CN103281556B - Objective evaluation method for stereo image quality on the basis of image decomposition - Google Patents

Abstract

The invention discloses an objective evaluation method for the stereo image quality on the basis of image decomposition. The objective evaluation method for the stereo image quality on the basis of the image decomposition comprises the following steps: firstly, respectively decomposing the left viewpoint image of a distorted stereo image to be evaluated into a recovery image and an interference image; decomposing the right viewpoint image of the distorted stereo image to be evaluated into a recovery image and an interference image; respectively evaluating the recovery images of the left viewpoint image and the right viewpoint image by adopting the local phase characteristic and the local vibration amplitude characteristic; respectively evaluating the interference images of the left viewpoint image and the right viewpoint image by using a singular value vector; and obtaining the objective evaluation predicted value for the distorted stereo image quality to be evaluated. The objective evaluation method for the stereo image quality on the basis of the image decomposition has the advantages that the recovery images and the interference images obtained by decomposition can better represent the influence of an image detail and redundant information on the image quality, so that an evaluation result can better conform to the human vision system, and therefore the correlation of an objective evaluation result and subjective perception can be effectively improved.

Description

A kind of objective evaluation method for quality of stereo images based on picture breakdown

Technical field

The present invention relates to a kind of image quality evaluating method, especially relate to a kind of objective evaluation method for quality of stereo images based on picture breakdown.

Background technology

Along with developing rapidly of image coding technique and stereo display technique, stereo-picture technology receives to be paid close attention to and application more and more widely, has become a current study hotspot.Stereo-picture technology utilizes the binocular parallax principle of human eye, and binocular receives the left and right visual point image from Same Scene independently of one another, is merged and forms binocular parallax, thus enjoy the stereo-picture with depth perception and realism by brain.Owing to being subject to the impact of acquisition system, store compressed and transmission equipment, stereo-picture can inevitably introduce a series of distortion, and compared with single channel image, stereo-picture needs the picture quality ensureing two passages simultaneously, therefore quality evaluation is carried out to it and have very important significance.But current stereoscopic image quality lacks effective method for objectively evaluating and evaluates.Therefore, set up effective stereo image quality objective evaluation model tool to be of great significance.

Current objective evaluation method for quality of stereo images is that plane picture quality evaluating method is directly applied to evaluation stereo image quality, or the depth perception of stereo-picture is evaluated by the quality evaluating disparity map, but, stereoscopic image carries out merging the expansion that the relief process of generation is not simple plane picture quality evaluating method, and human eye not direct viewing disparity map, the depth perception evaluating stereo-picture with the quality of disparity map is very inaccurate.Therefore, how effectively binocular solid perception to be simulated in stereo image quality evaluation procedure, and how the Influencing Mechanism of different type of distortion to three-dimensional perceived quality is analyzed, making evaluation result can reflect human visual system more objectively, is all carry out in stereoscopic image the problem that needs in evaluating objective quality process to research and solve.

Summary of the invention

Technical problem to be solved by this invention is to provide a kind of objective evaluation method for quality of stereo images based on picture breakdown that effectively can improve the correlation of objective evaluation result and subjective perception.

The present invention solves the problems of the technologies described above adopted technical scheme: a kind of objective evaluation method for quality of stereo images based on picture breakdown, it is characterized in that its processing procedure is:

First, 3 grades of wavelet transformations are implemented to the left visual point image of the left visual point image of original undistorted stereo-picture and the stereo-picture of distortion to be evaluated, again according to the matrix of wavelet coefficients of 3 directional subbands corresponding to every grade of wavelet transformation obtaining, obtain Recovery image and the interfering picture of the left visual point image of the stereo-picture of distortion to be evaluated; Equally, 3 grades of wavelet transformations are implemented to the right visual point image of the right visual point image of original undistorted stereo-picture and the stereo-picture of distortion to be evaluated, again according to the matrix of wavelet coefficients of 3 directional subbands corresponding to every grade of wavelet transformation obtaining, obtain Recovery image and the interfering picture of the right visual point image of the stereo-picture of distortion to be evaluated;

Secondly, by the local phase characteristic sum local amplitude feature of each pixel in the Recovery image of the left visual point image of the stereo-picture of the left visual point image and distortion to be evaluated that calculate original undistorted stereo-picture, obtain the picture quality objective evaluation predicted value of the Recovery image of the left visual point image of the stereo-picture of distortion to be evaluated; Equally, by the local phase characteristic sum local amplitude feature of each pixel in the Recovery image of the right visual point image of the stereo-picture of the right visual point image and distortion to be evaluated that calculate original undistorted stereo-picture, obtain the picture quality objective evaluation predicted value of the Recovery image of the right visual point image of the stereo-picture of distortion to be evaluated;

Then, by the singular value vector that each size in the interfering picture of the left visual point image of the stereo-picture of the left visual point image and distortion to be evaluated that calculate original undistorted stereo-picture is the sub-block of 8 × 8, obtain the picture quality objective evaluation predicted value of the interfering picture of the left visual point image of the stereo-picture of distortion to be evaluated; Equally, by the singular value vector that each size in the interfering picture of the right visual point image of the stereo-picture of the right visual point image and distortion to be evaluated that calculate original undistorted stereo-picture is the sub-block of 8 × 8, obtain the picture quality objective evaluation predicted value of the interfering picture of the right visual point image of the stereo-picture of distortion to be evaluated;

Afterwards, the Recovery image of the left visual point image of the stereo-picture of distortion to be evaluated and the picture quality objective evaluation predicted value of interfering picture are merged, obtains the picture quality objective evaluation predicted value of the left visual point image of the stereo-picture of distortion to be evaluated; Equally, the Recovery image of the right visual point image of the stereo-picture of distortion to be evaluated and the picture quality objective evaluation predicted value of interfering picture are merged, obtains the picture quality objective evaluation predicted value of the right visual point image of the stereo-picture of distortion to be evaluated;

Moreover, the left visual point image of the stereo-picture of distortion to be evaluated and the picture quality objective evaluation predicted value of right visual point image are merged, obtains the picture quality objective evaluation predicted value of the stereo-picture of distortion to be evaluated;

Finally, adopt the undistorted stereo-picture that several are original, set up its distortion stereo-picture set under the different distortion level of different type of distortion, this distortion stereo-picture set comprises the stereo-picture of several distortions, then calculates the picture quality objective evaluation predicted value of the stereo-picture of every width distortion in this distortion stereo-picture set respectively according to the process of the picture quality objective evaluation predicted value of the stereo-picture of above-mentioned acquisition distortion to be evaluated.

Objective evaluation method for quality of stereo images based on picture breakdown of the present invention, is characterized in that it specifically comprises the following steps:

1. S is made _orgfor original undistorted stereo-picture, make S _disfor the stereo-picture of distortion to be evaluated, by S _orgleft visual point image be designated as { L _org(x, y) }, by S _orgright visual point image be designated as { R _org(x, y) }, by S _disleft visual point image be designated as { L _dis(x, y) }, by S _disright visual point image be designated as { R _dis(x, y) }, wherein, (x, y) coordinate position of the pixel in left visual point image and right visual point image is represented, 1≤x≤W, 1≤y≤H, W represents the width of left visual point image and right visual point image, and H represents the height of left visual point image and right visual point image, L _org(x, y) represents { L _org(x, y) } in coordinate position be the pixel value of the pixel of (x, y), R _org(x, y) represents { R _org(x, y) } in coordinate position be the pixel value of the pixel of (x, y), L _dis(x, y) represents { L _dis(x, y) } in coordinate position be the pixel value of the pixel of (x, y), R _dis(x, y) represents { R _dis(x, y) } in coordinate position be the pixel value of the pixel of (x, y);

2. respectively to { L _org(x, y) } and { L _dis(x, y) } implement 3 grades of wavelet transformations, then according to { L _org(x, y) } matrix of wavelet coefficients of 3 directional subbands that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding and { L _dis(x, y) } matrix of wavelet coefficients of 3 directional subbands that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding, obtain { L _dis(x, y) } Recovery image and interfering picture, correspondence is designated as with wherein, represent middle coordinate position is the pixel value of the pixel of (x, y), represent middle coordinate position is the pixel value of the pixel of (x, y);

Respectively to { R _org(x, y) } and { R _dis(x, y) } implement 3 grades of wavelet transformations, then according to { R _org(x, y) } matrix of wavelet coefficients of 3 directional subbands that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding and { R _dis(x, y) } matrix of wavelet coefficients of 3 directional subbands that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding, obtain { R _dis(x, y) } Recovery image and interfering picture, correspondence is designated as with wherein, represent middle coordinate position is the pixel value of the pixel of (x, y), represent middle coordinate position is the pixel value of the pixel of (x, y);

3. { L is calculated respectively _org(x, y) }, { R _org(x, y) }, with in the local phase characteristic sum local amplitude feature of each pixel, by { L _org(x, y) } in coordinate position be that the local phase feature of the pixel of (x, y) is designated as by { L _org(x, y) } in coordinate position be that the local amplitude feature of the pixel of (x, y) is designated as by { R _org(x, y) } in coordinate position be that the local phase feature of the pixel of (x, y) is designated as by { R _org(x, y) } in coordinate position be that the local amplitude feature of the pixel of (x, y) is designated as will middle coordinate position is that the local phase feature of the pixel of (x, y) is designated as will middle coordinate position is that the local amplitude feature of the pixel of (x, y) is designated as will middle coordinate position is that the local phase feature of the pixel of (x, y) is designated as will middle coordinate position is that the local amplitude feature of the pixel of (x, y) is designated as

4. according to { L _org(x, y) } and in the local phase characteristic sum local amplitude feature of each pixel, calculate picture quality objective evaluation predicted value, be designated as $Q_L^{\mathrm{LPA}}=\left(\frac{1}{H\×W}\underset{y=1}{\overset{H}{\mathrm{\Σ}}}\underset{x=1}{\overset{W}{\mathrm{\Σ}}}{S}_L^{\mathrm{LP}}(x,y)\right)\×\left(\frac{1}{H\×W}\underset{y=1}{\overset{H}{\mathrm{\Σ}}}\underset{x=1}{\overset{W}{\mathrm{\Σ}}}{S}_L^{\mathrm{LA}}(x,y)\right),$ $S_L^{\mathrm{LP}}(x,y)=\frac{2\×{\mathrm{LP}}_{L_{\mathrm{org}}}(x,y)\×{\mathrm{LP}}_L^{\mathrm{res}}(x,y)+T_1}{{\left({\mathrm{LP}}_{L_{\mathrm{org}}}(x,y)\right)}^2+{\left({\mathrm{LP}}_L^{\mathrm{res}}(x,y)\right)}^2+T_1},$ $S_L^{\mathrm{LA}}(x,y)=\frac{2\×{\mathrm{LA}}_{L_{\mathrm{org}}}(x,y)\×{\mathrm{LA}}_L^{\mathrm{res}}(x,y)+T_2}{{\left({\mathrm{LA}}_{L_{\mathrm{org}}}(x,y)\right)}^2+{\left({\mathrm{LA}}_L^{\mathrm{res}}(x,y)\right)}^2+T_2},$ Wherein, T ₁and T ₂for controling parameters;

According to { R _org(x, y) } and in the local phase characteristic sum local amplitude feature of each pixel, calculate picture quality objective evaluation predicted value, be designated as $Q_R^{\mathrm{LPA}}=\left(\frac{1}{H\×W}\underset{y=1}{\overset{H}{\mathrm{\Σ}}}\underset{x=1}{\overset{W}{\mathrm{\Σ}}}{S}_R^{\mathrm{LP}}(x,y)\right)\×\left(\frac{1}{H\×W}\underset{y=1}{\overset{H}{\mathrm{\Σ}}}\underset{x=1}{\overset{W}{\mathrm{\Σ}}}{S}_R^{\mathrm{LA}}(x,y)\right),$ $S_R^{\mathrm{LP}}(x,y)=\frac{2\×{\mathrm{LP}}_{R_{\mathrm{org}}}(x,y)\×{\mathrm{LP}}_R^{\mathrm{res}}(x,y)+T_1}{{\left({\mathrm{LP}}_{R_{\mathrm{org}}}(x,y)\right)}^2+{\left({\mathrm{LP}}_R^{\mathrm{res}}(x,y)\right)}^2+T_1},$ $S_R^{\mathrm{LA}}(x,y)=\frac{2\×{\mathrm{LA}}_{R_{\mathrm{org}}}(x,y)\×{\mathrm{LA}}_R^{\mathrm{res}}(x,y)+T_2}{{\left({\mathrm{LA}}_{R_{\mathrm{org}}}(x,y)\right)}^2+{\left({\mathrm{LA}}_R^{\mathrm{res}}(x,y)\right)}^2+T_2},$ Wherein, T ₁and T ₂for controling parameters;

