CN102708568A - Stereoscopic image objective quality evaluation method on basis of structural distortion - Google Patents

Abstract

The invention discloses a stereoscopic image objective quality evaluation method on the basis of the structural distortion, which comprises the following steps: firstly, respectively carrying out regional division on left and right viewpoint images of an undistorted stereoscopic image and a distorted stereoscopic image to obtain an eye sensitive region and a corresponding nonsensitive region and then respectively obtaining evaluation indexes of the sensitive region and the nonsensitive region from two aspects of the structural amplitude distortion and the structural direction distortion; secondly, acquiring quality evaluation values of the left and right viewpoint images; thirdly, sampling singular value difference and a mean deviation ratio of residual images of which singular values are deprived to evaluate the distortion condition of the depth perception of a stereoscopic image so as to obtain an evaluation value of stereoscopic perceived quality; and finally, combining the quality of the left and right viewpoint images with the stereoscopic perceived quality to obtain a final quality evaluation result of the stereoscopic image. The method disclosed by the invention avoids simulating each composition part of an eye vision system, but sufficiently utilizes structure information of the stereoscopic image, so the consistency of the objective evaluation result and the subjective perception is effectively improved.

Description

Three-dimensional image objective quality evaluation method based on structural distortion

Technical Field

The invention relates to an image quality evaluation technology, in particular to a three-dimensional image objective quality evaluation method based on structural distortion.

Background

The evaluation of the quality of the stereo image occupies a very important position in a stereo image/video system, and not only can judge the quality of a processing algorithm in the stereo image/video system, but also can optimize and design the algorithm so as to improve the efficiency of the stereo image/video processing system. The stereo image quality evaluation methods are mainly classified into two types: subjective quality assessment and objective quality assessment. The subjective quality evaluation method is to perform weighted average comprehensive evaluation on the quality of a stereo image to be evaluated by a plurality of observers, and the result accords with the characteristics of a human visual system, but the subjective quality evaluation method is limited by a plurality of factors such as inconvenient calculation, low speed, high cost and the like, so that the subjective quality evaluation method causes difficulty in embedding the system, and cannot be widely popularized in practical application. The objective quality evaluation method has the characteristics of simple operation, low cost, easy realization, real-time optimization algorithm and the like, and becomes the key point of the quality evaluation research of the three-dimensional image.

Currently, a mainstream stereo image objective quality evaluation model comprises two parts, namely left and right viewpoint image quality evaluation and depth perception quality evaluation. However, since human perception of the visual system of the human eye is limited, it is difficult to accurately simulate the various components of the human eye, and thus the consistency between these models and subjective perception is not good.

Disclosure of Invention

The invention aims to provide a three-dimensional image objective quality evaluation method based on structural distortion, which can effectively improve the consistency between the three-dimensional image objective quality evaluation result and subjective perception.

The technical scheme adopted by the invention for solving the technical problems is as follows: a three-dimensional image objective quality evaluation method based on structural distortion is characterized by comprising the following steps:

① order S_orgFor original undistorted stereo image, let S_disFor the distorted stereo image to be evaluated, the original undistorted stereo image S is taken_orgIs recorded as L_orgThe original undistorted stereo image S is processed_orgIs recorded as R_orgDistorted stereoscopic image S to be evaluated_disIs recorded as L_disDistorted stereoscopic image S to be evaluated_disIs recorded as R_dis；

② pairs L_orgAnd L_dis、R_orgAnd R_disThe 4 images were divided into regions to obtain L_orgAnd L_dis、R_orgAnd R_disThe sensitive area matrix mapping map corresponding to each of the 4 images will L_orgAnd L_disRespectively carrying out region division to obtain coefficient matrixes of respectively corresponding sensitive region matrix mapping graphs, and recording the coefficient matrixes as A_LFor A_LThe value of the coefficient at the middle coordinate position (i, j), which is denoted as A_L(i,j)，

R is to be_orgAnd R_disRespectively carrying out region division to obtain coefficient matrixes of respectively corresponding sensitive region matrix mapping graphs, and recording the coefficient matrixes as A_RFor A_RThe value of the coefficient at the middle coordinate position (i, j), which is denoted as A_R(i,j)，

Wherein, 0 is not less than i not more than (W-8), 0 is not less than j not more than (H-8), W represents L_org、L_dis、R_orgAnd R_disH denotes L_org、L_dis、R_orgAnd R_disHigh of (d);

③ mixing L_orgAnd L_disThe 2 images were divided into (W-7) × (H-7) overlapping blocks of size 8 × 8, respectively, and then L was calculated_orgAnd L_disMapping the structural amplitude distortion of two overlapped blocks with the same coordinate position in 2 images, and mapping the structural amplitudeThe coefficient matrix of the distortion map is denoted as B_LFor B_LThe value of the coefficient at the middle coordinate position (i, j), which is denoted as B_L(i,j)， $B_L(i,j)=\frac{2\&times;{\mathrm{\&sigma;}}_{\mathrm{org},\mathrm{dis},L}(i,j)+C_1}{{\left({\mathrm{\&sigma;}}_{\mathrm{org},L}(i,j)\right)}^2+{\left({\mathrm{\&sigma;}}_{\mathrm{dis},L}(i,j)\right)}^2+{\mathrm{<figure-callout\; id="1"\; label="C"\; filenames="HDA00001627421000031.png,HDA00001627421000032.png"\; state="\{\{state\}\}">C</figure-callout>}}_1},$ Wherein, B_L(i, j) also denotes L_orgOverlapping block L of size 8 × 8 with middle upper left coordinate position (i, j)_disThe upper-left coordinate position is the structural amplitude distortion value of the overlapped block with the size of 8 × 8 (i, j), ${\mathrm{\&sigma;}}_{\mathrm{org},L}(i,j)=\sqrt{\frac{1}{64}\underset{x=0}{\overset{7}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{7}{\mathrm{\&Sigma;}}}{(L_{\mathrm{org}}(i+x,j+y)-U_{\mathrm{org},L}(i,j))}^2},$ $U_{\mathrm{org},L}(i,j)=\frac{1}{64}\underset{x=0}{\overset{7}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{7}{\mathrm{\&Sigma;}}}{L}_{\mathrm{org}}(i+x,j+y),$ ${\mathrm{\&sigma;}}_{\mathrm{dis},L}(i,j)=\sqrt{\frac{1}{64}\underset{x=0}{\overset{7}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{7}{\mathrm{\&Sigma;}}}{(L_{\mathrm{dis}}(i+x,j+y)-U_{\mathrm{dis},L}(i,j))}^2},$ $U_{\mathrm{dis},L}(i,j)=\frac{1}{64}\underset{x=0}{\overset{7}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{7}{\mathrm{\&Sigma;}}}{L}_{\mathrm{dis}}(i+x,j+y),$ ${\mathrm{\&sigma;}}_{\mathrm{org},\mathrm{dis},L}(i,j)=\frac{1}{64}\underset{x=0}{\overset{7}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{7}{\mathrm{\&Sigma;}}}((L_{\mathrm{org}}(i+x,j+y)-U_{\mathrm{org},L}(i,j))\&times;(L_{\mathrm{dis}}(i+x,j+y)-U_{\mathrm{dis},L}(i,j))),$ L_org(i + x, j + y) denotes L_orgThe pixel value of the pixel point with the middle coordinate position of (i + x, j + y), L_dis(i + x, j + y) denotes L_disThe pixel value C of the pixel point with the middle coordinate position of (i + x, j + y)₁Represents a constant, where 0. ltoreq. i.ltoreq.W-8, 0. ltoreq. j.ltoreq.H-8;

