CN106548472A - Non-reference picture quality appraisement method based on Walsh Hadamard transform - Google Patents
Non-reference picture quality appraisement method based on Walsh Hadamard transform Download PDFInfo
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Abstract
The present invention relates to a kind of non-reference picture quality appraisement method based on Walsh Hadamard transform, comprises the following steps：The distorted image that selection is trained；Image conversion is carried out to each distorted image, the local Walsh Hadamard transform figure of image is obtained；Image characteristics extraction is carried out to zero row rate item and the uniform LBP operators of non-zero column rate item application invariable rotary respectively on three yardsticks；Features training, using SVR network training features, obtains characteristics of image to the mapping relations model of subjective quality scores, using this model as forecast model, the quality of image is predicted.
Description
Technical field
The invention belongs to image quality evaluation field, is related to a kind of non-reference picture quality appraisement method.
Background technology
With the fast development of Display Technique, people are carried to the quality of image during using various electronic products
Higher requirement is gone out, and image quality evaluation has served vital effect in the acquisition of high-quality picture.Picture quality
Evaluation methodology can be divided into subjective evaluation method and method for objectively evaluating.According to dependence journey of the algorithm to original undistorted image
Degree, method for objectively evaluating can be divided into full reference image quality appraisement (full-reference image quality again
Assessment, FR-IQA) algorithm, half reference image quality appraisement (reduced-reference image quality
Assessment, RR-IQA) algorithm and non-reference picture quality appraisement (no-reference image quality
Assessment, NR-IQA) algorithm.
In recent years, with deepening continuously that image quality evaluation is studied, a large amount of outstanding single distorted image quality have been emerged in large numbers
Evaluation algorithms.However, image can introduce various distortions when obtaining, transmitting, compress, storing, simply by single distorted image
Quality evaluation algorithm is evaluated many distorted image quality and there is very big error.For mix distortion image quality evaluating method more
Complexity not only needs to consider impact of the individual distortion to picture quality, and also needs to consider the phase interaction between different distortions
With shielding effect etc., the quality evaluation of mixing distorted image are in the stage at the early-stage.Gu Ke et al. simulate human vision system
Various distortions are separately considered and are estimated, finally estimated these by system (Human Visual System, HVS) quality assessment process
The fraction of meter is weighted and obtains last image quality score；On this basis, Gu Ke et al. add free energy explanation again
Interaction and shielding effect between various distortions.Li et al. reflects the various features of image fault degree, phase by extracting
The features such as bit integrity, gray level co-occurrence matrixes, are built using support vector regression (support vectorregression, SVR)
Mapping relations of the vertical feature to mass fraction, evaluate many distorted image quality.Lu et al. optionally chooses sensitive to distortion
Feature, using improved Bag-of-word coding characteristics, realizes feature to mass fraction finally by simple linear weighted function
Mapping.Existing research is not thorough enough for the understanding of many distorted images, and the relation assurance between various distortions is not accurate enough,
But as real-life image major part is many distorted images, so the image quality evaluation for mixing distortion will be not
Carry out the emphasis of image quality evaluation area research.Therefore, the present invention attempts to extract image using LWHT and the uniform LBP of invariable rotary
Feature, evaluates the quality of mixing distorted image.
[1]L.P.G.G.and D.S.,"Walsh–Hadamard Transform Kernel-Based Feature
Vector for Shot Boundary Detection,"in IEEE Transactions on Image Processing,
vol.23,no.12,pp.5187-5197,Dec.2014.
[2]M.Zhang,J.Xie,X.Zhou and H.Fujita,"No reference image quality
assessment based on local binary pattern statistics,"Visual Communications
and Image Processing(VCIP),2013,Kuching,2013,pp.1-6.
The content of the invention
It is an object of the invention to a kind of non-reference picture quality appraisement method based on Walsh Hadamard transform is proposed,
Characteristics of image is extracted using LWHT and the uniform LBP of invariable rotary, mixing distorted image quality is evaluated.The inventive method step is such as
Under：
A kind of non-reference picture quality appraisement method based on Walsh Hadamard transform, comprises the following steps：
1) select the distorted image being trained；
2) image conversion is carried out to each distorted image, obtains the local Walsh Hadamard transform figure of image
3) figure is carried out to zero row rate item and the uniform LBP operators of non-zero column rate item application invariable rotary respectively on three yardsticks
As feature extraction；
4) features training, using SVR network training features, obtains characteristics of image to the mapping relations mould of subjective quality scores
Type, using this model as forecast model, is predicted to the quality of image.