5. respectively by { L _org(x, y) } and be divided into the size of individual non-overlapping copies is the sub-block of 8 × 8, then respectively to { L _org(x, y) } in each sub-block and in each sub-block implement singular value decomposition, obtain { L _org(x, y) } in each sub-block singular value vector and in the singular value vector of each sub-block, then to calculate picture quality objective evaluation predicted value, be designated as wherein, N _blockrepresent { L _org(x, y) } in the number of sub-block that comprises, also represent in the number of sub-block that comprises, symbol " || " is the symbol that takes absolute value, " <> " for getting interior Product function, represent { L _org(x, y) } in the singular value vector of a kth sub-block, represent in the singular value vector of a kth sub-block;

Respectively by { R _org(x, y) } and be divided into the size of individual non-overlapping copies is the sub-block of 8 × 8, then respectively to { R _org(x, y) } in each sub-block and in each sub-block implement singular value decomposition, obtain { R _org(x, y) } in each sub-block singular value vector and in the singular value vector of each sub-block, then to calculate picture quality objective evaluation predicted value, be designated as wherein, N ' _blockshow { R _org(x, y) } in the number of sub-block that comprises, also represent in the number of sub-block that comprises, symbol " || " is the symbol that takes absolute value, " <> " for getting interior Product function, represent { R _org(x, y) } in the singular value vector of a kth sub-block, represent in the singular value vector of a kth sub-block;

6. right picture quality objective evaluation predicted value with picture quality objective evaluation predicted value merge, obtain { L _dis(x, y) } picture quality objective evaluation predicted value, be designated as Q _l, and it is right picture quality objective evaluation predicted value with picture quality objective evaluation predicted value merge, obtain { R _dis(x, y) } picture quality objective evaluation predicted value, be designated as Q _r, wherein, w ₁represent with weights proportion, w ₂represent with weights proportion, w ₁+ w ₂=1;

7. to { L _dis(x, y) } picture quality objective evaluation predicted value Q _l{ R _dis(x, y) } picture quality objective evaluation predicted value Q _rmerge, obtain S _dispicture quality objective evaluation predicted value, be designated as Q, $Q=\frac{{(\mathrm{Stagel}\left(Q_L\right)+\mathrm{Stagel}\left(Q_R\right))}^p}{z+{(\mathrm{Stagel}\left(Q_L\right)+\mathrm{Stagel}\left(Q_R\right))}^q},$ $\mathrm{Stagel}\left(Q_L\right)=\frac{{\left(Q_L\right)}^m}{s+Q_L+Q_R},$ $\mathrm{Stagel}\left(Q_R\right)=\frac{{\left(Q_R\right)}^m}{s+Q_L+Q_R},$ Wherein, p, q, m, s and z are model coefficient.

Described step detailed process is 2.:

2.-1, to { L _org(x, y) } implement 3 grades of wavelet transformations, obtain the matrix of wavelet coefficients of 3 directional subbands corresponding to every grade of wavelet transformation, by { L _org(x, y) } implement m level wavelet transformation after the matrix of wavelet coefficients of the n-th directional subband that obtains be designated as wherein, 3 directional subbands are respectively horizontal direction subband, vertical direction subband and diagonal angle directional subband, 1≤m≤3,1≤n≤3, represent middle coordinate position is the wavelet coefficient at (x, y) place;

2.-2, to { L _dis(x, y) } implement 3 grades of wavelet transformations, obtain the matrix of wavelet coefficients of 3 directional subbands corresponding to every grade of wavelet transformation, by { L _dis(x, y) } implement m level wavelet transformation after the matrix of wavelet coefficients of the n-th directional subband that obtains be designated as wherein, 3 directional subbands are respectively horizontal direction subband, vertical direction subband and diagonal angle directional subband, 1≤m≤3,1≤n≤3, represent middle coordinate position is the wavelet coefficient at (x, y) place;

2.-3, according to { L _org(x, y) } matrix of wavelet coefficients of 3 directional subbands that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding and { L _dis(x, y) } matrix of wavelet coefficients of 3 directional subbands that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding, estimate to obtain { L _dis(x, y) } the matrix of wavelet coefficients compensating parameter matrix separately of all directions subband that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding, by { L _dis(x, y) } implement m level wavelet transformation after the matrix of wavelet coefficients of the n-th directional subband that obtains compensating parameter matrix be designated as wherein, represent middle coordinate position is the compensating parameter at (x, y) place, for inputting be truncated to the truncation funcation that [0,1] is interval;

2.-4, according to { L _org(x, y) } the matrix of wavelet coefficients, { L of 3 directional subbands that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding _dis(x, y) } the matrix of wavelet coefficients, { L of 3 directional subbands that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding _dis(x, y) } the matrix of wavelet coefficients compensating parameter matrix separately of all directions subband that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding, calculate { L _dis(x, y) } matrix of wavelet coefficients that obtains after hanging oneself and recovering of the matrix of wavelet coefficients of all directions subband that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding, by { L _dis(x, y) } implement m level wavelet transformation after the matrix of wavelet coefficients of the n-th directional subband that obtains the matrix of wavelet coefficients obtained after recovering is designated as wherein, represent middle coordinate position is the wavelet coefficient at (x, y) place, then to { L _dis(x, y) } matrix of wavelet coefficients that obtains after hanging oneself and recovering of the matrix of wavelet coefficients of all directions subband that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding implements anti-wavelet transformation, obtains { L _dis(x, y) } Recovery image, be designated as wherein, represent middle coordinate position is the pixel value of the pixel of (x, y);

2.-5, according to { L _org(x, y) } the matrix of wavelet coefficients, { L of 3 directional subbands that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding _dis(x, y) } the matrix of wavelet coefficients, { L of 3 directional subbands that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding _dis(x, y) } matrix of wavelet coefficients that the matrix of wavelet coefficients of all directions subband that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding obtains after hanging oneself and recovering, calculate { L _dis(x, y) } matrix of wavelet coefficients of all directions subband that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding to hang oneself the matrix of wavelet coefficients obtained after interference, by { L _dis(x, y) } implement m level wavelet transformation after the matrix of wavelet coefficients of the n-th directional subband that obtains the matrix of wavelet coefficients obtained after interference is designated as wherein, represent middle coordinate position is the wavelet coefficient at (x, y) place, $C_{L_{\mathrm{dis}},m,n}^{\mathrm{int}}(x,y)=C_{L_{\mathrm{org}},m,n}(x,y)+C_{L_{\mathrm{dis}},m,n}(x,y)-C_{L_{\mathrm{dis}},m,n}^{\mathrm{res}}(x,y);$ Then to { L _dis(x, y) } matrix of wavelet coefficients of all directions subband that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations the is corresponding matrix of wavelet coefficients obtained after interference of hanging oneself implements anti-wavelet transformation, obtains { L _dis(x, y) } interfering picture, be designated as wherein, represent middle coordinate position is the pixel value of the pixel of (x, y);

2.-6,2.-1 2.-5 { L are obtained to step according to step _dis(x, y) } Recovery image { L _dis(x, y) } interfering picture operation, in an identical manner obtain { R _dis(x, y) } Recovery image { R _dis(x, y) } interfering picture

Described step detailed process is 3.:

3.-1, adopt log-Garbor filter to { L _org(x, y) } in each pixel carry out filtering process, obtain { L _org(x, y) } in each pixel in the even symmetry frequency response of different scale and different directions and odd symmetry frequency response, by { L _org(x, y) } in coordinate position be that the pixel of (x, y) is designated as e in the even symmetry frequency response of different scale and different directions _{α, θ}(x, y), by { L _org(x, y) } in coordinate position be that the pixel of (x, y) is designated as o in the odd symmetry frequency response of different scale and different directions _{α, θ}(x, y), wherein, α represents the scale factor of log-Garbor filter, 1≤α≤4, and θ represents the direction factor of log-Garbor filter, 1≤θ≤4;

3.-2, { L is calculated _org(x, y) } in each pixel in the phase equalization feature of different directions, by { L _org(x, y) } in coordinate position be that the pixel of (x, y) is designated as PC in the phase equalization feature of different directions _θ(x, y), ${\mathrm{PC}}_{\mathrm{\&theta;}}(x,y)=\frac{E_{\mathrm{\&theta;}}(x,y)}{\underset{\mathrm{\&alpha;}=1}{\overset{4}{\mathrm{\&Sigma;}}}{A}_{\mathrm{\&alpha;},\mathrm{\&theta;}}(x,y)},$ Wherein, $A_{\mathrm{\&alpha;},\mathrm{\&theta;}}(x,y)=\sqrt{e_{\mathrm{\&alpha;},\mathrm{\&theta;}}{(x,y)}^2+o_{\mathrm{\&alpha;},\mathrm{\&theta;}}{(x,y)}^2},$ $E_{\mathrm{\&theta;}}(x,y)=\sqrt{F_{\mathrm{\&theta;}}{(x,y)}^2+H_{\mathrm{\&theta;}}{(x,y)}^2},$ $F_{\mathrm{\&theta;}}(x,y)=\underset{\mathrm{\&alpha;}=1}{\overset{4}{\mathrm{\&Sigma;}}}{e}_{\mathrm{\&alpha;},\mathrm{\&theta;}}(x,y),$ $H_{\mathrm{\&theta;}}(x,y)=\underset{\mathrm{\&alpha;}=1}{\overset{4}{\mathrm{\&Sigma;}}}{o}_{\mathrm{\&alpha;},\mathrm{\&theta;}}(x,y);$

3.-3, according to { L _org(x, y) } in direction corresponding to the maximum phase consistency feature of each pixel, calculate { L _org(x, y) } in the local phase characteristic sum local amplitude feature of each pixel, for { L _org(x, y) } in coordinate position be the pixel of (x, y), first find out its maximum phase consistency feature in the phase equalization feature of different directions, next finds out direction corresponding to this maximum phase consistency feature, is designated as θ _m, again according to θ _mcalculate { L _org(x, y) } in coordinate position be the local phase characteristic sum local amplitude feature of the pixel of (x, y), correspondence is designated as with ${\mathrm{LP}}_{L_{\mathrm{org}}}(x,y)=\arctan (H_{{\mathrm{\&theta;}}_m}(x,y),F_{{\mathrm{\&theta;}}_m}(x,y)),$ ${\mathrm{LA}}_{L_{\mathrm{org}}}(x,y)=\underset{\mathrm{\&alpha;}=1}{\overset{4}{\mathrm{\&Sigma;}}}{A}_{{\mathrm{\&alpha;},\mathrm{\&theta;}}_m}(x,y),$ Wherein, $F_{{\mathrm{\&theta;}}_m}(x,y)=\underset{\mathrm{\&alpha;}=1}{\overset{4}{\mathrm{\&Sigma;}}}{e}_{\mathrm{\&alpha;},{\mathrm{\&theta;}}_m}(x,y),$ $H_{{\mathrm{\&theta;}}_m}(x,y)=\underset{\mathrm{\&alpha;}=1}{\overset{4}{\mathrm{\&Sigma;}}}{o}_{\mathrm{\&alpha;},{\mathrm{\&theta;}}_m}(x,y),$ $A_{\mathrm{\&alpha;},{\mathrm{\&theta;}}_m}(x,y)=\sqrt{e_{\mathrm{\&alpha;},{\mathrm{\&theta;}}_m}{(x,y)}^2+o_{\mathrm{\&alpha;},{\mathrm{\&theta;}}_m}{(x,y)}^2},$ represent { L _org(x, y) } in coordinate position be that the pixel of (x, y) is at different scale and direction θ corresponding to maximum phase consistency feature _meven symmetry frequency response, represent middle coordinate position is that the pixel of (x, y) is at different scale and direction θ corresponding to maximum phase consistency feature _modd symmetry frequency response, arctan () is negate tan;

3.-4,3.-1 3.-3 { L are obtained to step according to step _org(x, y) } in the operation of local phase characteristic sum local amplitude feature of each pixel, obtain { R in an identical manner _org(x, y) }, with in the local phase characteristic sum local amplitude feature of each pixel.