r is to be_orgAnd R_disThe 2 images were each divided into (W-7) × (H-7) overlapping blocks of size 8 × 8, and R was calculated_orgAnd R_disThe structural amplitude distortion mapping maps of two overlapped blocks with the same coordinate positions in 2 images are recorded as B in the coefficient matrix of the structural amplitude distortion mapping maps_RFor B_RThe value of the coefficient at the middle coordinate position (i, j), which is denoted as B_R(i,j)， $B_R(i,j)=\frac{2\&times;{\mathrm{\&sigma;}}_{\mathrm{org},\mathrm{dis},R}(i,j)+C_1}{{\left({\mathrm{\&sigma;}}_{\mathrm{org},R}(i,j)\right)}^2+{\left({\mathrm{\&sigma;}}_{\mathrm{dis},R}(i,j)\right)}^2+{\mathrm{<figure-callout\; id="1"\; label="C"\; filenames="HDA00001627421000031.png,HDA00001627421000032.png"\; state="\{\{state\}\}">C</figure-callout>}}_1},$ Wherein, B_R(i, j) also represents R_orgThe size of the overlapped block with the upper left corner coordinate position of (i, j) being 8 × 8 and R_disThe coordinate position of the middle upper left corner is (i, j)The structural amplitude distortion values of the overlapping blocks of 8 × 8, ${\mathrm{\&sigma;}}_{\mathrm{org},R}(i,j)=\sqrt{\frac{1}{64}\underset{x=0}{\overset{7}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{7}{\mathrm{\&Sigma;}}}{(R_{\mathrm{org}}(i+x,j+y)-U_{\mathrm{org},R}(i,j))}^2},$ $U_{\mathrm{org},R}(i,j)=\frac{1}{64}\underset{x=0}{\overset{7}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{7}{\mathrm{\&Sigma;}}}{R}_{\mathrm{org}}(i+x,j+y),$ ${\mathrm{\&sigma;}}_{\mathrm{dis},R}(i,j)=\sqrt{\frac{1}{64}\underset{x=0}{\overset{7}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{7}{\mathrm{\&Sigma;}}}{(R_{\mathrm{dis}}(i+x,j+y)-U_{\mathrm{dis},R}(i,j))}^2},$ $U_{\mathrm{dis},R}(i,j)=\frac{1}{64}\underset{x=0}{\overset{7}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{7}{\mathrm{\&Sigma;}}}{R}_{\mathrm{dis}}(i+x,j+y),$ ${\mathrm{\&sigma;}}_{\mathrm{org},\mathrm{dis},R}(i,j)=\frac{1}{64}\underset{x=0}{\overset{7}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{7}{\mathrm{\&Sigma;}}}((R_{\mathrm{org}}(i+x,j+y)-U_{\mathrm{org},R}(i,j))\&times;(R_{\mathrm{dis}}(i+x,j+y)-U_{\mathrm{dis},R}(i,j))),$ R_org(i + x, j + y) represents R_orgThe pixel value R of the pixel point with the middle coordinate position of (i + x, j + y)_dis(i + x, j + y) represents R_disThe pixel value C of the pixel point with the middle coordinate position of (i + x, j + y)₁Represents a constant, where 0. ltoreq. i.ltoreq.W-8, 0. ltoreq. j.ltoreq.H-8;

④ pairs L_orgAnd L_disRespectively carrying out Sobel operator processing in the horizontal direction and Sobel operator processing in the vertical direction on the 2 images to respectively obtain L_orgAnd L_disL corresponding to the horizontal direction gradient matrix mapping map and the vertical direction gradient matrix mapping map of each of the 2 images_orgThe coefficient matrix of the corresponding horizontal gradient matrix mapping graph obtained after horizontal Sobel operator processing is carried out is marked as I_h，org,LFor I_h，org,LThe value of the coefficient at the middle coordinate position (I, j), which is denoted as I_h，org，L(i,j)， $\begin{array}{c}{I}_{h,\mathrm{org},L}(i,j)=L_{\mathrm{org}}(i+2,j)+2L_{\mathrm{org}}(i+2,j+1)+L_{\mathrm{org}}(i+2,j+2)\\ -L_{\mathrm{org}}(i,j)-2L_{\mathrm{org}}(i,j+1)-L_{\mathrm{org}}(i,j+2)\end{array},$ L will be mixed_orgThe coefficient matrix of the corresponding vertical gradient matrix mapping graph obtained after the vertical Sobel operator is implemented is marked as I_v，org，LFor I_v，org,LThe value of the coefficient at the middle coordinate position (I, j), which is denoted as I_v，org,L(i,j)， $\begin{array}{c}{I}_{v,\mathrm{org},L}(i,j)=L_{\mathrm{org}}(i,j+2)+2L_{\mathrm{org}}(i+1,j+2)+L_{\mathrm{org}}(i+2,j+2)\\ -L_{\mathrm{org}}(i,j)-2L_{\mathrm{org}}(i+1,j)-L_{\mathrm{org}}(i+2,j)\end{array},$ L will be mixed_disThe coefficient matrix of the corresponding horizontal gradient matrix mapping graph obtained after horizontal Sobel operator processing is carried out is marked as I_h，dis，LFor I_h，dis，LThe value of the coefficient at the middle coordinate position (I, j), which is denoted as I_h，dis,L(i,j)， $\begin{array}{c}{I}_{h,\mathrm{dis},L}(i,j)=L_{\mathrm{dis}}(i+2,j)+2L_{\mathrm{dis}}(i+2,j+1)+L_{\mathrm{dis}}(i+2,j+2)\\ -L_{\mathrm{dis}}(i,j)-2L_{\mathrm{dis}}(i,j+1)-L_{\mathrm{dis}}(i,j+2)\end{array},$ L will be mixed_disThe coefficient matrix of the corresponding vertical gradient matrix mapping graph obtained after the vertical Sobel operator is implemented is marked as I_v，dis，LFor I_v，dis，LThe value of the coefficient at the middle coordinate position (I, j), which is denoted as I_v，dis，L(i,j)， $\begin{array}{c}{I}_{v,\mathrm{dis},L}(i,j)=L_{\mathrm{dis}}(i,j+2)+2L_{\mathrm{dis}}(i+1,j+2)+L_{\mathrm{dis}}(i+2,j+2)\\ -L_{\mathrm{dis}}(i,j)-2L_{\mathrm{dis}}(i+1,j)-L_{\mathrm{dis}}(i+2,j)\end{array},$ Wherein, L_org(i+2,j)、L_org(i+2,j+1)、L_org(i+2,j+2)、L_org(i,j)、L_org(i,j+1)、L_org(i,j+2)、L_org(i+1,j+2)、L_org(i +1, j) are respectively corresponded to L_orgThe pixel values of the pixel points with the middle coordinate positions of (i +2, j), (i +2, j +1), (i +2, j +2), (i, j +1), (i, j +2), (i +1, j +2), and (i +1, j), L_dis(i+2,j)、L_dis(i+2,j+1)、L_dis(i+2,j+2)、L_dis(i,j)、L_dis(i,j+1)、L_dis(i,j+2)、L_dis(i+1,j+2)、L_dis(i +1, j) are respectively corresponded to L_disThe middle coordinate position is the pixel value of the pixel point of (i +2, j), (i +2, j +1), (i +2, j +2), (i, j +1), (i, j +2), (i +1, j +2), or (i +1, j);