The beneficial effects of the present invention is the non-reference picture quality appraisement method based on Walsh Hadamard transform is different
In traditional single distorted image quality evaluation algorithm based on spatial domain, but local Walsh Hadamard transform is generated by LWHT
Figure, carries out the mixing distorted image quality evaluation algorithm of feature extraction in Walsh Hadamard transform domain.The inventive method and master
The concordance that perception is received is strong, and performance is better than the most of FR-IQA algorithms and NR-IQA algorithms that presently, there are.
Description of the drawings
Fig. 1 realizes block diagram for the totality of the inventive method；
Fig. 2 is two-dimentional Walsh Hadamard transform core；
Fig. 3 is 16 width local Walsh Hadamard transform figures；
Fig. 4 is zero row rate item of Walsh Hadamard transform and non-zero column rate item, and the 1st width characteristic pattern is zero row rate item, the 2nd width
It is non-zero column rate item with the 3rd width characteristic pattern.
Specific embodiment
The present invention proposes a kind of non-reference picture quality appraisement method based on Walsh Hadamard transform, and which is totally realized
Block diagram is as shown in Figure 1.To make technical scheme clearer, below the specific embodiment of the invention is done further
Description.
1. data base selects
Experimental material is MLIVE and two mixing distorted image data storehouses of MDID2013, and MLIVE data bases are sub comprising two
Collection, each subset include the different degrees of distorted image of 15 width reference pictures and its corresponding 225 width, MDID2013 data bases bag
Containing the different degrees of distorted image of 12 width reference pictures and its corresponding 324 width.Data base's specifying information is shown in Table 1.
1 database information of table
2. image conversion, obtains image local Walsh Hadamard transform figure by LWHT
By all overlapping block Jing WHTM conversion of image, given piece image I, its LWHT realize that the inventive method adopts four
Rank WHT, implements process as follows：
1) reset hadamard matrix and obtain WHTM
Formula 1 is minimum second order hadamard matrix, and N rank hada is obtained by the Kronecker product recursion of second order hadamard matrix
Agate matrix HN：
In formula, N represents the exponent number of hadamard matrix.
N ranks hadamard matrix often capable ± 1 sign modification number of times is calculated, sign modification number of times is referred to as row rate, passs according to row rate
The order rearrangement hadamard matrix of increasing is obtained WHTM.Quadravalence WHTM is as follows
In formula, row rate of the numeral 0,1,2,3 for quadravalence hadamard matrix.
2) two dimension WHTK is obtained using WHTM
Every a line of WHTM is referred to as one-dimensional Walsh Hadamard base vector, by one-dimensional ranks Walsh Hadamard base vector
Tensor product can obtain two-dimentional Walsh Hadamard transform core g (x, y, u, v), as shown in Figure 2.It is found that master is right from figure
WHTK more than linea angulata is the transposition of the WHTK of below leading diagonal.Use wi=g (x, y, u, v), u, v=0,1,2,3, i=1,
2 ..., 16 represent WHTK, w1(0) x, y, 0 represent the zero row rate core of Walsh Hadamard transform in the upper left corner, remaining transformation kernel to=g
For Walsh Hadamard transform non-zero column rate core.
3) projected image, the LWHT of image is projection of all overlapping blocks of image on WHTK, and 16 width will be obtained after projection
Local Walsh Hadamard transform figure
The matrix obtained after image block projection is referred to as Walsh Hadamard projection matrix, and image is in Walsh Hadamard transform
Projection on zero row rate core is referred to as zero row rate item, and the projection on Walsh Hadamard transform non-zero column rate core is referred to as non-zero column rate
Item, zero row rate item and non-zero column rate item are referred to as local Walsh Hadamard transform figure.Image block f (x, y) of the size for N × N
Projection formula on WHTK is as follows
In formula：(x, y) represents the coordinate of image block pixel, x, y=0, and (usual N is 2 integral number power to 1 ..., N-1, N
=2n), H (u, v) represents Walsh Hadamard transform value.
Piece image will obtain 16 width local Walsh Hadamard transform figures Jing after quadravalence WHT, wherein a width is zero row rate
, 15 width are non-zero column rate item in addition.Fig. 3 gives 16 width local Walsh Hadamard transform figures of piece image.
3. feature extraction, extracts the uniform LBP statistical natures of invariable rotary of local Walsh Hadamard transform figure
1) representational zero row rate item and non-zero column rate item are selected
WHT has various useful properties：
1. zero row rate item measures the monochrome information of image.
2. spatial domain energy and Walsh Hadamard transform domain preservation of energy.
3. energy compaction property, Jing after WHT, most energy is compressed to a few Walsh Hadamard and becomes image
Change in value, these Walsh Hadamard transform values have heavier relative to other Walsh Hadamard transform values
The meaning wanted.