Described step 5. in picture quality objective evaluation predicted value acquisition process be:

5.-1a, respectively by { L _org(x, y) } and be divided into the size of individual non-overlapping copies is the sub-block of 8 × 8, by { L _org(x, y) } in a current pending kth sub-block be defined as current first sub-block, will in the sub-block of current pending kth be defined as current second sub-block, wherein,

5.-2a, current first sub-block is designated as current second sub-block is designated as wherein, (x ₂, y ₂) represent with in the coordinate position of pixel, 1≤x ₂≤ 8,1≤y ₂≤ 8, represent middle coordinate position is (x ₂, y ₂) the pixel value of pixel, represent middle coordinate position is (x ₂, y ₂) the pixel value of pixel;

5.-3a, general be expressed as in the form of vectors right implement singular value decomposition, $D_{L_{\mathrm{org}},k}=U_{L_{\mathrm{org}},k}\&times;S_{L_{\mathrm{org}},k}\&times;{\left(V_{L_{\mathrm{org}},k}\right)}^T,$ Wherein, for left singular vector, for right singular vector, for singular value vector, diagonal on element be singular value, and its value arranges according to order from big to small, for transposed vector;

Will be expressed as in the form of vectors right implement singular value decomposition, wherein, for left singular vector, for right singular vector, for singular value vector, diagonal on element be singular value, and its value arranges according to order from big to small, for transposed vector;

5.-4a, make k=k+1, by { L _org(x, y) } in next pending sub-block as current first sub-block, will the pending sub-block of the middle next one as current second sub-block, then return step 5.-2a continue to perform, until { L _org(x, y) } and in all sub-blocks be all disposed, obtain { L _org(x, y) } in each sub-block singular value vector and in the singular value vector of each sub-block, wherein, "=" in k=k+1 is assignment;

5.-5a, basis { L _org(x, y) } in each sub-block singular value vector and in the singular value vector of each sub-block, calculate picture quality objective evaluation predicted value, be designated as wherein, N _blockrepresent { L _org(x, y) } in the number of sub-block that comprises, also represent in the number of sub-block that comprises, symbol " || " is the symbol that takes absolute value, and " <> " is for getting interior Product function;

Described step 5. in picture quality objective evaluation predicted value acquisition process be:

5.-1b, respectively by { R _org(x, y) } and be divided into the size of individual non-overlapping copies is the sub-block of 8 × 8, by { R _org(x, y) } in a current pending kth sub-block be defined as current first sub-block, will in the sub-block of current pending kth be defined as current second sub-block, wherein,

5.-2b, current first sub-block is designated as current second sub-block is designated as wherein, (x ₂, y ₂) represent with in the coordinate position of pixel, 1≤x ₂≤ 8,1≤y ₂≤ 8, represent middle coordinate position is (x ₂, y ₂) the pixel value of pixel, represent middle coordinate position is (x ₂, y ₂) the pixel value of pixel;

5.-3b, general be expressed as in the form of vectors right implement singular value decomposition, $D_{R_{\mathrm{org}},k}=U_{R_{\mathrm{org}},k}\&times;S_{R_{\mathrm{org}},k}\&times;{\left(V_{R_{\mathrm{org}},k}\right)}^T,$ Wherein, for left singular vector, for right singular vector, for singular value vector, diagonal on element be singular value, and its value arranges according to order from big to small, for transposed vector;

Will be expressed as in the form of vectors right implement singular value decomposition, wherein, for left singular vector, for right singular vector, for singular value vector, diagonal on element be singular value, and its value arranges according to order from big to small, for transposed vector;

5.-4b, make k=k+1, by { R _org(x, y) } in next pending sub-block as current first sub-block, will the pending sub-block of the middle next one as current second sub-block, then return step 5.-2b continue to perform, until { R _org(x, y) } and in all sub-blocks be all disposed, obtain { R _org(x, y) } in each sub-block singular value vector and in the singular value vector of each sub-block, wherein, "=" in k=k+1 is assignment;

5.-5b, basis { R _org(x, y) } in each sub-block singular value vector and in the singular value vector of each sub-block, calculate picture quality objective evaluation predicted value, be designated as wherein, N ' _blcokrepresent { R _org(x, y) } in the number of sub-block that comprises, also represent in the number of sub-block that comprises, symbol " || " is the symbol that takes absolute value, and " <> " is for getting interior Product function.

Described step 4. in get T ₁=0.85, T ₂=160.

Described step 6. in get w ₁=0.9208, w ₂=0.0792.

Described step 7. in get p=7.99, q=6.59, m=1.28, s=0.985 and z=0.077.

Compared with prior art, the invention has the advantages that:

1) the inventive method considers that distortion can cause image detail to be lost or redundant information increases, therefore the stereo-picture of distortion is decomposed into Recovery image and interfering picture, and respectively Recovery image and interfering picture are evaluated, the mass change situation of stereo-picture can be reflected so preferably, make evaluation result more meet human visual system.

2) the inventive method adopts local phase characteristic sum local amplitude feature to evaluate Recovery image, singular value vector is adopted to evaluate interfering picture, token image details and redundant information on the impact of picture quality, thus can effectively can improve the correlation of objective evaluation result and subjective perception well like this.

Accompanying drawing explanation

Fig. 1 be the inventive method totally realize block diagram;

Fig. 2 a is that Akko(is of a size of 640 × 480) the left visual point image of stereo-picture;

Fig. 2 b is that Akko(is of a size of 640 × 480) the right visual point image of stereo-picture;

Fig. 3 a is that Altmoabit(is of a size of 1024 × 768) the left visual point image of stereo-picture;

Fig. 3 b is that Altmoabit(is of a size of 1024 × 768) the right visual point image of stereo-picture;

Fig. 4 a is that Balloons(is of a size of 1024 × 768) the left visual point image of stereo-picture;

Fig. 4 b is that Balloons(is of a size of 1024 × 768) the right visual point image of stereo-picture;

Fig. 5 a is that Doorflower(is of a size of 1024 × 768) the left visual point image of stereo-picture;

Fig. 5 b is that Doorflower(is of a size of 1024 × 768) the right visual point image of stereo-picture;

Fig. 6 a is that Kendo(is of a size of 1024 × 768) the left visual point image of stereo-picture;

Fig. 6 b is that Kendo(is of a size of 1024 × 768) the right visual point image of stereo-picture;

Fig. 7 a is that LeaveLaptop(is of a size of 1024 × 768) the left visual point image of stereo-picture;

Fig. 7 b is that LeaveLaptop(is of a size of 1024 × 768) the right visual point image of stereo-picture;

Fig. 8 a is that Lovebierd1(is of a size of 1024 × 768) the left visual point image of stereo-picture;

Fig. 8 b is that Lovebierd1(is of a size of 1024 × 768) the right visual point image of stereo-picture;

Fig. 9 a is that Newspaper(is of a size of 1024 × 768) the left visual point image of stereo-picture;

Fig. 9 b is that Newspaper(is of a size of 1024 × 768) the right visual point image of stereo-picture;

Figure 10 a is that Xmas(is of a size of 640 × 480) the left visual point image of stereo-picture;

Figure 10 b is that Xmas(is of a size of 640 × 480) the right visual point image of stereo-picture;

Figure 11 is that the picture quality objective evaluation predicted value of the stereo-picture of each width distortion in the set of distortion stereo-picture and mean subjective are marked the scatter diagram of difference.

Embodiment

Below in conjunction with accompanying drawing embodiment, the present invention is described in further detail.

A kind of objective evaluation method for quality of stereo images based on picture breakdown that the present invention proposes, it totally realizes block diagram as shown in Figure 1, and its processing procedure is:

First, 3 grades of wavelet transformations are implemented to the left visual point image of the left visual point image of original undistorted stereo-picture and the stereo-picture of distortion to be evaluated, again according to the matrix of wavelet coefficients of 3 directional subbands corresponding to every grade of wavelet transformation obtaining, obtain Recovery image and the interfering picture of the left visual point image of the stereo-picture of distortion to be evaluated; Equally, 3 grades of wavelet transformations are implemented to the right visual point image of the right visual point image of original undistorted stereo-picture and the stereo-picture of distortion to be evaluated, again according to the matrix of wavelet coefficients of 3 directional subbands corresponding to every grade of wavelet transformation obtaining, obtain Recovery image and the interfering picture of the right visual point image of the stereo-picture of distortion to be evaluated;

Secondly, by the local phase characteristic sum local amplitude feature of each pixel in the Recovery image of the left visual point image of the stereo-picture of the left visual point image and distortion to be evaluated that calculate original undistorted stereo-picture, obtain the picture quality objective evaluation predicted value of the Recovery image of the left visual point image of the stereo-picture of distortion to be evaluated; Equally, by the local phase characteristic sum local amplitude feature of each pixel in the Recovery image of the right visual point image of the stereo-picture of the right visual point image and distortion to be evaluated that calculate original undistorted stereo-picture, obtain the picture quality objective evaluation predicted value of the Recovery image of the right visual point image of the stereo-picture of distortion to be evaluated;

Then, by the singular value vector that each size in the interfering picture of the left visual point image of the stereo-picture of the left visual point image and distortion to be evaluated that calculate original undistorted stereo-picture is the sub-block of 8 × 8, obtain the picture quality objective evaluation predicted value of the interfering picture of the left visual point image of the stereo-picture of distortion to be evaluated; Equally, by the singular value vector that each size in the interfering picture of the right visual point image of the stereo-picture of the right visual point image and distortion to be evaluated that calculate original undistorted stereo-picture is the sub-block of 8 × 8, obtain the picture quality objective evaluation predicted value of the interfering picture of the right visual point image of the stereo-picture of distortion to be evaluated;

Afterwards, the Recovery image of the left visual point image of the stereo-picture of distortion to be evaluated and the picture quality objective evaluation predicted value of interfering picture are merged, obtains the picture quality objective evaluation predicted value of the left visual point image of the stereo-picture of distortion to be evaluated; Equally, the Recovery image of the right visual point image of the stereo-picture of distortion to be evaluated and the picture quality objective evaluation predicted value of interfering picture are merged, obtains the picture quality objective evaluation predicted value of the right visual point image of the stereo-picture of distortion to be evaluated;

Moreover, the left visual point image of the stereo-picture of distortion to be evaluated and the picture quality objective evaluation predicted value of right visual point image are merged, obtains the picture quality objective evaluation predicted value of the stereo-picture of distortion to be evaluated;

Finally, adopt the undistorted stereo-picture that several are original, set up its distortion stereo-picture set under the different distortion level of different type of distortion, this distortion stereo-picture set comprises the stereo-picture of several distortions, then calculates the picture quality objective evaluation predicted value of the stereo-picture of every width distortion in this distortion stereo-picture set respectively according to the process of the picture quality objective evaluation predicted value of the stereo-picture of above-mentioned acquisition distortion to be evaluated.

Objective evaluation method for quality of stereo images of the present invention, specifically comprises the following steps:

1. S is made _orgfor original undistorted stereo-picture, make S _disfor the stereo-picture of distortion to be evaluated, by S _orgleft visual point image be designated as { L _org(x, y) }, by S _orgright visual point image be designated as { R _org(x, y) }, by S _disleft visual point image be designated as { L _dis(x, y) }, by S _disright visual point image be designated as { R _dis(x, y) }, wherein, (x, y) coordinate position of the pixel in left visual point image and right visual point image is represented, 1≤x≤W, 1≤y≤H, W represents the width of left visual point image and right visual point image, and H represents the height of left visual point image and right visual point image, L _org(x, y) represents { L _org(x, y) } in coordinate position be the pixel value of the pixel of (x, y), R _org(x, y) represents { R _org(x, y) } in coordinate position be the pixel value of the pixel of (x, y), L _dis(x, y) represents { L _dis(x, y) } in coordinate position be the pixel value of the pixel of (x, y), R _dis(x, y) represents { R _dis(x, y) } in coordinate position be the pixel value of the pixel of (x, y).

2. can distinguish due to distortion and introduce information dropout distortion (information-loss distortion) and information interpolation distortion (information-additive distortion) in the picture, two kinds of distortion impacts on perceived quality are different, such as information dropout distortion can cause binocular to suppress, and the impact of information interpolation distortion on perception is not very large, therefore the inventive method is respectively to { L _org(x, y) } and { L _dis(x, y) } implement 3 grades of wavelet transformations, then according to { L _org(x, y) } matrix of wavelet coefficients of 3 directional subbands that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding and { L _dis(x, y) } matrix of wavelet coefficients of 3 directional subbands that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding, obtain { L _dis(x, y) } Recovery image and interfering picture, correspondence is designated as with wherein, represent middle coordinate position is the pixel value of the pixel of (x, y), represent middle coordinate position is the pixel value of the pixel of (x, y); Respectively to { R _org(x, y) } and { R _dis(x, y) } implement 3 grades of wavelet transformations, then according to { R _org(x, y) } matrix of wavelet coefficients of 3 directional subbands that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding and { R _dis(x, y) } matrix of wavelet coefficients of 3 directional subbands that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding, obtain { R _dis(x, y) } Recovery image and interfering picture, correspondence is designated as with wherein, represent middle coordinate position is the pixel value of the pixel of (x, y), represent middle coordinate position is the pixel value of the pixel of (x, y).

