picture, left and right view
Merge obtained composite diagram and mainly consider parallax information.Therefore the present invention is handled as follows to left and right view, and left and right view is carried out
Gabor is filtered, and the multiple Gabor filtering of two dimension is defined as follows：
Wherein R1=xccs θ+ysin θ, R2=-sin θ+ycos θ.σx, σyIt is standard deviation, ζx, ζyFor spatial frequency, its value
3.67cycle/degree is set to, θ is filtering direction.Gabor filter parameter is set, four direction (it is horizontal, vertical, two pairs
Linea angulata) amplitude response energy sum as local energy, energy response value be respectively GEL, GER.The calculating of left and right weight is logical
Normalization Gabor filtered energies response assignment is crossed to obtain,
It is defined as：
Such as Fig. 2, composite diagram is defined as by the present invention：
C (x, y)=wL(x,y)·IL(x,y)+wR(x+d,y)·IR(x+d,y) (4)
Wherein, C represents composite diagram, ILAnd IRLeft and right viewpoint figure is represented, d is parallax, WLAnd WRFor left and right weight, for vertical
The quality evaluation of body image, present invention utilizes the parallax information of left and right view to synthesize composite diagram, is prepared for extraction feature.
The d synthesis disparity maps of each pixel.
2. Wei moors statistical nature
Ben Wei pool distributions have very strong correlation to the vision of image with the mankind, and the GM of blurred picture follows Wei Bofen
Cloth, human visual system and the shape and the scale parameter degree of correlation of Wei Bo distributions are high.The two parameters can accurately describe to scheme
The Space Consistency and complexity of picture.
GM of the gray level image at position (i, j) place is calculated as follows：
* it is convolution symbol, pxAnd pyPosition (i, j) place is illustrated respectively in along Prewitt filtering both horizontally and vertically
Device.It is defined as follows：
(6)
Wei Bo points of shape and scale parameter can be obtained by its probability density function, and the probability density function of Wei Bo distributions is as follows：
η and β represents shape and scale parameter respectively, and η represents the width of distribution, and β refers to the peak value of distribution.η can reflect
Local contrast, and local contrast change influences perception of the human eye to picture quality；β is quicker to local rim space frequency
Sense.Image fault can be described with two parameters of η and β.
3. grid intensity and grid are regular
But only η and β parameters can't describe image well and be influenceed by distortions such as structures, therefore extract grid
Feature description graph is specific as follows to reach better image prediction of quality effect as features such as structure and edges.
1) grid images
Gray-scale Image Edge is extracted first, utilizes second differnce calculated level and vertical edge：
Dh(x, y)=| 2I (x, y)-I (x-1, y)-I (x+1, y) | (8)
Dv(x, y)=| 2I (x, y)-I (x, y-1)-I (x, y+1) | (9)
I (x, y) is gray level image.
Edge pixel saltus step can cause image fault, and in relatively flat region, edge pixel is weaker, and edge pixel changes past
Toward being not easy to discover, the present invention uses every 33 row mutually to add up to strengthen weaker margin signal：
In order to obtain the amplitude of relatively equalization, local intermediate value is subtracted with above formula：
Eh(x, y)=Ea(x,y)-median({Ea(i,y)|x-16≤i≤x+16}) (12)
Ev(x, y)=Eb(x,y)-median({Eb(x,j)|y-16≤j≤y+16}) (13)
Median () represents to take intermediate value.
In view of JPEG coded block sizes be 8 × 8, extracting cycle of the present invention be 8 grid, horizontal and vertical grid formula
It is expressed as：
Gh(x, y)=median ({ Eh(i, y) | i=x-16, x-8, x+8, x+16 }) (14)
Gv(x, y)=median ({ Ev(x, j) | j=y-16, y-8, y+8, y+16 }) (15)
Image lattice is calculated as follows formula：
G (x, y)=Gh(x,y)+Gv(x,y) (16)
2) grid intensity
When observing image, human eye is more sensitive to the structure distortion of non-planar regions, due to texture shielding effect be present,
Thus it is not readily observed the distortion of texture region.First define a weight function
σ (x, y) represents the standard deviation of pixel (x, y) neighborhood territory pixel in original image, for describing Texture complication,
Larger weights can be assigned to the grid intensity level of smooth region, and less power is assigned to the grid intensity of texture region
Value.