Image ability of the conversion with separate picture multi-frequency, the low frequency component of image are relative with the brightness of image
Should, the high-frequency components of image are corresponding with the edge feature of image；Known by first Transformation Properties, zero row rate item measurement image
Monochrome information, it is possible to the brightness of image is obtained from the statistical property of zero row rate item, zero row rate item corresponds to image
Low frequency component, zero row rate core of Walsh Hadamard transform have catch image low frequency component ability；By spatial domain and Walsh
Hadamard transform domain preservation of energy can show that energy is constant before and after image conversion, and as zero row rate item is corresponding to image
Low frequency component, and image is divided into zero row rate item and non-zero column rate item Jing after WHT conversion, so non-zero column rate item is corresponding to image
High-frequency components, Walsh Hadamard transform non-zero column rate core have the ability for catching image high-frequency components, can be from non-zero column rate
The edge feature of image is extracted in the statistical property of item.
As gradation of image Distribution value is more uniform, the energy of image more concentrates on the corner of Walsh Hadamard projection matrix
On, all than more uniform, Jing after WHT, most energy is compressed to few image for natural image and distorted image grey value profile
In the several corner Walsh Hadamard transform values of number, and any two non-zero column rate item all be enough to express the high frequency spy of image
Property, so the present invention does not carry out feature extraction on all of local Walsh Hadamard transform figure in order to reduce redundancy,
And being selected on unique zero row rate item and two non-zero column rate items carries out feature extraction.From image in w5=g (x, y, 1,0)
And w6(1) x, y, 1 extract image edge information in the projection on two Walsh Hadamard transform non-zero column rate cores, in w to=g1
(0,0) x, y extract the monochrome information of image to=g in the projection on zero row rate core of Walsh Hadamard transform.
w1=[1,1,1,1；1,1,1,1；1,1,1,1；1,1,1,1], (7)
w5=[1,1,1,1；1,1,1,1；-1,-1,-1,-1；-1,-1,-1,-1], (8)
w6=[1,1, -1, -1；1,1,-1,-1；-1,-1,1,1；-1,-1,1,1], (9)
2) the zero row rate item and non-zero column rate item of selection are calculated
One width size is M1×M2Image I, seek its quadravalence WHT, the present invention divides the image into M using slip window sampling1×
M2Individual size is 4 × 4 overlapping block, if, m=1,2 ..., M1×M2It is m-th image block BmIn w1, w5, w6On three WHTK
Walsh Hadamard transform value, computing formula are as follows：
In formula, convolution is represented.By the M for obtaining1×M2It is individual, size is individually placed to for M1×M2Three width characteristic patterns correspondence
Position, the first width characteristic pattern are zero row rate items, and the second width and the 3rd width characteristic pattern are non-zero column rate items, as shown in Figure 4.
3) extract the statistical nature of zero row rate item and non-zero column rate item
The image of different distortion levels, low frequency component are different with high-frequency components attenuation degree, corresponding to the zero of low frequency component
Row rate item and corresponding to height
The statistical property of the non-zero column rate item of frequency composition is naturally also different.The spy that the present invention is carried out by using these properties
Levy extraction.
Meet the process that human visual system observes image due to multiple dimensioned, the different scale of image has shadow to algorithm performance
Ring, and as the monochrome information and edge letter of image can be extracted in the statistical property from zero row rate item and non-zero column rate item respectively
Breath, the uniform LBP operators of invariable rotary are a kind of effective Statistical Operators, so the present invention on three yardsticks respectively to zero row rate
Item and non-zero column rate item application R=1, the uniform LBP operators of invariable rotary of P=8 carry out image characteristics extraction.Implement process
It is as follows：
1. using each pixel in the uniform LBP operators coded image of invariable rotary, you can obtain invariable rotary equal
Even LBP figures, the uniform LBP operators computing formula of invariable rotary are as follows：
In formula：U represents 0,1 transition times, and riu2 represents invariable rotary uniform LBP of 0,1 transition times less than 2 times.
Formula 7 is uniform pattern 0,1 transition times computing formula, and formula 8 is that the uniform LBP of invariable rotary encodes formula.
2. the statistic histogram of the uniform LBP figures of invariable rotary is calculated, and the statistical nature of image is represented with statistic histogram, it is public
Formula is as follows：
In formula：K represents the different coding pattern of the uniform LBP of invariable rotary, and K represents the different volumes of the uniform LBP of invariable rotary
Pattern sum.
The present invention is obtained to zero row rate item and the uniform LBP operators of non-zero column rate item application invariable rotary on three yardsticks respectively
Scheme to the uniform LBP of invariable rotary, LBP figures uniform to invariable rotary seek its statistic histogram, the feature of 90 dimensions for obtaining is former
The brightness and edge feature of beginning image.