to R_orgAnd R_disRespectively carrying out Sobel operator processing in the horizontal direction and the vertical direction on the 2 images to respectively obtain R_orgAnd R_disThe gradient matrix mapping map in the horizontal direction and the gradient matrix mapping map in the vertical direction corresponding to the 2 images are respectively obtained by mapping R_orgThe coefficient matrix of the corresponding horizontal gradient matrix mapping graph obtained after horizontal Sobel operator processing is carried out is marked as I_h，org,RFor I_h，org,RThe value of the coefficient at the middle coordinate position (I, j), which is denoted as I_h，org，R(i,j)， $\begin{array}{c}{I}_{h,\mathrm{org},R}(i,j)=R_{\mathrm{org}}(i+2,j)+2R_{\mathrm{org}}(i+2,j+1)+R_{\mathrm{org}}(i+2,j+2)\\ -R_{\mathrm{org}}(i,j)-2R_{\mathrm{org}}(i,j+1)-R_{\mathrm{org}}(i,j+2)\end{array},$ R is to be_orgThe coefficient matrix of the corresponding vertical gradient matrix mapping graph obtained after the vertical Sobel operator is implemented is marked as I_v，org，RFor I_v，org，RThe value of the coefficient at the middle coordinate position (I, j), which is denoted as I_v，org,R(i,j)， $\begin{array}{c}{I}_{v,\mathrm{org},R}(i,j)=R_{\mathrm{org}}(i,j+2)+2R_{\mathrm{org}}(i+1,j+2)+R_{\mathrm{org}}(i+2,j+2)\\ -R_{\mathrm{org}}(i,j)-2R_{\mathrm{org}}(i+1,j)-R_{\mathrm{org}}(i+2,j)\end{array},$ R is to be_disThe coefficient matrix of the corresponding horizontal gradient matrix mapping graph obtained after horizontal Sobel operator processing is carried out is marked as I_h，dis，RFor I_h，dis，RThe value of the coefficient at the middle coordinate position (I, j), which is denoted as I_h，dis，R(i,j)， $\begin{array}{c}{I}_{h,\mathrm{dis},R}(i,j)=R_{\mathrm{dis}}(i+2,j)+2R_{\mathrm{dis}}(i+2,j+1)+R_{\mathrm{dis}}(i+2,j+2)\\ -R_{\mathrm{dis}}(i,j)-2R_{\mathrm{dis}}(i,j+1)-R_{\mathrm{dis}}(i,j+2)\end{array},$ R is to be_disThe coefficient matrix of the corresponding vertical gradient matrix mapping graph obtained after the vertical Sobel operator is implemented is marked as I_v，dis，RFor I_v，dis，RThe value of the coefficient at the middle coordinate position (I, j), which is denoted as I_v，dis,R(i,j)， $\begin{array}{c}{I}_{v,\mathrm{dis},R}(i,j)=R_{\mathrm{dis}}(i,j+2)+2R_{\mathrm{dis}}(i+1,j+2)+R_{\mathrm{dis}}(i+2,j+2)\\ -R_{\mathrm{dis}}(i,j)-2R_{\mathrm{dis}}(i+1,j)-R_{\mathrm{dis}}(i+2,j)\end{array},$ Wherein R is_org(i+2,j)、R_org(i+2,j+1)、R_org(i+2,j+2)、R_org(i,j)、R_org(i,j+1)、R_org(i,j+2)、R_org(i+1,j+2)、R_org(i +1, j) each represents R_orgThe middle coordinate position is the pixel value, R, of the pixel points of (i +2, j), (i +2, j +1), (i +2, j +2), (i, j +1), (i, j +2), (i +1, j +2), and (i +1, j)_dis(i+2,j)、R_dis(i+2,j+1)、R_dis(i+2,j+2)、R_dis(i,j)、R_dis(i,j+1)、R_dis(i,j+2)、R_dis(i+1,j+2)、R_dis(i +1, j) each represents R_disThe middle coordinate position is the pixel value of the pixel point of (i +2, j), (i +2, j +1), (i +2, j +2), (i, j +1), (i, j +2), (i +1, j +2), or (i +1, j);

⑤ calculation L_orgAnd L_disThe structural direction distortion mapping maps of two overlapped blocks with the same coordinate positions in 2 images are recorded as E in the coefficient matrix of the structural direction distortion mapping maps_LFor E_LThe value of the coefficient at the middle coordinate position (i, j), denoted as E_L(i,j)， $E_L(i,j)=\frac{I_{h,\mathrm{org},L}(i,j)\&times;I_{h,\mathrm{dis},L}(i,j)+I_{v,\mathrm{org},L}(i,j)\&times;I_{v,\mathrm{dis},L}(i,j)+C_2}{\sqrt{{\left(I_{h,\mathrm{org},L}(i,j)\right)}^2+{\left(I_{v,\mathrm{org},L}(i,j)\right)}^2}\&times;\sqrt{{\left(I_{h,\mathrm{dis},L}(i,j)\right)}^2+{\left(I_{v,\mathrm{dis},L}(i,j)\right)}^2}+{\mathrm{<figure-callout\; id="2"\; label="C"\; filenames="HDA00001627421000031.png,HDA00001627421000032.png"\; state="\{\{state\}\}">C</figure-callout>}}_2},$ Wherein, C₂Represents a constant;

calculation of R_orgAnd R_disThe structural direction distortion mapping maps of two overlapped blocks with the same coordinate positions in 2 images are recorded as E in the coefficient matrix of the structural direction distortion mapping maps_RFor E_RThe value of the coefficient at the middle coordinate position (i, j), denoted as E_R(i,j)， $E_R(i,j)=\frac{I_{h,\mathrm{org},R}(i,j)\&times;I_{h,\mathrm{dis},R}(i,j)+I_{v,\mathrm{org},R}(i,j)\&times;I_{v,\mathrm{dis},R}(i,j)+C_2}{\sqrt{{\left(I_{h,\mathrm{org},R}(i,j)\right)}^2+{\left(I_{v,\mathrm{org},R}(i,j)\right)}^2}\&times;\sqrt{{\left(I_{h,\mathrm{dis},R}(i,j)\right)}^2+{\left(I_{v,\mathrm{dis},R}(i,j)\right)}^2}+{\mathrm{<figure-callout\; id="2"\; label="C"\; filenames="HDA00001627421000031.png,HDA00001627421000032.png"\; state="\{\{state\}\}">C</figure-callout>}}_2};$

⑥ calculation L_orgAnd L_disStructural distortion evaluation value of (1), denoted as Q^L，Q^L=ω₁×Q_m，L+ω₂×Q_nm，L， $Q_{m,L}=\frac{1}{N_{L,m}}\underset{i=0}{\overset{W-8}{\mathrm{\&Sigma;}}}\underset{j=0}{\overset{H-8}{\mathrm{\&Sigma;}}}(0.5\&times;(B_L(i,j)+E_L(i,j))\&times;A_L(i,j)),$ $N_{L,m}=\underset{i=0}{\overset{W-8}{\mathrm{\&Sigma;}}}\underset{j=0}{\overset{H-8}{\mathrm{\&Sigma;}}}{A}_L(i,j),$ $Q_{\mathrm{nm},L}=\frac{1}{N_{L,\mathrm{nm}}}\underset{i=0}{\overset{W-8}{\mathrm{\&Sigma;}}}\underset{j=0}{\overset{H-8}{\mathrm{\&Sigma;}}}(0.5\&times;(B_L(i,j)+E_L(i,j))\&times;(1-A_L(i,j))),$ $N_{L,\mathrm{nm}}=\underset{i=0}{\overset{W-8}{\mathrm{\&Sigma;}}}\underset{j=0}{\overset{H-8}{\mathrm{\&Sigma;}}}(1-A_L(i,j)),$ Wherein, ω is₁Representation L_orgAnd L_disWeight value of middle sensitive area, omega₂Representation L_orgAnd L_disThe weight value of the middle non-sensitive area;

calculation of R_orgAnd R_disStructural distortion evaluation value of (1), denoted as Q^R，Q^R=ω′₁×Q_m，R+ω′₂×Q_nm，R， $Q_{m,R}=\frac{1}{N_{R,m}}\underset{i=0}{\overset{W-8}{\mathrm{\&Sigma;}}}\underset{j=0}{\overset{H-8}{\mathrm{\&Sigma;}}}(0.5\&times;(B_R(i,j)+E_R(i,j))\&times;A_R(i,j)),$ $N_{R,m}=\underset{i=0}{\overset{W-8}{\mathrm{\&Sigma;}}}\underset{j=0}{\overset{H-8}{\mathrm{\&Sigma;}}}{A}_R(i,j),$ $Q_{\mathrm{nm},R}=\frac{1}{N_{R,\mathrm{nm}}}\underset{i=0}{\overset{W-8}{\mathrm{\&Sigma;}}}\underset{j=0}{\overset{H-8}{\mathrm{\&Sigma;}}}(0.5\&times;(B_R(i,j)+E_R(i,j))\&times;(1-A_R(i,j))),$ $N_{R,\mathrm{nm}}=\underset{i=0}{\overset{W-8}{\mathrm{\&Sigma;}}}\underset{j=0}{\overset{H-8}{\mathrm{\&Sigma;}}}(1-A_R(i,j)),$ Wherein, omega'₁Represents R_orgAnd R_disWeight value of Medium sensitive region, ω'₂Represents R_orgAnd R_disThe weight value of the middle non-sensitive area;

⑦ according to Q^LAnd Q^RCalculating a distorted stereo image S to be evaluated_disRelative to the original undistorted stereo image S_orgThe spatial frequency similarity measure of (2), denoted as Q_F，Q_F=β₁×Q^L+(1-β₁)×Q^RWherein, β₁Represents Q^LThe weight of (2);

⑧ calculation L_orgAnd R_orgIs expressed in matrix form as

Calculation L_disAnd R_disIs expressed in matrix form as

Wherein, "|" is an absolute value symbol;

⑨ will be

Andthe 2 images are respectively divided into

A non-overlapping block of size 8 × 8, and thenAnd

all the blocks in the 2 images are respectively subjected to singular value decomposition to obtain

Corresponding singular value map composed of singular value matrix of each block thereof and

a corresponding singular value mapping map composed of the singular value matrix of each block thereof is to

The coefficient matrix of the singular value mapping graph obtained after singular value decomposition is recorded as G_orgFor G_orgThe coordinate position in the singular value matrix of the nth block is the singular value at (p, q), which is recorded as