In this particular embodiment, step detailed process is 2.:

2.-1, to { L _org(x, y) } implement 3 grades of wavelet transformations, obtain the matrix of wavelet coefficients of 3 directional subbands corresponding to every grade of wavelet transformation, by { L _org(x, y) } implement m level wavelet transformation after the matrix of wavelet coefficients of the n-th directional subband that obtains be designated as wherein, 3 directional subbands are respectively horizontal direction subband, vertical direction subband and diagonal angle directional subband, 1≤m≤3,1≤n≤3, during n=1, the n-th directional subband is horizontal direction subband, and during n=2, the n-th directional subband is vertical direction subband, during n=3, the n-th directional subband is diagonal angle directional subband represent middle coordinate position is the wavelet coefficient at (x, y) place.

2.-2, to { L _dis(x, y) } implement 3 grades of wavelet transformations, obtain the matrix of wavelet coefficients of 3 directional subbands corresponding to every grade of wavelet transformation, by { L _dis(x, y) } implement m level wavelet transformation after the matrix of wavelet coefficients of the n-th directional subband that obtains be designated as wherein, 3 directional subbands are respectively horizontal direction subband, vertical direction subband and diagonal angle directional subband, 1≤m≤3,1≤n≤3, during n=1, the n-th directional subband is horizontal direction subband, and during n=2, the n-th directional subband is vertical direction subband, during n=3, the n-th directional subband is diagonal angle directional subband represent middle coordinate position is the wavelet coefficient at (x, y) place.

2.-3, according to { L _org(x, y) } matrix of wavelet coefficients of 3 directional subbands that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding and { L _dis(x, y) } matrix of wavelet coefficients of 3 directional subbands that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding, estimate to obtain { L _dis(x, y) } the matrix of wavelet coefficients compensating parameter matrix separately of all directions subband that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding, by { L _dis(x, y) } implement m level wavelet transformation after the matrix of wavelet coefficients of the n-th directional subband that obtains compensating parameter matrix be designated as wherein, represent middle coordinate position is the compensating parameter at (x, y) place, namely represents middle coordinate position is the wavelet coefficient at (x, y) place compensating parameter, for inputting be truncated to the truncation funcation that [0,1] is interval.

2.-4, according to { L _org(x, y) } the matrix of wavelet coefficients, { L of 3 directional subbands that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding _dis(x, y) } the matrix of wavelet coefficients, { L of 3 directional subbands that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding _dis(x, y) } the matrix of wavelet coefficients compensating parameter matrix separately of all directions subband that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding, calculate { L _dis(x, y) } matrix of wavelet coefficients that obtains after hanging oneself and recovering of the matrix of wavelet coefficients of all directions subband that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding, by { L _dis(x, y) } implement m level wavelet transformation after the matrix of wavelet coefficients of the n-th directional subband that obtains the matrix of wavelet coefficients obtained after recovering is designated as wherein, represent middle coordinate position is the wavelet coefficient at (x, y) place, then to { L _dis(x, y) } matrix of wavelet coefficients that obtains after hanging oneself and recovering of the matrix of wavelet coefficients of all directions subband that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding implements anti-wavelet transformation, obtains { L _dis(x, y) } Recovery image, be designated as wherein, represent middle coordinate position is the pixel value of the pixel of (x, y).

2.-5, according to { L _org(x, y) } the matrix of wavelet coefficients, { L of 3 directional subbands that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding _dis(x, y) } the matrix of wavelet coefficients, { L of 3 directional subbands that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding _dis(x, y) } matrix of wavelet coefficients that the matrix of wavelet coefficients of all directions subband that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding obtains after hanging oneself and recovering, calculate { L _dis(x, y) } matrix of wavelet coefficients of all directions subband that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding to hang oneself the matrix of wavelet coefficients obtained after interference, by { L _dis(x, y) } implement m level wavelet transformation after the matrix of wavelet coefficients of the n-th directional subband that obtains the matrix of wavelet coefficients obtained after interference is designated as wherein, represent middle coordinate position is the wavelet coefficient at (x, y) place, $C_{L_{\mathrm{dis}},m,n}^{\mathrm{int}}(x,y)=C_{L_{\mathrm{org}},m,n}(x,y)+C_{L_{\mathrm{dis}},m,n}(x,y)-C_{L_{\mathrm{dis}},m,n}^{\mathrm{res}}(x,y);$ Then to { L _dis(x, y) } matrix of wavelet coefficients of all directions subband that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations the is corresponding matrix of wavelet coefficients obtained after interference of hanging oneself implements anti-wavelet transformation, obtains { L _dis(x, y) } interfering picture, be designated as wherein, represent middle coordinate position is the pixel value of the pixel of (x, y).

2.-6,2.-1 2.-5 { L are obtained to step according to step _dis(x, y) } Recovery image { L _dis(x, y) } interfering picture operation, in an identical manner obtain { R _dis(x, y) } Recovery image { R _dis(x, y) } interfering picture namely detailed process is: 1) to { R _org(x, y) } implement 3 grades of wavelet transformations, obtain the matrix of wavelet coefficients of 3 directional subbands corresponding to every grade of wavelet transformation, by { R _org(x, y) } implement m level wavelet transformation after the matrix of wavelet coefficients of the n-th directional subband that obtains be designated as represent middle coordinate position is the wavelet coefficient at (x, y) place; 2) to { R _dis(x, y) } implement 3 grades of wavelet transformations, obtain the matrix of wavelet coefficients of 3 directional subbands corresponding to every grade of wavelet transformation, by { R _dis(x, y) } implement m level wavelet transformation after the matrix of wavelet coefficients of the n-th directional subband that obtains be designated as represent middle coordinate position is the wavelet coefficient at (x, y) place; 3) { R is calculated _dis(x, y) } the matrix of wavelet coefficients compensating parameter matrix separately of all directions subband that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding, by { R _dis(x, y) } implement m level wavelet transformation after the matrix of wavelet coefficients of the n-th directional subband that obtains compensating parameter matrix be designated as wherein represent middle coordinate position is the compensating parameter at (x, y) place, 4) { R is calculated _dis(x, y) } matrix of wavelet coefficients that obtains after hanging oneself and recovering of the matrix of wavelet coefficients of all directions subband that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding, by { R _dis(x, y) } implement m level wavelet transformation after the matrix of wavelet coefficients of the n-th directional subband that obtains the matrix of wavelet coefficients obtained after recovering is designated as wherein, represent middle coordinate position is the wavelet coefficient at (x, y) place, then to { R _dis(x, y) } matrix of wavelet coefficients that obtains after hanging oneself and recovering of the matrix of wavelet coefficients of all directions subband that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding implements anti-wavelet transformation, obtains { R _dis(x, y) } Recovery image, be designated as wherein, represent middle coordinate position is the pixel value of the pixel of (x, y); 5) { R is calculated _dis(x, y) } matrix of wavelet coefficients of all directions subband that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding to hang oneself the matrix of wavelet coefficients obtained after interference, by { R _dis(x, y) } implement m level wavelet transformation after the matrix of wavelet coefficients of the n-th directional subband that obtains the matrix of wavelet coefficients obtained after interference is designated as wherein, represent middle coordinate position is the wavelet coefficient at (x, y) place, $C_{R_{\mathrm{dis}},m,n}^{\mathrm{int}}(x,y)=C_{R_{\mathrm{org}},m,n}(x,y)+C_{R_{\mathrm{dis}},m,n}(x,y)-C_{R_{\mathrm{dis}},m,n}^{\mathrm{res}}(x,y);$ Then to { R _dis(x, y) } matrix of wavelet coefficients of all directions subband that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations the is corresponding matrix of wavelet coefficients obtained after interference of hanging oneself implements anti-wavelet transformation, obtains { R _dis(x, y) } interfering picture, be designated as wherein, represent middle coordinate position is the pixel value of the pixel of (x, y).

3. { L is calculated respectively _org(x, y) }, { R _org(x, y) }, with in the local phase characteristic sum local amplitude feature of each pixel, by { L _org(x, y) } in coordinate position be that the local phase feature of the pixel of (x, y) is designated as by { L _org(x, y) } in coordinate position be that the local amplitude feature of the pixel of (x, y) is designated as by { R _org(x, y) } in coordinate position be that the local phase feature of the pixel of (x, y) is designated as by { R _org(x, y) } in coordinate position be that the local amplitude feature of the pixel of (x, y) is designated as will middle coordinate position is that the local phase feature of the pixel of (x, y) is designated as will middle coordinate position is that the local amplitude feature of the pixel of (x, y) is designated as will middle coordinate position is that the local phase feature of the pixel of (x, y) is designated as will middle coordinate position is that the local amplitude feature of the pixel of (x, y) is designated as

In this particular embodiment, step detailed process is 3.:

3.-1, adopt log-Garbor filter to { L _org(x, y) } in each pixel carry out filtering process, obtain { L _org(x, y) } in each pixel in the even symmetry frequency response of different scale and different directions and odd symmetry frequency response, by { L _org(x, y) } in coordinate position be that the pixel of (x, y) is designated as e in the even symmetry frequency response of different scale and different directions _{α, θ}(x, y), by { L _org(x, y) } in coordinate position be that the pixel of (x, y) is designated as o in the odd symmetry frequency response of different scale and different directions _{α, θ}(x, y), wherein, α represents the scale factor of log-Garbor filter, 1≤α≤4, and θ represents the direction factor of log-Garbor filter, 1≤θ≤4.

3.-2, { L is calculated _org(x, y) } in each pixel in the phase equalization feature of different directions, by { L _org(x, y) } in coordinate position be that the pixel of (x, y) is designated as PC in the phase equalization feature of different directions _θ(x, y), ${\mathrm{PC}}_{\mathrm{\&theta;}}(x,y)=\frac{E_{\mathrm{\&theta;}}(x,y)}{\underset{\mathrm{\&alpha;}=1}{\overset{4}{\mathrm{\&Sigma;}}}{A}_{\mathrm{\&alpha;},\mathrm{\&theta;}}(x,y)},$ Wherein, $A_{\mathrm{\&alpha;},\mathrm{\&theta;}}(x,y)=\sqrt{e_{\mathrm{\&alpha;},\mathrm{\&theta;}}{(x,y)}^2+o_{\mathrm{\&alpha;},\mathrm{\&theta;}}{(x,y)}^2},$ $E_{\mathrm{\&theta;}}(x,y)=\sqrt{F_{\mathrm{\&theta;}}{(x,y)}^2+H_{\mathrm{\&theta;}}{(x,y)}^2},$ $F_{\mathrm{\&theta;}}(x,y)=\underset{\mathrm{\&alpha;}=1}{\overset{4}{\mathrm{\&Sigma;}}}{e}_{\mathrm{\&alpha;},\mathrm{\&theta;}}(x,y),$ $H_{\mathrm{\&theta;}}(x,y)=\underset{\mathrm{\&alpha;}=1}{\overset{4}{\mathrm{\&Sigma;}}}{o}_{\mathrm{\&alpha;},\mathrm{\&theta;}}(x,y).$

3.-3, according to { L _org(x, y) } in direction corresponding to the maximum phase consistency feature of each pixel, calculate { L _org(x, y) } in the local phase characteristic sum local amplitude feature of each pixel, for { L _org(x, y) } in coordinate position be the pixel of (x, y), first find out its maximum phase consistency feature in the phase equalization feature of different directions, next finds out direction corresponding to this maximum phase consistency feature, is designated as θ _m, again according to θ _mcalculate { L _org(x, y) } in coordinate position be the local phase characteristic sum local amplitude feature of the pixel of (x, y), correspondence is designated as with ${\mathrm{LP}}_{L_{\mathrm{org}}}(x,y)=\arctan (H_{{\mathrm{\&theta;}}_m}(x,y),F_{{\mathrm{\&theta;}}_m}(x,y)),$ ${\mathrm{LA}}_{L_{\mathrm{org}}}(x,y)=\underset{\mathrm{\&alpha;}=1}{\overset{4}{\mathrm{\&Sigma;}}}{A}_{{\mathrm{\&alpha;},\mathrm{\&theta;}}_m}(x,y),$ Wherein, $F_{{\mathrm{\&theta;}}_m}(x,y)=\underset{\mathrm{\&alpha;}=1}{\overset{4}{\mathrm{\&Sigma;}}}{e}_{\mathrm{\&alpha;},{\mathrm{\&theta;}}_m}(x,y),$ $H_{{\mathrm{\&theta;}}_m}(x,y)=\underset{\mathrm{\&alpha;}=1}{\overset{4}{\mathrm{\&Sigma;}}}{o}_{\mathrm{\&alpha;},{\mathrm{\&theta;}}_m}(x,y),$ $A_{\mathrm{\&alpha;},{\mathrm{\&theta;}}_m}(x,y)=\sqrt{e_{\mathrm{\&alpha;},{\mathrm{\&theta;}}_m}{(x,y)}^2+o_{\mathrm{\&alpha;},{\mathrm{\&theta;}}_m}{(x,y)}^2},$ represent { L _org(x, y) } in coordinate position be that the pixel of (x, y) is at different scale and direction θ corresponding to maximum phase consistency feature _meven symmetry frequency response, represent { L _org(x, y) } in coordinate position be that the pixel of (x, y) is at different scale and direction θ corresponding to maximum phase consistency feature _modd symmetry frequency response, arctan () is negate tan.