4. features training, using SVR network training features, obtains training pattern, realizes that feature arrives objective quality scores
Mapping
The characteristic vector and its corresponding subjective quality scores of image are trained using SVR networks, obtain image
The mapping relations model of characteristic vector and subjective quality scores, using this model as forecast model, is carried out to the quality of image pre-
Survey.In the present invention, the kernel function that SVR networks are adopted is for RBF.
To prove that it is very high consistent that image prediction objective quality scores that the inventive method obtains and subjective quality scores have
Property, predict that objective quality scores can accurately reflect the quality of image, by the inventive method in two mixing of MLIVE and MDID2013
Distorted image data is tested on storehouse, takes the index evaluation of 3 measurement Objective image quality evaluation algorithms conventional in the world
The performance of the inventive method, 3 indexs are respectively Spearman sequence correlation coefficient (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 RMSE refer to
Mark weighs the forecasting accuracy of objective algorithm, and SRCC indexs weigh the prediction monotonicity of objective algorithm.The value of PLCC and SRCC is got over
The value for being close to 1, RMSE is less, illustrates that algorithm performance is better, predicts that objective quality scores and the dependency of subjective quality scores are got over
It is high.In order to reduce impact of the non-linear factor to algorithm performance, the present invention is when PLCC and RMSE is calculated using 5 parameters
Logistic function pair masters, objective assessment score carry out nonlinear regression.The Logistic function formulas of 5 parameters are as follows：
Q (x)=μ1logistic(μ2,(x-μ3))+μ4x+μ5, (17)
The inventive method mixes testing algorithm performance on distortion data storehouse at two respectively, and concrete test process is as follows：
1) in each data base 80% image is taken at random as training set, and remaining 20% image is used as test set.
2) characteristic vector and its corresponding subjective quality scores of training set image are trained using SVR networks, are obtained
To characteristic vector to the mapping relations model of subjective quality scores, the quality of test set image is predicted using this model.
Calculate the value of prediction objective quality scores and SRCC, PLCC and RMSE of subjective quality scores.
3) repeatedly 1), 2) process 1000 times, take 1000 times and test the intermediate value of SRCC, PLCC and RMSE value for obtaining as most
Whole performance, for method comparison.
5. compare and parser performance
Relatively the inventive method and some outstanding FR-IQA and NR-IQA algorithms, FR-IQA algorithms include PSNR, SSIM,
VSNR, VIF, FSIM, IGM, GMSD, NR-IQA algorithms include BRISQUE, NFERM, NIQE, IL-NIQE, as shown in table 2, are
Facilitate viewing, in form, use italic overstriking font representation performance one algorithm of highest.As can be seen from the table, the present invention
The degree of correlation of method and subjective assessment, order of accuarcy are significantly improved, the best performance compared with other algorithms, SRCC and
PLCC values are maximum, and RMSE value is minimum, illustrate that the prediction objective quality scores of the inventive method are high with subjective quality scores dependency.
2 algorithm performance of table compares
The inventive method has advantages below：
(1) the inventive method is strong with the concordance of subjective feeling.
(2) degree of correlation of the inventive method and subjective assessment, order of accuarcy are significantly improved.
(3) the inventive method is different from traditional single distorted image quality evaluation algorithm based on spatial domain, but based on change
The mixing distorted image quality evaluating method in domain is changed, performance is calculated better than the most of FR-IQA algorithms and NR-IQA that presently, there are
Method.
Claims (1)
1. a kind of non-reference picture quality appraisement method based on Walsh Hadamard transform, comprises the following steps：
1) select the distorted image being trained；
2) image conversion is carried out to each distorted image, obtains the local Walsh Hadamard transform figure of image
3) carry out image on three yardsticks respectively to zero row rate item and the uniform LBP operators of non-zero column rate item application invariable rotary special
Levy extraction；
4) features training, using SVR network training features, obtains characteristics of image to the mapping relations model of subjective quality scores,
Using this model as forecast model, the quality of image is predicted.
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| CN107481236A (en) * | 2017-08-28 | 2017-12-15 | 方玉明 | A kind of quality evaluating method of screen picture |
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| CN107767367A (en) * | 2017-09-26 | 2018-03-06 | 天津大学 | It is a kind of for HDR figures without reference mass method for objectively evaluating |
| CN108446292A (en) * | 2018-01-17 | 2018-08-24 | 天津大学 | Subjective quality assessment method based on more distortion screenshotss images |
| CN108681997A (en) * | 2018-04-26 | 2018-10-19 | 天津大学 | Based on improvement LBP features without with reference to more distorted image quality evaluating methods |
| CN109344860A (en) * | 2018-08-19 | 2019-02-15 | 天津大学 | A kind of non-reference picture quality appraisement method based on LBP |
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Application publication date: 20170329 |