Will be provided withObtained by performing singular value decompositionThe coefficient matrix of the singular value mapping graph is marked as G_disFor G_disThe coordinate position in the singular value matrix of the nth block is the singular value at (p, q), which is recorded asWherein, W_LRTo represent

And

width of (H)_LRTo represent

And

is high in the direction of the horizontal axis, $0\&le;n\&le;\frac{W_{\mathrm{LR}}\&times;{\mathrm{<figure-callout\; id="8"\; label="H\; LR"\; filenames="HDA00001627421000072.png,HDA00001627421000082.png"\; state="\{\{state\}\}">H</figure-callout>}}_{\mathrm{LR}}}{8\&times;8}-1,$ 0≤p≤7,0≤q≤7；

⑩ calculationCorresponding singular value map and

the singular value deviation evaluation value of the corresponding singular value map, denoted as K, $K=\frac{64}{W_{\mathrm{LR}}\&times;H_{\mathrm{LR}}}\&times;\underset{n=0}{\overset{W_{\mathrm{LR}}\&times;H_{\mathrm{LR}}/64-1}{\mathrm{\&Sigma;}}}\frac{\underset{p=0}{\overset{7}{\mathrm{\&Sigma;}}}(G_{\mathrm{org}}^n(p,p)\&times;|G_{\mathrm{org}}^n(p,p)-G_{\mathrm{dis}}^n(p,p)\left|\right)}{\underset{p=0}{\overset{7}{\mathrm{\&Sigma;}}}{G}_{\mathrm{org}}^n(p,p)},$ wherein the content of the first and second substances,

represents G_orgThe coordinate position in the singular value matrix of the nth block is the singular value at (p, p),

represents G_disThe coordinate position in the singular value matrix of the nth block is a singular value at (p, p);

to pairAnd

respectively carrying out singular value decomposition to respectively obtain

And2 orthogonal matrixes and 1 singular value matrix corresponding to each other are used for generating a plurality of singular value matrixes

2 orthogonal matrixes obtained after singular value decomposition are respectively recorded as chi_orgAnd V_orgWill be

The singular value matrix obtained after singular value decomposition is recorded as O_org，

Will be provided with

2 orthogonal matrixes obtained after singular value decomposition are respectively recorded as chi_disAnd V_disWill beThe singular value matrix obtained after singular value decomposition is recorded as O_dis，

Respectively calculate

And

2 residual matrix images of the images after the singular value deprivation

The residual matrix image after the singular value deprivation is marked as X_org，X_org=χ_org×Λ×V_orgWill beThe residual matrix image after the singular value deprivation is marked as X_dis，X_dis=χ_dis×Λ×V_disWhere Λ denotes the identity matrix, Λ size and O_orgAnd O_disAre consistent in size;

calculating X_orgAnd X_disThe mean deviation ratio of (D) is recorded as

Wherein X represents X_orgAnd X_disThe abscissa of the pixel point in (1) and y represents X_orgAnd X_disThe ordinate of the pixel point in (1);

calculating a distorted stereo image S to be evaluated_disRelative to the original undistorted stereo image S_orgThe stereo perception evaluation metric of (1), denoted as Q_S，

Wherein τ represents a constant for adjusting K and

at Q_SThe importance of (1);

according to Q_FAnd Q_SCalculating a distorted stereoscopic image S to be evaluated_disThe image quality evaluation score of (1), denoted as Q, Q = Q_F×（Q_S）^ρWhere ρ represents a weight coefficient value.

L of the step ②_orgAnd L_disCoefficient matrix A of the respective corresponding sensitive area matrix map_LThe acquisition process comprises the following steps:

② -a1, pair L_orgProcessing with Sobel operator in horizontal and vertical directions to obtain L_orgRespectively, as Z, in the horizontal direction and in the vertical direction_h，l1And Z_v，l1Then calculate L_orgIs marked as Z_l1， $Z_{l1}(x,y)=\sqrt{{\left(Z_{h,l1}(x,y)\right)}^2+{\left(Z_{v,l1}(x,y)\right)}^2},$ Wherein Z is_l1(x, y) represents Z_l1Gradient amplitude, Z, of pixel point with (x, y) as middle coordinate position_h，l1(x, y) represents Z_h，l1The horizontal gradient value Z of the pixel point with the (x, y) middle coordinate position_v，l1(x, y) represents Z_v，l1The gradient value in the vertical direction of the pixel point with the middle coordinate position (x, y) is that x is more than or equal to 1 and less than or equal to W ', y is more than or equal to 1 and less than or equal to H ', wherein W ' represents Z_l1Width of (A), H' represents Z_l1High of (d);

② -a2, pair L_disProcessing with Sobel operator in horizontal and vertical directions to obtain L_disRespectively, as Z, in the horizontal direction and in the vertical direction_h，l2And Z_v，l2Then calculate L_disIs marked as Z_l2， $Z_{l2}(x,y)=\sqrt{{\left(Z_{h,l2}(x,y)\right)}^2+{\left(Z_{v,l2}(x,y)\right)}^2},$ Wherein Z is_l2(x, y) represents Z_l2Gradient amplitude, Z, of pixel point with (x, y) as middle coordinate position_h，l2(x, y) represents Z_h，l2The horizontal gradient value Z of the pixel point with the (x, y) middle coordinate position_v，l2(x, y) represents Z_v，l2The gradient value in the vertical direction of the pixel point with the middle coordinate position (x, y) is that x is more than or equal to 1 and less than or equal to W ', y is more than or equal to 1 and less than or equal to H ', wherein W ' represents Z_l2Width of (A), H' represents Z_l2High of (d);

② -a3, calculating the threshold value T required when dividing the region, $T=\mathrm{\&alpha;}\&times;\frac{1}{W^{\&prime;}\&times;H^{\&prime;}}\&times;(\underset{x=0}{\overset{W^{\&prime;}}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{H^{\&prime;}}{\mathrm{\&Sigma;}}}{Z}_{l1}(x,y)+\underset{x=0}{\overset{W^{\&prime;}}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{H^{\&prime;}}{\mathrm{\&Sigma;}}}{Z}_{l2}(x,y)),$ wherein α is a constant, Z_l1(x, y) represents Z_l1Gradient amplitude, Z, of pixel point with (x, y) as middle coordinate position_l2(x, y) represents Z_l2The gradient amplitude of the pixel point with the middle coordinate position (x, y);

② -a4, mixing Z with_l1The gradient amplitude of the pixel point with the middle coordinate position (i, j) is recorded as Z_l1(i, j) reacting Z_l2The gradient amplitude of the pixel point with the middle coordinate position (i, j) is recorded as Z_l2(i, j), judgment of Z_l1(i,j)>T or Z_l2(i,j)>If T is true, then a determination L is made_orgAnd L_disThe pixel point with the middle coordinate position (i, j) belongs to the sensitive area, and order A_L(i, j) =1, otherwise, determination L_orgAnd L_disMiddle seatThe pixel point with the mark position of (i, j) belongs to the non-sensitive area, and order A_L(i, j) =0, wherein i is more than or equal to 0 and is less than or equal to (W-8), and j is more than or equal to 0 and is less than or equal to (H-8);

in the step ②, R_orgAnd R_disCoefficient matrix A of the respective corresponding sensitive area matrix map_RThe acquisition process comprises the following steps:

② -b1, p.R_orgPerforming Sobel operator processing in horizontal and vertical directions to obtain R_orgRespectively, as Z, in the horizontal direction and in the vertical direction_h，r1And Z_v，r1Then calculating R_orgIs marked as Z_r1， $Z_{r1}(x,y)=\sqrt{{\left(Z_{h,r1}(x,y)\right)}^2+{\left(Z_{v,r1}(x,y)\right)}^2},$ Wherein Z is_r1(x, y) represents Z_r1Gradient amplitude, Z, of pixel point with (x, y) as middle coordinate position_h，r1(x, y) represents Z_h，r1The horizontal gradient value Z of the pixel point with the (x, y) middle coordinate position_v，r1(x, y) represents Z_v，r1The gradient value in the vertical direction of the pixel point with the middle coordinate position (x, y) is that x is more than or equal to 1 and less than or equal to W ', y is more than or equal to 1 and less than or equal to H ', wherein W ' represents Z_r1Width of (A), H' represents Z_r1High of (d);