3.-4,3.-1 3.-3 { L are obtained to step according to step _org(x, y) } in the operation of local phase characteristic sum local amplitude feature of each pixel, obtain { R in an identical manner _org(x, y) }, with in the local phase characteristic sum local amplitude feature of each pixel.As: obtain in the detailed process of local phase characteristic sum local amplitude feature of each pixel be: 1) adopt log-Garbor filter pair in each pixel carry out filtering process, obtain in each pixel in the even symmetry frequency response of different scale and different directions and odd symmetry frequency response, will middle coordinate position is that the pixel of (x, y) is designated as e' in the even symmetry frequency response of different scale and different directions _{α, θ}(x, y), will middle coordinate position is that the pixel of (x, y) is designated as o' in the odd symmetry frequency response of different scale and different directions _{α, θ}(x, y); 2) calculate in each pixel in the phase equalization feature of different directions, will middle coordinate position is that the pixel of (x, y) is designated as PC' in the phase equalization feature of different directions _θ(x, y), wherein, $A_{\mathrm{\&alpha;},\mathrm{\&theta;}}^{\&prime;}(x,y)=\sqrt{e_{\mathrm{\&alpha;},\mathrm{\&theta;}}^{\&prime;}{(x,y)}^2+o_{\mathrm{\&alpha;},\mathrm{\&theta;}}^{\&prime;}{(x,y)}^2},$ $E_{\mathrm{\&theta;}}^{\&prime;}(x,y)=\sqrt{F_{\mathrm{\&theta;}}^{\&prime;}{(x,y)}^2+H_{\mathrm{\&theta;}}^{\&prime;}{(x,y)}^2},$ ${F^{\&prime;}}_{\mathrm{\&theta;}}(x,y)=\underset{\mathrm{\&alpha;}=1}{\overset{4}{\mathrm{\&Sigma;}}}{e^{\&prime;}}_{\mathrm{\&alpha;},\mathrm{\&theta;}}(x,y),$ ${H^{\&prime;}}_{\mathrm{\&theta;}}(x,y)=\underset{\mathrm{\&alpha;}=1}{\overset{4}{\mathrm{\&Sigma;}}}{o^{\&prime;}}_{\mathrm{\&alpha;},\mathrm{\&theta;}}(x,y);$ 3) basis in direction corresponding to the maximum phase consistency feature of each pixel, calculate in the local phase characteristic sum local amplitude feature of each pixel, for middle coordinate position is the pixel of (x, y), and first find out its maximum phase consistency feature in the phase equalization feature of different directions, next finds out direction corresponding to this maximum phase consistency feature, is designated as θ _m, again according to θ _mcalculate middle coordinate position is the local phase characteristic sum local amplitude feature of the pixel of (x, y), and correspondence is designated as with ${\mathrm{LP}}_L^{\mathrm{res}}(x,y)=\arctan (H_{{\mathrm{\&theta;}}_m}^{\&prime;}(x,y),F_{{\mathrm{\&theta;}}_m}^{\&prime;}(x,y)),$ ${\mathrm{LA}}_L^{\mathrm{res}}(x,y)=\underset{\mathrm{\&alpha;}=1}{\overset{4}{\mathrm{\&Sigma;}}}{A}_{\mathrm{\&alpha;},{\mathrm{\&theta;}}_m}^{\&prime;}(x,y),$ Wherein, $F_{{\mathrm{\&theta;}}_m}^{\&prime;}(x,y)=\underset{\mathrm{\&alpha;}=1}{\overset{4}{\mathrm{\&Sigma;}}}{e}_{\mathrm{\&alpha;},{\mathrm{\&theta;}}_m}^{\&prime;}(x,y),$ $H_{{\mathrm{\&theta;}}_m}^{\&prime;}(x,y)=\underset{\mathrm{\&alpha;}=1}{\overset{4}{\mathrm{\&Sigma;}}}{o}_{\mathrm{\&alpha;},{\mathrm{\&theta;}}_m}^{\&prime;}(x,y),$ $A_{\mathrm{\&alpha;},{\mathrm{\&theta;}}_m}^{\&prime;}(x,y)=\sqrt{e_{\mathrm{\&alpha;},{\mathrm{\&theta;}}_m}^{\&prime;}{(x,y)}^2+o_{\mathrm{\&alpha;},{\mathrm{\&theta;}}_m}^{\&prime;}{(x,y)}^2},$ represent middle coordinate position is that the pixel of (x, y) is at different scale and direction θ corresponding to maximum phase consistency feature _meven symmetry frequency response, represent middle coordinate position is that the pixel of (x, y) is at different scale and direction θ corresponding to maximum phase consistency feature _modd symmetry frequency response.

4. compared with original image, Recovery image can introduce information dropout distortion (information-loss distortion), and local phase characteristic sum local amplitude feature can evaluate image detail information change well, and therefore the inventive method is according to { L _org(x, y) } and in the local phase characteristic sum local amplitude feature of each pixel, calculate picture quality objective evaluation predicted value, be designated as $Q_L^{\mathrm{LPA}}=\left(\frac{1}{H\&times;W}\underset{y=1}{\overset{H}{\mathrm{\&Sigma;}}}\underset{x=1}{\overset{W}{\mathrm{\&Sigma;}}}{S}_L^{\mathrm{LP}}(x,y)\right)\&times;\left(\frac{1}{H\&times;W}\underset{y=1}{\overset{H}{\mathrm{\&Sigma;}}}\underset{x=1}{\overset{W}{\mathrm{\&Sigma;}}}{S}_L^{\mathrm{LA}}(x,y)\right),$ $S_L^{\mathrm{LP}}(x,y)=\frac{2\&times;{\mathrm{LP}}_{L_{\mathrm{org}}}(x,y)\&times;{\mathrm{LP}}_L^{\mathrm{res}}(x,y)+T_1}{{\left({\mathrm{LP}}_{L_{\mathrm{org}}}(x,y)\right)}^2+{\left({\mathrm{LP}}_L^{\mathrm{res}}(x,y)\right)}^2+T_1},$ $S_L^{\mathrm{LA}}(x,y)=\frac{2\&times;{\mathrm{LA}}_{L_{\mathrm{org}}}(x,y)\&times;{\mathrm{LA}}_L^{\mathrm{res}}(x,y)+T_2}{{\left({\mathrm{LA}}_{L_{\mathrm{org}}}(x,y)\right)}^2+{\left({\mathrm{LA}}_L^{\mathrm{res}}(x,y)\right)}^2+T_2},$ Wherein, T ₁and T ₂for controling parameters; And according to with in the local phase characteristic sum local amplitude feature of each pixel, calculate picture quality objective evaluation predicted value, be designated as $Q_R^{\mathrm{LPA}}=\left(\frac{1}{H\&times;W}\underset{y=1}{\overset{H}{\mathrm{\&Sigma;}}}\underset{x=1}{\overset{W}{\mathrm{\&Sigma;}}}{S}_R^{\mathrm{LP}}(x,y)\right)\&times;\left(\frac{1}{H\&times;W}\underset{y=1}{\overset{H}{\mathrm{\&Sigma;}}}\underset{x=1}{\overset{W}{\mathrm{\&Sigma;}}}{S}_R^{\mathrm{LA}}(x,y)\right),$ $S_R^{\mathrm{LP}}(x,y)=\frac{2\&times;{\mathrm{LP}}_{R_{\mathrm{org}}}(x,y)\&times;{\mathrm{LP}}_R^{\mathrm{res}}(x,y)+T_1}{{\left({\mathrm{LP}}_{R_{\mathrm{org}}}(x,y)\right)}^2+{\left({\mathrm{LP}}_R^{\mathrm{res}}(x,y)\right)}^2+T_1},$ $S_R^{\mathrm{LA}}(x,y)=\frac{2\&times;{\mathrm{LA}}_{R_{\mathrm{org}}}(x,y)\&times;{\mathrm{LA}}_R^{\mathrm{res}}(x,y)+T_2}{{\left({\mathrm{LA}}_{R_{\mathrm{org}}}(x,y)\right)}^2+{\left({\mathrm{LA}}_R^{\mathrm{res}}(x,y)\right)}^2+T_2},$ Wherein, T ₁and T ₂for controling parameters.In the present embodiment, T is got ₁=0.85, T ₂=160.

5. interfering picture can be introduced information and adds distortion (information-additive distortion), and singular value can Description Image energy well, and the change of singular value can be utilized to reflect redundant information, and therefore the inventive method is respectively by { L _org(x, y) } and be divided into the size of individual non-overlapping copies is the sub-block of 8 × 8, then respectively to { L _org(x, y) } in each sub-block and in each sub-block implement singular value decomposition, obtain { L _org(x, y) } in each sub-block singular value vector and in the singular value vector of each sub-block, then to calculate picture quality objective evaluation predicted value, be designated as $Q_L^{\mathrm{SVD}}=\frac{1}{N_{\mathrm{block}}}\underset{k=1}{\overset{N_{\mathrm{block}}}{\mathrm{\&Sigma;}}}{\mathrm{\&tau;}}_k,$ ${\mathrm{\&tau;}}_k=\frac{<|S_{L_{\mathrm{org}},k}-S_{L,k}^{\mathrm{int}}|\&CenterDot;S_{L_{\mathrm{org}},k}>}{<S_{L_{\mathrm{org}},k}\&CenterDot;S_{L_{\mathrm{org}},k}>},$ Wherein, N _blockrepresent { L _org(x, y) } in the number of sub-block that comprises, also represent in the number of sub-block that comprises, symbol " || " is the symbol that takes absolute value, " <> " for getting interior Product function, represent { L _org(x, y) } in the singular value vector of a kth sub-block, represent in the singular value vector of a kth sub-block; Equally, respectively will with be divided into the size of individual non-overlapping copies is the sub-block of 8 × 8, then respectively to { R _org(x, y) } in each sub-block and in each sub-block implement singular value decomposition, obtain { R _org(x, y) } in each sub-block singular value vector and in the singular value vector of each sub-block, then to calculate picture quality objective evaluation predicted value, be designated as wherein, N ' _blockrepresent { R _org(x, y) } in the number of sub-block that comprises, also represent in the number of sub-block that comprises, symbol " || " is the symbol that takes absolute value, " <> " for getting interior Product function, represent in the singular value vector of a kth sub-block, represent in the singular value vector of a kth sub-block.

In this particular embodiment, step 5. in picture quality objective evaluation predicted value acquisition process be:

5.-1a, respectively by { L _org(x, y) } and be divided into the size of individual non-overlapping copies is the sub-block of 8 × 8, by { L _org(x, y) } in a current pending kth sub-block be defined as current first sub-block, will in the sub-block of current pending kth be defined as current second sub-block, wherein,

5.-2a, current first sub-block is designated as current second sub-block is designated as wherein, (x ₂, y ₂) represent with in the coordinate position of pixel, 1≤x ₂≤ 8,1≤y ₂≤ 8, represent middle coordinate position is (x ₂, y ₂) the pixel value of pixel, represent middle coordinate position is (x ₂, y ₂) the pixel value of pixel.