Grid intensity is defined with following formula：
Wherein, M × N is the size of image,
Q (x, y)=| G (x, y) | (19)
3) grid is regular
The regularity of Grid Signal strengthens with the increase of image fault degree, thus regularity is described can be with
Reflect the degree of structure distortion in image.Before calculating grid regularity, first with weight functionTo grid image G weighted sums
Normalized：
Grid image G ' after weighting is converted to one-dimensional signal, and is expressed as Vh.Adjacent element difference is calculated below：
VD (i)=| Vi(i)-Vi(i+1)|,1≤i≤MN-1 (21)
It is previously noted that the cycle of Grid Signal is 8, therefore difference signal VD cycle is also 8.To describe VD cycle
Property, carried out discrete fourier transform and calculate its power spectrum.
st={ st[n]=VD [nt+n]},0≤n≤K-1 (22)
K is 2 integral number power；1≤t≤L, L=MN/K, ntIt is the starting point of t sections.stPower spectrum be designated as：
pt={ pt[n,0≤n≤K-1]} (23)
Horizontal direction grid regularity is defined as：
P is the average of power spectrum, p1For the power spectrum after medium filtering；Parameter 8/7 is mainly used in keeping total energy；1/
7 average peak for calculating power spectrum.The regular measurement R of vertical direction gridvIt can be obtained by similar approach.
Grid regularity is：
4. establish model
When observing image, due to the change of the metric space of observation, same image can produce human eye in different scale space
Raw different visual effect, for more accurate prognostic chart picture quality, we are first to image drop sampling.It is N* for a width size
M distortion stereo-picture, down-sampled coefficient is set to 1, is often to go to take a point and every every 1 point in figure before sampling
Arrange and take a point to form the image that the new size of a width is (N/2) * (M/2) every 1 point.
The present invention left and right view, composite diagram and disparity map are carried out it is down-sampled twice transform to different scale space, and
It is down-sampled before and every time it is down-sampled after signal all as input, extract respectively each signal Wei pool statistical nature (shape join
Number η and scale parameter β) and grid intensity and grid regularity feature., it is necessary to which a regression model is by each feature after feature extraction
It is mapped as representing the fraction of image oeverall quality, the present invention uses SVR using radial direction base core as kernel function as mapping function,
Establish model and test of heuristics comprises the following steps that：
A. the image that 80% is randomly selected in each database is used as test as training set, the image of residue 20%
Collection.
B. training set characteristics of image and the subjective quality scores of correspondence image are inputted, are trained using SVR networks, are obtained
Mapping relations model of the feature to subjective quality scores；To test set image, prediction of quality is carried out using obtained model.Respectively
Calculate SRCC, PLCC and RMSE value.
C. a, b process are repeated 1000 times, tests obtained SRCC, PLCC to 1000 times respectively and RMSE value takes intermediate value conduct
Final SRCC, PLCC and RMSE value, compares parameter as algorithm performance.
5. database selects
Two open test storehouses of present invention selection, they are the asymmetric stereo-picture test libraries that LIVE laboratories provide
LIVE-3D I and symmetrical stereo-picture test library LIVE-3D II.In LIVE-3D II databases, totally 360 width distortions are three-dimensional
Image, the storehouse include symmetrical and asymmetric 2 kinds of distortions, include 5 kinds of type of distortion:JPEG compression, JPEG2000 (JP2K), Gauss
Fuzzy (Gaussian blur, GBLUR), white noise (white noise, WN) and fast weak (fast fading, FF), and
Provide the subjective scoring difference and parallax value of every group of distortion stereo-picture.In LIVE 3D I datums storehouse, totally 365 width distortions are stood
Body image, it is symmetrical distortion, includes 5 kinds of type of distortion:JPEG,JP2K,GBLUR,WN,FF.
For prove image prediction objective quality scores that the inventive method obtains and subjective quality scores have it is very high consistent
Property, prediction objective quality scores can accurately reflect the quality of image, the inventive method is surveyed in symmetrical and asymmetric stereo-picture
Tested on examination storehouse LIVE-3D II and LIVE-3D I, take the index of 3 conventional measurement Objective image quality evaluation algorithms
The performance of the inventive method is assessed, 3 indexs are respectively Spearman sequence coefficient correlation (Spearman rank-order
Correlation coefficient, SRCC), Pearson's linearly dependent coefficient (Pearson linear correlation
Coefficient, PLCC) and root-mean-square error (Root Mean Squared Error, RMSE), wherein PLCC and SRCC's
Span is [0 1], and it is better closer to 1 performance to be worth, and the RMSE smaller algorithm performance of value is better.