② -b2, p.R_disPerforming Sobel operator processing in horizontal and vertical directions to obtain R_disRespectively, as Z, in the horizontal direction and in the vertical direction_h，r2And Z_v,r2Then calculating R_disIs marked as Z_r2， $Z_{r2}(x,y)=\sqrt{{\left(Z_{h,r2}(x,y)\right)}^2+{\left(Z_{v,r2}(x,y)\right)}^2},$ Wherein Z is_r2(x, y) represents Z_r2Gradient amplitude, Z, of pixel point with (x, y) as middle coordinate position_h，r2(x, y) represents Z_h，r2With (x, y) as the middle coordinate positionHorizontal gradient value, Z, of pixel points_v，r2(x, y) represents Z_v，r2The gradient value in the vertical direction of the pixel point with the middle coordinate position (x, y) is that x is more than or equal to 1 and less than or equal to W ', y is more than or equal to 1 and less than or equal to H ', wherein W ' represents Z_r2Width of (A), H' represents Z_r2High of (d);

② -b3, calculating the threshold value T' required when dividing the region, $T^{\&prime;}=\mathrm{\&alpha;}\&times;\frac{1}{W^{\&prime;}\&times;H^{\&prime;}}\&times;(\underset{x=0}{\overset{W^{\&prime;}}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{H^{\&prime;}}{\mathrm{\&Sigma;}}}{Z}_{r1}(x,y)+\underset{x=0}{\overset{W^{\&prime;}}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{H^{\&prime;}}{\mathrm{\&Sigma;}}}{Z}_{r2}(x,y)),$ wherein α is a constant, Z_r1(x, y) represents Z_r1Gradient amplitude, Z, of pixel point with (x, y) as middle coordinate position_r2(x, y) represents Z_r2The gradient amplitude of the pixel point with the middle coordinate position (x, y);

② -b4, mixing Z with_r1The gradient amplitude of the pixel point with the middle coordinate position (i, j) is recorded as Z_r1(i, j) reacting Z_r2The gradient amplitude of the pixel point with the middle coordinate position (i, j) is recorded as Z_r2(i, j), judgment of Z_r1(i,j)>T or Z_r2(i,j)>Whether T is true, and if so, determining R_orgAnd R_disThe pixel point with the middle coordinate position (i, j) belongs to the sensitive area, and order A_R(i, j) =1, otherwise, determine R_orgAnd R_disThe pixel point with the middle coordinate position (i, j) belongs to the non-sensitive area, and order A_R(i, j) =0, wherein i is more than or equal to 0 and is less than or equal to (W-8), and j is more than or equal to 0 and is less than or equal to (H-8).

β of the step ⑦₁The acquisition process comprises the following steps:

⑦ -1, adopting n undistorted stereo images to establish a distorted stereo image set under different distortion types and different distortion degrees, wherein the distorted stereo image set comprises a plurality of distorted stereo images, and n is more than or equal to 1;

⑦ -2, obtaining an average subjective score difference of each distorted stereo image in the distorted stereo image set by using a subjective quality evaluation method, and recording the average subjective score difference as DMOS, wherein DMOS =100-MOS, wherein MOS represents the subjective score mean, DMOS ∈ [0,100 ];

⑦ -3, according to the operation procedures of step ① to step ⑥, an evaluation value Q of a sensitive area of a left viewpoint image of each distorted stereoscopic image in the distorted stereoscopic image set with respect to a left viewpoint image of a corresponding undistorted stereoscopic image is calculated_m,LAnd evaluation value Q of non-sensitive region_nm，L；

⑦ -4, use numberMean subjective score difference DMOS and corresponding Q of distorted stereo image in fitting distorted stereo image set by mathematical fitting method_m，LAnd Q_nm，LThereby obtaining β₁The value is obtained.

Compared with the prior art, the method has the advantages that firstly, the left viewpoint image and the right viewpoint image of the undistorted stereo image and the distorted stereo image are respectively subjected to region division to obtain the human eye sensitive region and the corresponding non-sensitive region, and then the evaluation indexes of the sensitive region and the non-sensitive region are respectively obtained from the two aspects of structural amplitude distortion and structural direction distortion; secondly, respectively obtaining a left viewpoint image quality evaluation value and a right viewpoint image quality evaluation value by adopting linear weighting, and further obtaining left and right viewpoint image quality evaluation values; according to the singular value, the structural information characteristics of the stereo image can be well represented, the distortion condition of the depth perception of the stereo image is measured by sampling the singular value difference and the mean deviation ratio of the residual image after the singular value is deprived, and therefore the evaluation value of the stereo perception quality is obtained; and finally, combining the image quality of the left viewpoint and the right viewpoint and the stereoscopic perception quality in a nonlinear mode to obtain a final quality evaluation result of the stereoscopic image.

Drawings

FIG. 1 is a block diagram of an overall implementation of the method of the present invention;

FIG. 2a is an Akko & Kayo (640 × 480) stereo image;

FIG. 2b is an Alt Moabit (1024 × 768) stereoscopic image;

fig. 2c is a balloon (1024 × 768) stereoscopic image;

FIG. 2d is a stereo image of Door Flowers (1024 × 768);

fig. 2e is a Kendo (1024 × 768) stereoscopic image;

FIG. 2f is a L viewing L apt (1024 × 768) stereo image;

FIG. 2g is a L ovespird 1(1024 × 768) stereo image;

fig. 2h is a newsapper (1024 × 768) stereoscopic image;

FIG. 2i is an Xmas (640 × 480) stereo image;

FIG. 2j is a Puppy (720 × 480) stereo image;

fig. 2k is a stereo image of Soccer2(720 × 480);

FIG. 2l is a Horse (480 × 270) stereo image;

FIG. 3 is a block diagram of left viewpoint image quality evaluation according to the method of the present invention;

FIG. 4a shows the difference α and ω₁A CC performance change graph between the image quality of the lower left viewpoint and the subjective perception quality;

FIG. 4b shows the difference α and ω₁The SROCC performance change graph between the lower left viewpoint image quality and the subjective perception quality;

FIG. 4c shows the difference α and ω₁A graph of RMSE performance variation between the lower left viewpoint image quality and the subjective perceptual quality;

FIG. 5a is at ω₁In the case of =1, CC performance variation graph between left view image quality and subjective perceptual quality at different α;

FIG. 5b is at ω₁In the case of =1, the SROCC performance variation graph between left view image quality and subjective perceptual quality at different α;

FIG. 5c is a graph at ω₁In the case of =1, the RMSE performance variation plot between left view image quality and subjective perceptual quality at different α;

FIG. 6a shows a variant β₁A CC performance change graph between the image quality of the lower left viewpoint and the image quality of the lower right viewpoint and the subjective perception quality;

FIG. 6b shows a difference β₁The SROCC performance change graph between the left and right viewpoint image quality and the subjective perception quality;

FIG. 6c shows a difference β₁The RMSE performance change graph between the left and right viewpoint image quality and the subjective perception quality;

fig. 7a is a graph of CC performance variation between stereo depth perception quality and subjective perception quality at different τ;

FIG. 7b is a graph of the SROCC performance variation between the stereo depth perception quality and the subjective perception quality at different τ;

fig. 7c is a graph of the RMSE performance variation between stereo depth perception quality and subjective perception quality at different τ;

fig. 8a is a graph of CC performance variation between stereo image quality and subjective perceptual quality at different ρ;

FIG. 8b is a graph of the SROCC performance variation between stereo image quality and subjective perceptual quality at different ρ;

fig. 8c is a graph of the RMSE performance variation between stereo image quality and subjective perceptual quality at different p.

Detailed Description

The invention is described in further detail below with reference to the accompanying examples.

According to the method for evaluating the objective quality of the stereo image based on the structural distortion, the image quality of the left and right viewpoints and the stereo perception quality of the stereo image are evaluated from the angle of the structural distortion, and the final quality evaluation value of the stereo image is obtained in a nonlinear weighting mode. Fig. 1 shows a block diagram of a general implementation of the method of the invention, comprising the following steps:

① order S_orgFor original undistorted stereo image, let S_disFor the distorted stereo image to be evaluated, the original undistorted stereo image S is taken_orgIs recorded as L_orgThe original undistorted stereo image S is processed_orgIs recorded as R_orgDistorted stereoscopic image S to be evaluated_disIs recorded as L_disDistorted stereoscopic image S to be evaluated_disIs recorded as R_dis。

② pairs L_orgAnd L_dis、R_orgAnd R_disThe 4 images were divided into regions to obtain L_orgAnd L_dis、R_orgAnd R_disThe sensitive area matrix mapping map corresponding to each of the 4 images will L_orgAnd L_disRespectively carrying out region division to obtain coefficient matrixes of respectively corresponding sensitive region matrix mapping graphs, and recording the coefficient matrixes as A_LFor A_LThe value of the coefficient at the middle coordinate position (i, j), which is denoted as A_L(i,j)，

R is to be_orgAnd R_disRespectively carrying out region division to obtain coefficient matrixes of respectively corresponding sensitive region matrix mapping graphs, and recording the coefficient matrixes as A_RFor A_RThe value of the coefficient at the middle coordinate position (i, j), which is denoted as A_R(i,j)，

Wherein, 0 is not less than i not more than (W-8), 0 is not less than j not more than (H-8), W represents L_org、L_dis、R_orgAnd R_disH denotes L_org、L_dis、R_orgAnd R_disIs high.