5.-3a, general be expressed as in the form of vectors right implement singular value decomposition, $D_{L_{\mathrm{org}},k}=U_{L_{\mathrm{org}},k}\&times;S_{L_{\mathrm{org}},k}\&times;{\left(V_{L_{\mathrm{org}},k}\right)}^T,$ Wherein, for left singular vector, for right singular vector, for singular value vector, diagonal on element be singular value, and its value arranges according to order from big to small, for transposed vector; Will be expressed as in the form of vectors right implement singular value decomposition, wherein, for left singular vector, for right singular vector, for singular value vector, diagonal on element be singular value, and its value arranges according to order from big to small, for transposed vector.

5.-4a, make k=k+1, by { L _org(x, y) } in next pending sub-block as current first sub-block, will the pending sub-block of the middle next one as current second sub-block, then return step 5.-2a continue to perform, until { L _org(x, y) } and in all sub-blocks be all disposed, obtain { L _org(x, y) } in each sub-block singular value vector and in the singular value vector of each sub-block, wherein, "=" in k=k+1 is assignment.

5.-5a, basis { L _org(x, y) } in each sub-block singular value vector and in the singular value vector of each sub-block, calculate picture quality objective evaluation predicted value, be designated as wherein, N _blockrepresent { L _org(x, y) } in the number of sub-block that comprises, also represent in the number of sub-block that comprises, symbol " || " is the symbol that takes absolute value, and " <> " is for getting interior Product function.

In this particular embodiment, step 5. in picture quality objective evaluation predicted value acquisition process be:

5.-1b, respectively by { R _org(x, y) } and be divided into the size of individual non-overlapping copies is the sub-block of 8 × 8, by { R _org(x, y) } in a current pending kth sub-block be defined as current first sub-block, will in the sub-block of current pending kth be defined as current second sub-block, wherein,

5.-2b, current first sub-block is designated as current second sub-block is designated as wherein, (x ₂, y ₂) represent with in the coordinate position of pixel, 1≤x ₂≤ 8,1≤y ₂≤ 8, represent middle coordinate position is (x ₂, y ₂) the pixel value of pixel, represent middle coordinate position is (x ₂, y ₂) the pixel value of pixel.

5.-3b, general be expressed as in the form of vectors right implement singular value decomposition, $D_{R_{\mathrm{org}},k}=U_{R_{\mathrm{org}},k}\&times;S_{R_{\mathrm{org}},k}\&times;{\left(V_{R_{\mathrm{org}},k}\right)}^T,$ Wherein, for left singular vector, for right singular vector, for singular value vector, diagonal on element be singular value, and its value arranges according to order from big to small, for transposed vector; Will be expressed as in the form of vectors right implement singular value decomposition, wherein, for left singular vector, for right singular vector, for singular value vector, diagonal on element be singular value, and its value arranges according to order from big to small, for transposed vector.

5.-4b, make k=k+1, by { R _org(x, y) } in next pending sub-block as current first sub-block, will the pending sub-block of the middle next one as current second sub-block, then return step 5.-2b continue to perform, until { R _org(x, y) } and in all sub-blocks be all disposed, obtain { R _org(x, y) } in each sub-block singular value vector and in the singular value vector of each sub-block, wherein, "=" in k=k+1 is assignment.

5.-5b, basis { R _org(x, y) } in each sub-block singular value vector and in the singular value vector of each sub-block, calculate picture quality objective evaluation predicted value, be designated as wherein, N ' _blockrepresent { R _org(x, y) } in the number of sub-block that comprises, also represent in the number of sub-block that comprises, symbol " || " is the symbol that takes absolute value, and " <> " is for getting interior Product function.

6. right picture quality objective evaluation predicted value with picture quality objective evaluation predicted value merge, obtain { L _dis(x, y) } picture quality objective evaluation predicted value, be designated as Q _l, and it is right picture quality objective evaluation predicted value with picture quality objective evaluation predicted value merge, obtain { R _dis(x, y) } picture quality objective evaluation predicted value, be designated as wherein, w ₁represent with weights proportion, w ₂represent with weights proportion, w ₁+ w ₂=1, in the present embodiment, get w ₁=0.9208, w ₂=0.0792.

7. to { L _dis(x, y) } picture quality objective evaluation predicted value Q _l{ R _dis(x, y) } picture quality objective evaluation predicted value Q _rmerge, obtain S _dispicture quality objective evaluation predicted value, be designated as Q, $Q=\frac{{(\mathrm{Stagel}\left(Q_L\right)+\mathrm{Stagel}\left(Q_R\right))}^p}{z+{(\mathrm{Stagel}\left(Q_L\right)+\mathrm{Stagel}\left(Q_R\right))}^q},$ $\mathrm{Stagel}\left(Q_L\right)=\frac{{\left(Q_L\right)}^m}{s+Q_L+Q_R},$ $\mathrm{Stagel}\left(Q_R\right)=\frac{{\left(Q_R\right)}^m}{s+Q_L+Q_R},$ Wherein, p, q, m, s and z are model coefficient, in the present embodiment, get p=7.99, q=6.59, m=1.28, s=0.985 and z=0.077.

8. the undistorted stereo-picture that n original is adopted, set up its distortion stereo-picture set under the different distortion level of different type of distortion, this distortion stereo-picture set comprises the stereo-picture of several distortions, utilizes subjective quality assessment method to obtain the mean subjective scoring difference of the stereo-picture of every width distortion in this distortion stereo-picture set respectively, is designated as DMOS, DMOS=100-MOS, wherein, MOS represents subjective scoring average, DMOS ∈ [0,100], n>=1; Then 1. 7. S is calculated to step according to step _disthe operation of picture quality objective evaluation predicted value Q, calculate the picture quality objective evaluation predicted value of the stereo-picture of every width distortion in this distortion stereo-picture set in an identical manner respectively.

In the present embodiment, utilize the stereo-picture as Fig. 2 a and Fig. 2 b is formed, the stereo-picture that Fig. 3 a and Fig. 3 b is formed, the stereo-picture that Fig. 4 a and Fig. 4 b is formed, the stereo-picture that Fig. 5 a and Fig. 5 b is formed, the stereo-picture that Fig. 6 a and Fig. 6 b is formed, the stereo-picture that Fig. 7 a and Fig. 7 b is formed, the stereo-picture that Fig. 8 a and Fig. 8 b is formed, the stereo-picture that Fig. 9 a and Fig. 9 b is formed, stereo-picture totally 9 width (n=9) the undistorted stereo-picture that Figure 10 a and Figure 10 b is formed, set up correspondence 5 specified distortion level under Gaussian Blur, lower 5 specified distortion level of white Gaussian noise, JPEG compresses lower 5 specified distortion level, JPEG2000 compresses lower 5 specified distortion level, H.264 the 234 width distortion stereo-pictures altogether of lower 6 specified distortion level are compressed as test stereo-picture.This 234 width distortion stereo-picture forms the set of a distortion stereo-picture, existing subjective quality assessment method is utilized to obtain the mean subjective scoring difference of the stereo-picture of every width distortion in this distortion stereo-picture set respectively, be designated as DMOS, DMOS=100-MOS, wherein, MOS represents subjective scoring average, DMOS ∈ [0,100]; Then 1. 7. S is calculated to step according to step _disthe operation of picture quality objective evaluation predicted value Q, calculate the picture quality objective evaluation predicted value of the stereo-picture of every width distortion in this distortion stereo-picture set in an identical manner respectively.

9 undistorted stereo-pictures shown in Fig. 2 a to Figure 10 b are adopted to analyze at the stereo-picture of JPEG compression in various degree, JPEG2000 compression, Gaussian Blur, white noise and 234 width distortions H.264 in coding distortion situation the correlation that the picture quality objective evaluation predicted value of the stereo-picture of this 234 width distortion and mean subjective mark between difference.In the present embodiment, utilize 4 of evaluate image quality evaluating method conventional objective parameters as evaluation index, namely Pearson correlation coefficient (the Pearson linear correlation coefficient under nonlinear regression condition, PLCC), Spearman coefficient correlation (Spearman rank order correlation coefficient, SROCC), Kendall coefficient correlation (Kendall rank-order correlation coefficient, KROCC), mean square error (root mean squarederror, RMSE), PLCC and RMSE reflects the accuracy of the picture quality objective evaluation predicted value of the stereo-picture of distortion, SROCC and KROCC reflects its monotonicity.The picture quality objective evaluation predicted value of the stereo-picture of the 234 width distortions calculated is done four parameter Logistic function nonlinear fittings, PLCC and SROCC value is higher, the less explanation of OR and RMSE value assessment method for encoding quality of the present invention and mean subjective difference correlation of marking is better.PLCC, SROCC, KROCC and RMSE coefficient of reflection three-dimensional image objective evaluation method performance is as shown in table 1, from the data listed by table 1, final picture quality objective evaluation predicted value and the mean subjective correlation of marking between difference of the stereo-picture of the distortion obtained by the inventive method are very high, the result fully indicating objective evaluation result and human eye subjective perception is more consistent, is enough to the validity that the inventive method is described.

Figure 11 gives the scatter diagram that the picture quality objective evaluation predicted value of the stereo-picture of 234 width distortions and mean subjective mark difference, and loose point is more concentrated, illustrates that the consistency of objective evaluation result and subjective perception is better.As can be seen from Figure 11, adopt the scatter diagram that obtains of the inventive method more concentrated, and the goodness of fit between subjective assessment data is higher.

Correlation between the picture quality objective evaluation predicted value of the stereo-picture of the 234 width distortions that table 1 utilizes the inventive method to obtain and subjective scoring

Claims (7)

1., based on an objective evaluation method for quality of stereo images for picture breakdown, it is characterized in that its processing procedure is:

First, 3 grades of wavelet transformations are implemented to the left visual point image of the left visual point image of original undistorted stereo-picture and the stereo-picture of distortion to be evaluated, again according to the matrix of wavelet coefficients of 3 directional subbands corresponding to every grade of wavelet transformation obtaining, obtain Recovery image and the interfering picture of the left visual point image of the stereo-picture of distortion to be evaluated; Equally, 3 grades of wavelet transformations are implemented to the right visual point image of the right visual point image of original undistorted stereo-picture and the stereo-picture of distortion to be evaluated, again according to the matrix of wavelet coefficients of 3 directional subbands corresponding to every grade of wavelet transformation obtaining, obtain Recovery image and the interfering picture of the right visual point image of the stereo-picture of distortion to be evaluated;

Secondly, by the local phase characteristic sum local amplitude feature of each pixel in the Recovery image of the left visual point image of the stereo-picture of the left visual point image and distortion to be evaluated that calculate original undistorted stereo-picture, obtain the picture quality objective evaluation predicted value of the Recovery image of the left visual point image of the stereo-picture of distortion to be evaluated; Equally, by the local phase characteristic sum local amplitude feature of each pixel in the Recovery image of the right visual point image of the stereo-picture of the right visual point image and distortion to be evaluated that calculate original undistorted stereo-picture, obtain the picture quality objective evaluation predicted value of the Recovery image of the right visual point image of the stereo-picture of distortion to be evaluated;

Then, by the singular value vector that each size in the interfering picture of the left visual point image of the stereo-picture of the left visual point image and distortion to be evaluated that calculate original undistorted stereo-picture is the sub-block of 8 × 8, obtain the picture quality objective evaluation predicted value of the interfering picture of the left visual point image of the stereo-picture of distortion to be evaluated; Equally, by the singular value vector that each size in the interfering picture of the right visual point image of the stereo-picture of the right visual point image and distortion to be evaluated that calculate original undistorted stereo-picture is the sub-block of 8 × 8, obtain the picture quality objective evaluation predicted value of the interfering picture of the right visual point image of the stereo-picture of distortion to be evaluated;

Afterwards, the Recovery image of the left visual point image of the stereo-picture of distortion to be evaluated and the picture quality objective evaluation predicted value of interfering picture are merged, obtains the picture quality objective evaluation predicted value of the left visual point image of the stereo-picture of distortion to be evaluated; Equally, the Recovery image of the right visual point image of the stereo-picture of distortion to be evaluated and the picture quality objective evaluation predicted value of interfering picture are merged, obtains the picture quality objective evaluation predicted value of the right visual point image of the stereo-picture of distortion to be evaluated;

Moreover, the left visual point image of the stereo-picture of distortion to be evaluated and the picture quality objective evaluation predicted value of right visual point image are merged, obtains the picture quality objective evaluation predicted value of the stereo-picture of distortion to be evaluated;