6. compare and parser performance
Present invention verification algorithm performance on stereo-picture test library LIVE-3D I and LIVE-3D II, from following contrast
In understand that the inventive method achieves good effect, the inventive method vertical stops image quality evaluating method uniformity with subjective
It is high.Table 1 represents performance of the present invention on LIVE 3D Phase I test libraries, as can be seen from Table 1：1) this algorithm exists
Performance on LIVE-3D I datums storehouse and LIVE-3D II databases is better than other algorithms, has accuracy high very well；2)
Because LIVE-3D I datums storehouse only includes symmetrical distortion, LIVE-3D II databases are existing to claim distortion to have asymmetric distortion again.Institute
So that for other algorithms, the performance on LIVE-3D I datums storehouse is always substantially better than the property on LIVE-3D II databases
Energy.And performance of the inventive algorithm on two databases is suitable；On the contrary, the SRCC values on LIVE-3D II databases are slightly higher
SRCC values on LIVE-3D I datums storehouse.This algorithm also enters one by one to the 5 class distorted images in LIVE-3D I datums storehouse respectively
The prediction of row mass fraction, the Comparative result such as table 2 with each algorithm, from Table 2, it can be seen that this algorithm is totally better than other calculations
Method, this algorithm have very high subjective and objective uniformity to tri- kinds of JP2K, WN, G blur distorted images.
The algorithm performance of table 1 compares
Tab.1 Performance comparison of algorithms
Single distortion performance compares on the LIVE-3D I datums storehouse of table 2
Tab.2 DETAILED PERFORMANCES ON LIVE 3D IMAGE DATABASE PHASE I
Claims (1)
1. a kind of moor the as follows without reference stereo image quality evaluation method, step of statistical property based on grid and Wei：
1) disparity map of calculated distortions stereo pairs and progress left view point diagram and the fusion of right viewpoint figure
To distortion stereo-picture, it is poor that left view is calculated by left view point diagram and right viewpoint figure, is added with left view point diagram with left view difference
Disparity map, then respond to obtain image weights by normalizing Gabor filtered energies by left view point diagram and disparity map, weighed according to image
Synthesize composite diagram again；
2) is carried out down-sampled twice to left view point diagram, right viewpoint figure, composite diagram and the disparity map of distortion stereo pairs；
3) extracts left and right view, composite diagram and disparity map and it carried out once, the feature in rear each figure down-sampled twice
By the left view point diagram of distortion stereo pairs, right viewpoint figure, composite diagram and disparity map and its carry out once down-sampled and two
It is secondary it is down-sampled after each figure be transformed into gradient field, then the form parameter and scale parameter of Wei Bo distributions are extracted from each gradient field
Feature, the Wei pool distribution shape and scale parameter of each width gradient map can be distributed the probability density function in this gradient map by Wei Bo
Obtain；Left view point diagram, right viewpoint figure, composite diagram from distortion stereo pairs and grid chart is extracted in each figure of disparity map, then
Grid intensity and grid regularity feature, method are extracted from each grid chart is：Counted respectively from the gray-scale map of each image
Horizontal edge and vertical edge are calculated, then filtered processing generation horizontal grid and vertical grid, horizontal grid and vertical grid
Addition obtains grid chart, and grid intensity is that the different images sensitizing range of grid chart is assigned different weights and is weighted it to ask
With obtain, grid regularity is to grid image weighted sum normalized by weighting function, then calculates its fourier transform
The power spectrum in domain obtains, and it is 48 to extract characteristic altogether；
4) establishes regression model, prognostic chart picture quality
Image in three-dimensional distorted image data storehouse is divided into test set and training set, will be extracted from training set distorted image
Features described above and corresponding subjective scores value be input in support vector regression between construction feature vector and subjective scoring value
Mapping relations are trained, and obtain training pattern, recycle the quality of obtained relation regression model to test set distorted image
It is predicted, so as to realize image quality evaluation.
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