In this embodiment, L of step ②_orgAnd L_disCoefficient matrix A of the respective corresponding sensitive area matrix map_LThe acquisition process comprises the following steps:

② -a1, pair L_orgProcessing with Sobel operator in horizontal and vertical directions to obtain L_orgRespectively, as Z, in the horizontal direction and in the vertical direction_h，l1And Z_v，l1Then calculate L_orgIs marked as Z_l1， $Z_{l1}(x,y)=\sqrt{{\left(Z_{h,l1}(x,y)\right)}^2+{\left(Z_{v,l1}(x,y)\right)}^2},$ Wherein Z is_l1(x, y) represents Z_l1Gradient amplitude, Z, of pixel point with (x, y) as middle coordinate position_h，l1(x, y) represents Z_h，l1The horizontal gradient value Z of the pixel point with the (x, y) middle coordinate position_v，l1(x, y) represents Z_v，l1The gradient value in the vertical direction of the pixel point with the middle coordinate position (x, y) is that x is more than or equal to 1 and less than or equal to W ', y is more than or equal to 1 and less than or equal to H ', wherein W ' represents Z_l1Width of (A), H' represents Z_l1Is high.

② -a2, pair L_disProcessing with Sobel operator in horizontal and vertical directions to obtain L_disRespectively, as Z, in the horizontal direction and in the vertical direction_h，l2And Z_v，l2Then calculate L_disIs marked as Z_l2， $Z_{l2}(x,y)=\sqrt{{\left(Z_{h,l2}(x,y)\right)}^2+{\left(Z_{v,l2}(x,y)\right)}^2},$ Wherein Z is_l2(x, y) represents Z_l2Gradient amplitude, Z, of pixel point with (x, y) as middle coordinate position_h，l2(x, y) represents Z_h，l2The horizontal gradient value Z of the pixel point with the (x, y) middle coordinate position_v，l2(x, y) represents Z_v，l2The gradient value in the vertical direction of the pixel point with the middle coordinate position (x, y) is that x is more than or equal to 1 and less than or equal to W ', y is more than or equal to 1 and less than or equal to H ', wherein W ' represents Z_l2Width of (A), H' represents Z_l2Is high.

② -a3, calculating the threshold value T required when dividing the region, $T=\mathrm{\&alpha;}\&times;\frac{1}{W^{\&prime;}\&times;H^{\&prime;}}\&times;(\underset{x=0}{\overset{W^{\&prime;}}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{H^{\&prime;}}{\mathrm{\&Sigma;}}}{Z}_{l1}(x,y)+\underset{x=0}{\overset{W^{\&prime;}}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{H^{\&prime;}}{\mathrm{\&Sigma;}}}{Z}_{l2}(x,y)),$ wherein W' represents Z_l1And Z_l2Width of (A), H' represents Z_l1And Z_l2α is constant, Z_l1(x, y) represents Z_l1Gradient amplitude, Z, of pixel point with (x, y) as middle coordinate position_l2(x, y) represents Z_l2And the gradient amplitude of the pixel point with the middle coordinate position (x, y).

② -a4, mixing Z with_l1The gradient amplitude of the pixel point with the middle coordinate position (i, j) is recorded as Z_l1(i, j) reacting Z_l2The gradient amplitude of the pixel point with the middle coordinate position (i, j) is recorded as Z_l2(i, j), judgment of Z_l1(i,j)>T or Z_l2(i,j)>If T is true, then a determination L is made_orgAnd L_disThe pixel point with the middle coordinate position (i, j) belongs to the sensitive area, and order A_L(i, j) =1, otherwise, determination L_orgAnd L_disThe pixel point with the middle coordinate position (i, j) belongs to the non-sensitive area, and order A_L(i, j) =0, wherein i is more than or equal to 0 and is less than or equal to (W-8), and j is more than or equal to 0 and is less than or equal to (H-8).

In this embodiment, R in step ②_orgAnd R_disCoefficient matrix A of the respective corresponding sensitive area matrix map_RIs obtained by：

② -b1, p.R_orgPerforming Sobel operator processing in horizontal and vertical directions to obtain R_orgRespectively, as Z, in the horizontal direction and in the vertical direction_h，r1And Z_v，r1Then calculating R_orgIs marked as Z_r1， $Z_{r1}(x,y)=\sqrt{{\left(Z_{h,r1}(x,y)\right)}^2+{\left(Z_{v,r1}(x,y)\right)}^2},$ Wherein Z is_r1(x, y) represents Z_r1Gradient amplitude, Z, of pixel point with (x, y) as middle coordinate position_h，r1(x, y) represents Z_h，r1The horizontal gradient value Z of the pixel point with the (x, y) middle coordinate position_v，r1(x, y) represents Z_v，r1The gradient value in the vertical direction of the pixel point with the middle coordinate position (x, y) is that x is more than or equal to 1 and less than or equal to W ', y is more than or equal to 1 and less than or equal to H ', wherein W ' represents Z_r1Width of (A), H' represents Z_r1Is high.

② -b2, p.R_disPerforming Sobel operator processing in horizontal and vertical directions to obtain R_disRespectively, as Z, in the horizontal direction and in the vertical direction_h，r2And Z_v,r2Then calculating R_disIs marked as Z_r2， $Z_{r2}(x,y)=\sqrt{{\left(Z_{h,r2}(x,y)\right)}^2+{\left(Z_{v,r2}(x,y)\right)}^2},$ Wherein Z is_r2(x, y) represents Z_r2Gradient amplitude, Z, of pixel point with (x, y) as middle coordinate position_h，r2(x, y) represents Z_h，r2The horizontal gradient value Z of the pixel point with the (x, y) middle coordinate position_v，r2(x, y) represents Z_v，r2The gradient value in the vertical direction of the pixel point with the middle coordinate position (x, y) is that x is more than or equal to 1 and less than or equal to W ', y is more than or equal to 1 and less than or equal to H ', wherein W ' represents Z_r2Width of (A), H' represents Z_r2Is high.

② -b3, calculation partitioningThe threshold value T' required for the region, $T^{\&prime;}=\mathrm{\&alpha;}\&times;\frac{1}{W^{\&prime;}\&times;H^{\&prime;}}\&times;(\underset{x=0}{\overset{W^{\&prime;}}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{H^{\&prime;}}{\mathrm{\&Sigma;}}}{Z}_{r1}(x,y)+\underset{x=0}{\overset{W^{\&prime;}}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{H^{\&prime;}}{\mathrm{\&Sigma;}}}{Z}_{r2}(x,y)),$ wherein W' represents Z_r1And Z_r2Width of (A), H' represents Z_r1And Z_r2α is constant, Z_r1(x, y) represents Z_r1Gradient amplitude, Z, of pixel point with (x, y) as middle coordinate position_r2(x, y) represents Z_r2And the gradient amplitude of the pixel point with the middle coordinate position (x, y).

② -b4, mixing Z with_r1The gradient amplitude of the pixel point with the middle coordinate position (i, j) is recorded as Z_r1(i, j) reacting Z_r2The gradient amplitude of the pixel point with the middle coordinate position (i, j) is recorded as Z_r2(i, j), judgment of Z_r1(i,j)>T or Z_r2(i,j)>Whether T is true, and if so, determining R_orgAnd R_disThe pixel point with the middle coordinate position (i, j) belongs to the sensitive area, and order A_R(i, j) =1, otherwise, determine R_orgAnd R_disThe pixel point with the middle coordinate position (i, j) belongs to the non-sensitive area, and order A_R(i, j) =0, wherein i is more than or equal to 0 and is less than or equal to (W-8), and j is more than or equal to 0 and is less than or equal to (H-8).