Finally, adopt the undistorted stereo-picture that several are original, set up its distortion stereo-picture set under the different distortion level of different type of distortion, this distortion stereo-picture set comprises the stereo-picture of several distortions, then calculates the picture quality objective evaluation predicted value of the stereo-picture of every width distortion in this distortion stereo-picture set respectively according to the process of the picture quality objective evaluation predicted value of the stereo-picture of above-mentioned acquisition distortion to be evaluated;

This objective evaluation method for quality of stereo images specifically comprises the following steps:

1. S is made _orgfor original undistorted stereo-picture, make S _disfor the stereo-picture of distortion to be evaluated, by S _orgleft visual point image be designated as { L _org(x, y) }, by S _orgright visual point image be designated as { R _org(x, y) }, by S _disleft visual point image be designated as { L _dis(x, y) }, by S _disright visual point image be designated as { R _dis(x, y) }, wherein, (x, y) coordinate position of the pixel in left visual point image and right visual point image is represented, 1≤x≤W, 1≤y≤H, W represents the width of left visual point image and right visual point image, and H represents the height of left visual point image and right visual point image, L _org(x, y) represents { L _org(x, y) } in coordinate position be the pixel value of the pixel of (x, y), R _org(x, y) represents { R _org(x, y) } in coordinate position be the pixel value of the pixel of (x, y), L _dis(x, y) represents { L _dis(x, y) } in coordinate position be the pixel value of the pixel of (x, y), R _dis(x, y) represents { R _dis(x, y) } in coordinate position be the pixel value of the pixel of (x, y);

2. respectively to { L _org(x, y) } and { L _dis(x, y) } implement 3 grades of wavelet transformations, then according to { L _org(x, y) } matrix of wavelet coefficients of 3 directional subbands that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding and { L _dis(x, y) } matrix of wavelet coefficients of 3 directional subbands that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding, obtain { L _dis(x, y) } Recovery image and interfering picture, correspondence is designated as with wherein, represent middle coordinate position is the pixel value of the pixel of (x, y), represent middle coordinate position is the pixel value of the pixel of (x, y);

Respectively to { R _org(x, y) } and { R _dis(x, y) } implement 3 grades of wavelet transformations, then according to { R _org(x, y) } matrix of wavelet coefficients of 3 directional subbands that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding and { R _dis(x, y) } matrix of wavelet coefficients of 3 directional subbands that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding, obtain { R _dis(x, y) } Recovery image and interfering picture, correspondence is designated as with wherein, represent middle coordinate position is the pixel value of the pixel of (x, y), represent middle coordinate position is the pixel value of the pixel of (x, y);

3. { L is calculated respectively _org(x, y) }, { R _org(x, y) }, with in the local phase characteristic sum local amplitude feature of each pixel, by { L _org(x, y) } in coordinate position be that the local phase feature of the pixel of (x, y) is designated as by { L _org(x, y) } in coordinate position be that the local amplitude feature of the pixel of (x, y) is designated as by { R _org(x, y) } in coordinate position be that the local phase feature of the pixel of (x, y) is designated as by { R _org(x, y) } in coordinate position be that the local amplitude feature of the pixel of (x, y) is designated as will middle coordinate position is that the local phase feature of the pixel of (x, y) is designated as will middle coordinate position is that the local amplitude feature of the pixel of (x, y) is designated as will middle coordinate position is that the local phase feature of the pixel of (x, y) is designated as will middle coordinate position is that the local amplitude feature of the pixel of (x, y) is designated as

4. according to { L _org(x, y) } and in the local phase characteristic sum local amplitude feature of each pixel, calculate picture quality objective evaluation predicted value, be designated as

$Q_L^{\mathrm{LPA}}=\left(\frac{1}{H\&times;W}\underset{y=1}{\overset{H}{\mathrm{\&Sigma;}}}\underset{x=1}{\overset{W}{\mathrm{\&Sigma;}}}{S}_L^{\mathrm{LP}}(x,y)\right)\&times;\left(\frac{1}{H\&times;W}\underset{y=1}{\overset{H}{\mathrm{\&Sigma;}}}\underset{x=1}{\overset{W}{\mathrm{\&Sigma;}}}{S}_L^{\mathrm{LA}}(x,y)\right),$

$S_L^{\mathrm{LP}}(x,y)=\frac{2\&times;{\mathrm{LP}}_{L_{\mathrm{org}}}(x,y)\&times;{\mathrm{LP}}_L^{\mathrm{res}}(x,y)+T_1}{{\left({\mathrm{LP}}_{L_{\mathrm{org}}}(x,y)\right)}^2+{\left({\mathrm{LP}}_L^{\mathrm{res}}(x,y)\right)}^2+T_1},$ $S_L^{\mathrm{LA}}(x,y)=\frac{2\&times;{\mathrm{LA}}_{L_{\mathrm{org}}}(x,y)\&times;{\mathrm{LA}}_L^{\mathrm{res}}(x,y)+T_2}{{\left({\mathrm{LA}}_{L_{\mathrm{org}}}(x,y)\right)}^2+{\left({\mathrm{LA}}_L^{\mathrm{res}}(x,y)\right)}^2+T_2},$ Wherein, T ₁and T ₂for controling parameters;

According to { R _org(x, y) } and in the local phase characteristic sum local amplitude feature of each pixel, calculate picture quality objective evaluation predicted value, be designated as

$Q_R^{\mathrm{LPA}}=\left(\frac{1}{H\&times;W}\underset{y=1}{\overset{H}{\mathrm{\&Sigma;}}}\underset{x=1}{\overset{W}{\mathrm{\&Sigma;}}}{S}_R^{\mathrm{LP}}(x,y)\right)\&times;\left(\frac{1}{H\&times;W}\underset{y=1}{\overset{H}{\mathrm{\&Sigma;}}}\underset{x=1}{\overset{W}{\mathrm{\&Sigma;}}}{S}_R^{\mathrm{LA}}(x,y)\right),$

$S_R^{\mathrm{LP}}(x,y)=\frac{2\&times;{\mathrm{LP}}_{R_{\mathrm{org}}}(x,y)\&times;{\mathrm{LP}}_R^{\mathrm{res}}(x,y)+T_1}{{\left({\mathrm{LP}}_{R_{\mathrm{org}}}(x,y)\right)}^2+{\left({\mathrm{LP}}_R^{\mathrm{res}}(x,y)\right)}^2+T_1},$ $S_R^{\mathrm{LA}}(x,y)=\frac{2\&times;{\mathrm{LA}}_{R_{\mathrm{org}}}(x,y)\&times;{\mathrm{LA}}_R^{\mathrm{res}}(x,y)+T_2}{{\left({\mathrm{LA}}_{R_{\mathrm{org}}}(x,y)\right)}^2+{\left({\mathrm{LA}}_R^{\mathrm{res}}(x,y)\right)}^2+T_2},$ Wherein, T ₁and T ₂for controling parameters;

5. respectively by { L _org(x, y) } and be divided into the size of individual non-overlapping copies is the sub-block of 8 × 8, then respectively to { L _org(x, y) } in each sub-block and in each sub-block implement singular value decomposition, obtain { L _org(x, y) } in each sub-block singular value vector and in the singular value vector of each sub-block, then to calculate picture quality objective evaluation predicted value, be designated as wherein, N _blockrepresent { L _org(x, y) } in the number of sub-block that comprises, also represent in the number of sub-block that comprises, symbol " || " is the symbol that takes absolute value, " <> " for getting interior Product function, represent { L _org(x, y) } in the singular value vector of a kth sub-block, represent in the singular value vector of a kth sub-block;

Respectively by { R _org(x, y) } and be divided into the size of individual non-overlapping copies is the sub-block of 8 × 8, then respectively to { R _org(x, y) } in each sub-block and in each sub-block implement singular value decomposition, obtain { R _org(x, y) } in each sub-block singular value vector and in the singular value vector of each sub-block, then to calculate picture quality objective evaluation predicted value, be designated as wherein, N' _blockrepresent { R _org(x, y) } in the number of sub-block that comprises, also represent in the number of sub-block that comprises, symbol " || " is the symbol that takes absolute value, " <> " for getting interior Product function, represent { R _org(x, y) } in the singular value vector of a kth sub-block, represent in the singular value vector of a kth sub-block;

6. right picture quality objective evaluation predicted value with picture quality objective evaluation predicted value merge, obtain { L _dis(x, y) } picture quality objective evaluation predicted value, be designated as Q _l, $Q_L=w_1\&times;Q_L^{\mathrm{LPA}}+w_2\&times;Q_L^{\mathrm{SVD}},$ And it is right picture quality objective evaluation predicted value with picture quality objective evaluation predicted value merge, obtain { R _dis(x, y) } picture quality objective evaluation predicted value, be designated as Q _r, $Q_R=w_1\&times;Q_R^{\mathrm{LPA}}+w_2\&times;Q_R^{\mathrm{SVD}},$ Wherein, w ₁represent with weights proportion, w ₂represent with weights proportion, w ₁+ w ₂=1;

7. to { L _dis(x, y) } picture quality objective evaluation predicted value Q _l{ R _dis(x, y) } picture quality objective evaluation predicted value Q _rmerge, obtain S _dispicture quality objective evaluation predicted value, be designated as Q,

$Q=\frac{{(\mathrm{Stagel}\left(Q_L\right)+\mathrm{Stagel}\left(Q_R\right))}^p}{z+{(\mathrm{Stagel}\left(Q_L\right)+\mathrm{Stagel}\left(Q_R\right))}^q},\mathrm{Stagel}\left(Q_L\right)=\frac{{\left(Q_L\right)}^m}{s+Q_L+Q_R},\mathrm{Stagel}\left(Q_R\right)=\frac{{\left(Q_R\right)}^m}{s+Q_L+Q_R},$ Wherein, p, q, m, s and z are model coefficient.

2. a kind of objective evaluation method for quality of stereo images based on picture breakdown according to claim 1, is characterized in that described step detailed process is 2.:

2.-1, to { L _org(x, y) } implement 3 grades of wavelet transformations, obtain the matrix of wavelet coefficients of 3 directional subbands corresponding to every grade of wavelet transformation, by { L _org(x, y) } implement m level wavelet transformation after the matrix of wavelet coefficients of the n-th directional subband that obtains be designated as wherein, 3 directional subbands are respectively horizontal direction subband, vertical direction subband and diagonal angle directional subband, 1≤m≤3,1≤n≤3, represent middle coordinate position is the wavelet coefficient at (x, y) place;

2.-2, to { L _dis(x, y) } implement 3 grades of wavelet transformations, obtain the matrix of wavelet coefficients of 3 directional subbands corresponding to every grade of wavelet transformation, by { L _dis(x, y) } implement m level wavelet transformation after the matrix of wavelet coefficients of the n-th directional subband that obtains be designated as wherein, 3 directional subbands are respectively horizontal direction subband, vertical direction subband and diagonal angle directional subband, 1≤m≤3,1≤n≤3, represent middle coordinate position is the wavelet coefficient at (x, y) place;

2.-3, according to { L _org(x, y) } matrix of wavelet coefficients of 3 directional subbands that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding and { L _dis(x, y) } matrix of wavelet coefficients of 3 directional subbands that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding, estimate to obtain { L _dis(x, y) } the matrix of wavelet coefficients compensating parameter matrix separately of all directions subband that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding, by { L _dis(x, y) } implement m level wavelet transformation after the matrix of wavelet coefficients of the n-th directional subband that obtains compensating parameter matrix be designated as wherein, represent middle coordinate position is the compensating parameter at (x, y) place, for inputting be truncated to the truncation funcation that [0,1] is interval;

2.-4, according to { L _org(x, y) } the matrix of wavelet coefficients, { L of 3 directional subbands that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding _dis(x, y) } the matrix of wavelet coefficients, { L of 3 directional subbands that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding _dis(x, y) } the matrix of wavelet coefficients compensating parameter matrix separately of all directions subband that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding, calculate { L _dis(x, y) } matrix of wavelet coefficients that obtains after hanging oneself and recovering of the matrix of wavelet coefficients of all directions subband that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding, by { L _dis(x, y) } implement m level wavelet transformation after the matrix of wavelet coefficients of the n-th directional subband that obtains the matrix of wavelet coefficients obtained after recovering is designated as wherein, represent middle coordinate position is the wavelet coefficient at (x, y) place, then to { L _dis(x, y) } matrix of wavelet coefficients that obtains after hanging oneself and recovering of the matrix of wavelet coefficients of all directions subband that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding implements anti-wavelet transformation, obtains { L _dis(x, y) } Recovery image, be designated as wherein, represent middle coordinate position is the pixel value of the pixel of (x, y);

2.-5, according to { L _org(x, y) } the matrix of wavelet coefficients, { L of 3 directional subbands that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding _dis(x, y) } the matrix of wavelet coefficients, { L of 3 directional subbands that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding _dis(x, y) } matrix of wavelet coefficients that the matrix of wavelet coefficients of all directions subband that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding obtains after hanging oneself and recovering, calculate { L _dis(x, y) } matrix of wavelet coefficients of all directions subband that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations is corresponding to hang oneself the matrix of wavelet coefficients obtained after interference, by { L _dis(x, y) } implement m level wavelet transformation after the matrix of wavelet coefficients of the n-th directional subband that obtains the matrix of wavelet coefficients obtained after interference is designated as wherein, represent middle coordinate position is the wavelet coefficient at (x, y) place, $C_{L_{\mathrm{dis}},m,n}^{\mathrm{int}}(x,y)=C_{L_{\mathrm{org}},m,n}(x,y)+C_{L_{\mathrm{dis}},m,n}(x,y)-C_{L_{\mathrm{dis}},m,n}^{\mathrm{res}}(x,y);$ Then to { L _dis(x, y) } matrix of wavelet coefficients of all directions subband that every grade of wavelet transformation implementing to obtain after 3 grades of wavelet transformations the is corresponding matrix of wavelet coefficients obtained after interference of hanging oneself implements anti-wavelet transformation, obtains { L _dis(x, y) } interfering picture, be designated as wherein, represent middle coordinate position is the pixel value of the pixel of (x, y);

2.-6,2.-1 2.-5 { L are obtained to step according to step _dis(x, y) } Recovery image { L _dis(x, y) } interfering picture operation, in an identical manner obtain { R _dis(x, y) } Recovery image { R _dis(x, y) } interfering picture

3. a kind of objective evaluation method for quality of stereo images based on picture breakdown according to claim 1 and 2, is characterized in that described step detailed process is 3.:

3.-1, adopt log-Garbor filter to { L _org(x, y) } in each pixel carry out filtering process, obtain { L _org(x, y) } in each pixel in the even symmetry frequency response of different scale and different directions and odd symmetry frequency response, by { L _org(x, y) } in coordinate position be that the pixel of (x, y) is designated as e in the even symmetry frequency response of different scale and different directions _{α, θ}(x, y), by { L _org(x, y) } in coordinate position be that the pixel of (x, y) is designated as o in the odd symmetry frequency response of different scale and different directions _{α, θ}(x, y), wherein, α represents the scale factor of log-Garbor filter, 1≤α≤4, and θ represents the direction factor of log-Garbor filter, 1≤θ≤4;

3.-2, { L is calculated _org(x, y) } in each pixel in the phase equalization feature of different directions, by { L _org(x, y) } in coordinate position be that the pixel of (x, y) is designated as PC in the phase equalization feature of different directions _θ(x, y), ${\mathrm{PC}}_{\mathrm{\&theta;}}(x,y)=\frac{E_0(x,y)}{\underset{\mathrm{\&alpha;}=1}{\overset{4}{\mathrm{\&Sigma;}}}{A}_{\mathrm{\&alpha;},\mathrm{\&theta;}}(x,y)},$ Wherein, $A_{\mathrm{\&alpha;},\mathrm{\&theta;}}(x,y)=\sqrt{e_{\mathrm{\&alpha;},\mathrm{\&theta;}}{(x,y)}^2+o_{\mathrm{\&alpha;},\mathrm{\&theta;}}{(x,y)}^2},$

$E_{\mathrm{\&theta;}}(x,y)=\sqrt{F_{\mathrm{\&theta;}}{(x,y)}^2+H_{\mathrm{\&theta;}}{(x,y)}^2},F_{\mathrm{\&theta;}}(x,y)=\underset{\mathrm{\&alpha;}=1}{\overset{4}{\mathrm{\&Sigma;}}}{e}_{\mathrm{\&alpha;},\mathrm{\&theta;}}(x,y),H_{\mathrm{\&theta;}}(x,y)=\underset{\mathrm{\&alpha;}=1}{\overset{4}{\mathrm{\&Sigma;}}}{o}_{\mathrm{\&alpha;},\mathrm{\&theta;}}(x,y);$

3.-3, according to { L _org(x, y) } in direction corresponding to the maximum phase consistency feature of each pixel, calculate { L _org(x, y) } in the local phase characteristic sum local amplitude feature of each pixel, for { L _org(x, y) } in coordinate position be the pixel of (x, y), first find out its maximum phase consistency feature in the phase equalization feature of different directions, next finds out direction corresponding to this maximum phase consistency feature, is designated as θ _m, again according to θ _mcalculate { L _org(x, y) } in coordinate position be the local phase characteristic sum local amplitude feature of the pixel of (x, y), correspondence is designated as with ${\mathrm{LA}}_{L_{\mathrm{org}}}(x,y),{\mathrm{LP}}_{L_{\mathrm{org}}}(x,y)=\arctan (H_{{\mathrm{\&theta;}}_m}(x,y),F_{{\mathrm{\&theta;}}_m}(x,y)),{\mathrm{LA}}_{L_{\mathrm{org}}}(x,y)=\underset{\mathrm{\&alpha;}=1}{\overset{4}{\mathrm{\&Sigma;}}}{A}_{\mathrm{\&alpha;},{\mathrm{\&theta;}}_m}(x,y),$ Wherein,

$F_{{\mathrm{\&theta;}}_m}(x,y)=\underset{\mathrm{\&alpha;}=1}{\overset{4}{\mathrm{\&Sigma;}}}{e}_{\mathrm{\&alpha;},{\mathrm{\&theta;}}_m}(x,y),H_{{\mathrm{\&theta;}}_m}(x,y)=\underset{\mathrm{\&alpha;}=1}{\overset{4}{\mathrm{\&Sigma;}}}{o}_{\mathrm{\&alpha;},{\mathrm{\&theta;}}_m}(x,y),A_{\mathrm{\&alpha;},{\mathrm{\&theta;}}_m}(x,y)=\sqrt{e_{\mathrm{\&alpha;},{\mathrm{\&theta;}}_m}{(x,y)}^2+o_{\mathrm{\&alpha;},{\mathrm{\&theta;}}_m}{(x,y)}^2},$ represent { L _org(x, y) } in coordinate position be that the pixel of (x, y) is at different scale and direction θ corresponding to maximum phase consistency feature _meven symmetry frequency response, represent { L _org(x, y) } in coordinate position be that the pixel of (x, y) is at different scale and direction θ corresponding to maximum phase consistency feature _modd symmetry frequency response, arctan () is negate tan;

3.-4,3.-1 3.-3 { L are obtained to step according to step _org(x, y) } in the operation of local phase characteristic sum local amplitude feature of each pixel, obtain { R in an identical manner _org(x, y) }, with in the local phase characteristic sum local amplitude feature of each pixel.

4. a kind of objective evaluation method for quality of stereo images based on picture breakdown according to claim 3, in is characterized in that described step 5. picture quality objective evaluation predicted value acquisition process be:

5.-1a, respectively by { L _org(x, y) } and be divided into the size of individual non-overlapping copies is the sub-block of 8 × 8, by { L _org(x, y) } in a current pending kth sub-block be defined as current first sub-block, will in the sub-block of current pending kth be defined as current second sub-block, wherein,

5.-2a, current first sub-block is designated as current second sub-block is designated as wherein, (x ₂, y ₂) represent with in the coordinate position of pixel, 1≤x ₂≤ 8,1≤y ₂≤ 8, represent middle coordinate position is (x ₂, y ₂) the pixel value of pixel, represent middle coordinate position is (x ₂, y ₂) the pixel value of pixel;

5.-3a, general be expressed as in the form of vectors right implement singular value decomposition, $D_{L_{\mathrm{org}},k}=U_{L_{\mathrm{org}},k}\&times;S_{L_{\mathrm{org}},k}\&times;{\left(V_{L_{\mathrm{org}},k}\right)}^T,$ Wherein, for left singular vector, for right singular vector, for singular value vector, diagonal on element be singular value, and its value arranges according to order from big to small, for transposed vector;

Will be expressed as in the form of vectors right implement singular value decomposition, $D_{L,k}^{\mathrm{int}}=U_{L,k}^{\mathrm{int}}\&times;S_{L,k}^{\mathrm{int}}\&times;{\left(V_{L,k}^{\mathrm{int}}\right)}^T,$ Wherein, for left singular vector, for right singular vector, for singular value vector, diagonal on element be singular value, and its value arranges according to order from big to small, for transposed vector;

5.-4a, make k=k+1, by { L _org(x, y) } in next pending sub-block as current first sub-block, will the pending sub-block of the middle next one as current second sub-block, then return step 5.-2a continue to perform, until { L _org(x, y) } and in all sub-blocks be all disposed, obtain { L _org(x, y) } in each sub-block singular value vector and in the singular value vector of each sub-block, wherein, "=" in k=k+1 is assignment;

5.-5a, basis { L _org(x, y) } in each sub-block singular value vector and in the singular value vector of each sub-block, calculate picture quality objective evaluation predicted value, be designated as wherein, N _blockrepresent { L _org(x, y) } in the number of sub-block that comprises, also represent in the number of sub-block that comprises, symbol " || " is the symbol that takes absolute value, and " <> " is for getting interior Product function;

Described step 5. in picture quality objective evaluation predicted value acquisition process be:

5.-1b, respectively by { R _org(x, y) } and be divided into the size of individual non-overlapping copies is the sub-block of 8 × 8, by { R _org(x, y) } in a current pending kth sub-block be defined as current first sub-block, will in the sub-block of current pending kth be defined as current second sub-block, wherein,

5.-2b, current first sub-block is designated as current second sub-block is designated as wherein, (x ₂, y ₂) represent with in the coordinate position of pixel, 1≤x ₂≤ 8,1≤y ₂≤ 8, represent middle coordinate position is (x ₂, y ₂) the pixel value of pixel, represent middle coordinate position is (x ₂, y ₂) the pixel value of pixel;

5.-3b, general be expressed as in the form of vectors right implement singular value decomposition, $D_{R_{\mathrm{org}},k}=U_{R_{\mathrm{org}},k}\&times;S_{R_{\mathrm{org}},k}\&times;{\left(V_{R_{\mathrm{org}},k}\right)}^T,$ Wherein, for left singular vector, for right singular vector, for singular value vector, diagonal on element be singular value, and its value arranges according to order from big to small, for transposed vector;

Will be expressed as in the form of vectors right implement singular value decomposition, $D_{R,k}^{\mathrm{int}}=U_{R,k}^{\mathrm{int}}\&times;S_{R,k}^{\mathrm{int}}\&times;{\left(V_{R,k}^{\mathrm{int}}\right)}^T,$ Wherein, for left singular vector, for right singular vector, for singular value vector, diagonal on element be singular value, and its value arranges according to order from big to small, for transposed vector;

5.-4b, make k=k+1, by { R _org(x, y) } in next pending sub-block as current first sub-block, will the pending sub-block of the middle next one as current second sub-block, then return step 5.-2b continue to perform, until { R _org(x, y) } and in all sub-blocks be all disposed, obtain { R _org(x, y) } in each sub-block singular value vector and in the singular value vector of each sub-block, wherein, "=" in k=k+1 is assignment;

5.-5b, basis { R _org(x, y) } in each sub-block singular value vector and in the singular value vector of each sub-block, calculate picture quality objective evaluation predicted value, be designated as wherein, N' _blockrepresent { R _org(x, y) } in the number of sub-block that comprises, also represent in the number of sub-block that comprises, symbol " || " is the symbol that takes absolute value, and " <> " is for getting interior Product function.

5. a kind of objective evaluation method for quality of stereo images based on picture breakdown according to claim 4, is characterized in that getting T during described step 4. ₁=0.85, T ₂=160.

6. a kind of objective evaluation method for quality of stereo images based on picture breakdown according to claim 5, is characterized in that getting w during described step 6. ₁=0.9208, w ₂=0.0792.

7. a kind of objective evaluation method for quality of stereo images based on picture breakdown according to claim 6, is characterized in that getting p=7.99, q=6.59, m=1.28, s=0.985 and z=0.077 during described step 7..