In the present embodiment, a set of distorted stereo images under different distortion degrees of different distortion types is created by using 12 undistorted stereo images as shown in fig. 2a, 2b, 2c, 2d, 2e, 2f, 2g, 2H, 2i, 2j, 2k and 2l, the distortion types include JPEG compression, JP2K compression, white gaussian noise, gaussian blur and H264 coding distortion, and the left viewpoint image and the right viewpoint image of the stereo image are simultaneously distorted to the same degree, the set of distorted stereo images includes 312 distorted stereo images, wherein the total number of distorted stereo images of JPEG compression is 60, the total number of distorted stereo images of JPEG2000 compression is 60, the total number of distorted stereo images of white gaussian noise is 60, the total number of distorted stereo images of gaussian blur is 60, and the total number of distorted stereo images of H264 coding is 72. The above-described region segmentation is performed on the 312 stereoscopic images.

In this embodiment, the α value determines the accuracy of the sensitive region division, and if the value is too large, the sensitive region will be mistaken for the non-sensitive region, and if the value is too small, the non-sensitive region will be mistaken for the sensitive region, so the determination process of the value is determined by the contribution of the left viewpoint image quality or the right viewpoint image quality to the stereoscopic image quality.

③ mixing L_orgAnd L_disThe 2 images were divided into (W-7) × (H-7) overlapping blocks of size 8 × 8, respectively, and then L was calculated_orgAnd L_disThe structural amplitude distortion mapping maps of two overlapped blocks with the same coordinate positions in 2 images are recorded as B in the coefficient matrix of the structural amplitude distortion mapping maps_LFor B_LThe value of the coefficient at the middle coordinate position (i, j), which is denoted as B_L(i,j)， $B_L(i,j)=\frac{2\&times;{\mathrm{\&sigma;}}_{\mathrm{org},\mathrm{dis},L}(i,j)+C_1}{{\left({\mathrm{\&sigma;}}_{\mathrm{org},L}(i,j)\right)}^2+{\left({\mathrm{\&sigma;}}_{\mathrm{dis},L}(i,j)\right)}^2+C_1},$ Wherein, B_L(i, j) also denotes L_orgOverlapping block L of size 8 × 8 with middle upper left coordinate position (i, j)_disThe upper-left coordinate position is the structural amplitude distortion value of the overlapped block with the size of 8 × 8 (i, j), ${\mathrm{\&sigma;}}_{\mathrm{org},L}(i,j)=\sqrt{\frac{1}{64}\underset{x=0}{\overset{7}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{7}{\mathrm{\&Sigma;}}}{(L_{\mathrm{org}}(i+x,j+y)-U_{\mathrm{org},L}(i,j))}^2},$ $U_{\mathrm{org},L}(i,j)=\frac{1}{64}\underset{x=0}{\overset{7}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{7}{\mathrm{\&Sigma;}}}{L}_{\mathrm{org}}(i+x,j+y),$ ${\mathrm{\&sigma;}}_{\mathrm{dis},L}(i,j)=\sqrt{\frac{1}{64}\underset{x=0}{\overset{7}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{7}{\mathrm{\&Sigma;}}}{(L_{\mathrm{dis}}(i+x,j+y)-U_{\mathrm{dis},L}(i,j))}^2},$ $U_{\mathrm{dis},L}(i,j)=\frac{1}{64}\underset{x=0}{\overset{7}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{7}{\mathrm{\&Sigma;}}}{L}_{\mathrm{dis}}(i+x,j+y),$ ${\mathrm{\&sigma;}}_{\mathrm{org},\mathrm{dis},L}(i,j)=\frac{1}{64}\underset{x=0}{\overset{7}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{7}{\mathrm{\&Sigma;}}}((L_{\mathrm{org}}(i+x,j+y)-U_{\mathrm{org},L}(i,j))\&times;(L_{\mathrm{dis}}(i+x,j+y)-U_{\mathrm{dis},L}(i,j))),$ L_org(i + x, j + y) denotes L_orgThe pixel value of the pixel point with the middle coordinate position of (i + x, j + y), L_dis(i + x, j + y) denotes L_disThe pixel value C of the pixel point with the middle coordinate position of (i + x, j + y)₁Represents a constant, C₁To avoid the use of $B_L(i,j)=\frac{2\&times;{\mathrm{\&sigma;}}_{\mathrm{org},\mathrm{dis},L}(i,j)+C_1}{{\left({\mathrm{\&sigma;}}_{\mathrm{org},L}(i,j)\right)}^2+{\left({\mathrm{\&sigma;}}_{\mathrm{dis},L}(i,j)\right)}^2+C_1},$ The denominator of (A) is zero, and can be taken as C in the practical application process₁=0.01, where 0. ltoreq. i.ltoreq (W-8), 0. ltoreq. j.ltoreq.H-8.

Here, considering the correlation between the pixel points of the image, an overlapped block having a size of 8 × 8 has 7 column overlaps with its most adjacent left or right block, and similarly, the 8 × 8 overlapped block has 7 row overlaps with its most adjacent upper or lower block.

R is to be_orgAnd R_disThe 2 images were each divided into (W-7) × (H-7) overlapping blocks of size 8 × 8, and R was calculated_orgAnd R_disThe structural amplitude distortion mapping maps of two overlapped blocks with the same coordinate positions in 2 images are recorded as B in the coefficient matrix of the structural amplitude distortion mapping maps_RFor B_RThe value of the coefficient at the middle coordinate position (i, j), which is denoted as B_R(i,j)， $B_R(i,j)=\frac{2\&times;{\mathrm{\&sigma;}}_{\mathrm{org},\mathrm{dis},R}(i,j)+C_1}{{\left({\mathrm{\&sigma;}}_{\mathrm{org},R}(i,j)\right)}^2+{\left({\mathrm{\&sigma;}}_{\mathrm{dis},R}(i,j)\right)}^2+C_1},$ Wherein, B_R(i, j) also represents R_orgThe size of the overlapped block with the upper left corner coordinate position of (i, j) being 8 × 8 and R_disThe upper-left coordinate position is the structural amplitude distortion value of the overlapped block with the size of 8 × 8 (i, j), ${\mathrm{\&sigma;}}_{\mathrm{org},R}(i,j)=\sqrt{\frac{1}{64}\underset{x=0}{\overset{7}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{7}{\mathrm{\&Sigma;}}}{(R_{\mathrm{org}}(i+x,j+y)-U_{\mathrm{org},R}(i,j))}^2},$ $U_{\mathrm{org},R}(i,j)=\frac{1}{64}\underset{x=0}{\overset{7}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{7}{\mathrm{\&Sigma;}}}{R}_{\mathrm{org}}(i+x,j+y),$ ${\mathrm{\&sigma;}}_{\mathrm{dis},R}(i,j)=\sqrt{\frac{1}{64}\underset{x=0}{\overset{7}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{7}{\mathrm{\&Sigma;}}}{(R_{\mathrm{dis}}(i+x,j+y)-U_{\mathrm{dis},R}(i,j))}^2},$ $U_{\mathrm{dis},R}(i,j)=\frac{1}{64}\underset{x=0}{\overset{7}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{7}{\mathrm{\&Sigma;}}}{R}_{\mathrm{dis}}(i+x,j+y),$ ${\mathrm{\&sigma;}}_{\mathrm{org},\mathrm{dis},R}(i,j)=\frac{1}{64}\underset{x=0}{\overset{7}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{7}{\mathrm{\&Sigma;}}}((R_{\mathrm{org}}(i+x,j+y)-U_{\mathrm{org},R}(i,j))\&times;(R_{\mathrm{dis}}(i+x,j+y)-U_{\mathrm{dis},R}(i,j))),$ R_org(i + x, j + y) represents R_orgThe pixel value R of the pixel point with the middle coordinate position of (i + x, j + y)_dis(i + x, j + y) represents R_disThe pixel value C of the pixel point with the middle coordinate position of (i + x, j + y)₁Represents a constant, C₁To avoid the use of $B_R(i,j)=\frac{2\&times;{\mathrm{\&sigma;}}_{\mathrm{org},\mathrm{dis},R}(i,j)+C_1}{{\left({\mathrm{\&sigma;}}_{\mathrm{org},R}(i,j)\right)}^2+{\left({\mathrm{\&sigma;}}_{\mathrm{dis},R}(i,j)\right)}^2+C_1}$ The denominator of (A) is zero, and can be taken as C in the practical application process₁=0.01, where 0. ltoreq. i.ltoreq (W-8), 0. ltoreq. j.ltoreq.H-8.

④ pairs L_orgAnd L_disRespectively carrying out Sobel operator processing in the horizontal direction and Sobel operator processing in the vertical direction on the 2 images to respectively obtain L_orgAnd L_dis2 imagesL corresponding to the horizontal gradient matrix map and the vertical gradient matrix map respectively_orgThe coefficient matrix of the corresponding horizontal gradient matrix mapping graph obtained after horizontal Sobel operator processing is carried out is marked as I_h，org,LFor I_h，org,LThe value of the coefficient at the middle coordinate position (I, j), which is denoted as I_h，org,L(i,j)， $\begin{array}{c}{I}_{h,\mathrm{org},L}(i,j)=L_{\mathrm{org}}(i+2,j)+2L_{\mathrm{org}}(i+2,j+1)+L_{\mathrm{org}}(i+2,j+2)\\ -L_{\mathrm{org}}(i,j)-2L_{\mathrm{org}}(i,j+1)-L_{\mathrm{org}}(i,j+2)\end{array},$ L will be mixed_orgThe coefficient matrix of the corresponding vertical gradient matrix mapping graph obtained after the vertical Sobel operator is implemented is marked as I_v，org，LFor I_v，org,LThe value of the coefficient at the middle coordinate position (I, j), which is denoted as I_v，org,L(i,j)， $\begin{array}{c}{I}_{v,\mathrm{org},L}(i,j)=L_{\mathrm{org}}(i,j+2)+2L_{\mathrm{org}}(i+1,j+2)+L_{\mathrm{org}}(i+2,j+2)\\ -L_{\mathrm{org}}(i,j)-2L_{\mathrm{org}}(i+1,j)-L_{\mathrm{org}}(i+2,j)\end{array},$ L will be mixed_disThe coefficient matrix of the corresponding horizontal gradient matrix mapping graph obtained after horizontal Sobel operator processing is carried out is marked as I_h，dis，LFor I_h，dis，LThe value of the coefficient at the middle coordinate position (I, j), which is denoted as I_h，dis，L(i,j)， $\begin{array}{c}{I}_{h,\mathrm{dis},L}(i,j)=L_{\mathrm{dis}}(i+2,j)+2L_{\mathrm{dis}}(i+2,j+1)+L_{\mathrm{dis}}(i+2,j+2)\\ -L_{\mathrm{dis}}(i,j)-2L_{\mathrm{dis}}(i,j+1)-L_{\mathrm{dis}}(i,j+2)\end{array},$ L will be mixed_disThe coefficient matrix of the corresponding vertical gradient matrix mapping graph obtained after the vertical Sobel operator is implemented is marked as I_v，dis，LFor I_v，dis，LThe value of the coefficient at the middle coordinate position (I, j), which is denoted as I_v，dis，L(i,j)， $\begin{array}{c}{I}_{v,\mathrm{dis},L}(i,j)=L_{\mathrm{dis}}(i,j+2)+2L_{\mathrm{dis}}(i+1,j+2)+L_{\mathrm{dis}}(i+2,j+2)\\ -L_{\mathrm{dis}}(i,j)-2L_{\mathrm{dis}}(i+1,j)-L_{\mathrm{dis}}(i+2,j)\end{array},$ Wherein, L_org(i+2,j)、L_org(i+2,j+1)、L_org(i+2,j+2)、L_org(i,j)、L_org(i,j+1)、L_org(i,j+2)、L_org(i+1,j+2)、L_org(i +1, j) are respectively corresponded to L_orgThe pixel values of the pixel points with the middle coordinate positions of (i +2, j), (i +2, j +1), (i +2, j +2), (i, j +1), (i, j +2), (i +1, j +2), and (i +1, j), L_dis(i+2,j)、L_dis(i+2,j+1)、L_dis(i+2,j+2)、L_dis(i,j)、L_dis(i,j+1)、L_dis(i,j+2)、L_dis(i+1,j+2)、L_dis(i +1, j) are respectively corresponded to L_disThe middle coordinate position is the pixel value of the pixel point of (i +2, j), (i +2, j +1), (i +2, j +2), (i, j +1), (i, j +2), (i +1, j +2), or (i +1, j).

To R_orgAnd R_disRespectively carrying out Sobel operator processing in the horizontal direction and the vertical direction on the 2 images to respectively obtain R_orgAnd R_disThe gradient matrix mapping map in the horizontal direction and the gradient matrix mapping map in the vertical direction corresponding to the 2 images are respectively obtained by mapping R_orgThe coefficient matrix of the corresponding horizontal gradient matrix mapping graph obtained after horizontal Sobel operator processing is carried out is marked as I_h，org,RFor I_h，org,RThe value of the coefficient at the middle coordinate position (I, j), which is denoted as I_h，org,R(i,j)， $\begin{array}{c}{I}_{h,\mathrm{org},R}(i,j)=R_{\mathrm{org}}(i+2,j)+2R_{\mathrm{org}}(i+2,j+1)+R_{\mathrm{org}}(i+2,j+2)\\ -R_{\mathrm{org}}(i,j)-2R_{\mathrm{org}}(i,j+1)-R_{\mathrm{org}}(i,j+2)\end{array},$ R is to be_orgThe coefficient matrix of the corresponding vertical gradient matrix mapping graph obtained after the vertical Sobel operator is implemented is marked as I_v，org，RFor I_v，org，RThe value of the coefficient at the middle coordinate position (I, j), which is denoted as I_v，org,R(i,j)， $\begin{array}{c}{I}_{v,\mathrm{org},R}(i,j)=R_{\mathrm{org}}(i,j+2)+2R_{\mathrm{org}}(i+1,j+2)+R_{\mathrm{org}}(i+2,j+2)\\ -R_{\mathrm{org}}(i,j)-2R_{\mathrm{org}}(i+1,j)-R_{\mathrm{org}}(i+2,j)\end{array},$ R is to be_disThe coefficient matrix of the corresponding horizontal gradient matrix mapping graph obtained after horizontal Sobel operator processing is carried out is marked as I_h，dis，RFor I_h，dis，RThe value of the coefficient at the middle coordinate position (I, j), which is denoted as I_h，dis，R(i,j)， $\begin{array}{c}{I}_{h,\mathrm{dis},R}(i,j)=R_{\mathrm{dis}}(i+2,j)+2R_{\mathrm{dis}}(i+2,j+1)+R_{\mathrm{dis}}(i+2,j+2)\\ -R_{\mathrm{dis}}(i,j)-2R_{\mathrm{dis}}(i,j+1)-R_{\mathrm{dis}}(i,j+2)\end{array},$ R is to be_disThe coefficient matrix of the corresponding vertical gradient matrix mapping graph obtained after the vertical Sobel operator is implemented is marked as I_v，dis，RFor I_v，dis，RThe value of the coefficient at the middle coordinate position (I, j), which is denoted as I_v，dis，R(i,j)， $\begin{array}{c}{I}_{v,\mathrm{dis},R}(i,j)=R_{\mathrm{dis}}(i,j+2)+2R_{\mathrm{dis}}(i+1,j+2)+R_{\mathrm{dis}}(i+2,j+2)\\ -R_{\mathrm{dis}}(i,j)-2R_{\mathrm{dis}}(i+1,j)-R_{\mathrm{dis}}(i+2,j)\end{array},$ Wherein R is_org(i+2,j)、R_org(i+2,j+1)、R_org(i+2,j+2)、R_org(i,j)、R_org(i,j+1)、R_org(i,j+2)、R_org(i+1,j+2)、R_org(i +1, j) each represents R_orgThe middle coordinate position is the pixel value, R, of the pixel points of (i +2, j), (i +2, j +1), (i +2, j +2), (i, j +1), (i, j +2), (i +1, j +2), and (i +1, j)_dis(i+2,j)、R_dis(i+2,j+1)、R_dis(i+2,j+2)、R_dis(i,j)、R_dis(i,j+1)、R_dis(i,j+2)、R_dis(i+1,j+2)、R_dis(i +1, j) each represents R_disThe middle coordinate position is the pixel value of the pixel point of (i +2, j), (i +2, j +1), (i +2, j +2), (i, j +1), (i, j +2), (i +1, j +2), or (i +1, j).

⑤ calculation L_orgAnd L_disThe structural direction distortion mapping maps of two overlapped blocks with the same coordinate positions in 2 images are recorded as E in the coefficient matrix of the structural direction distortion mapping maps_LFor E_LThe value of the coefficient at the middle coordinate position (i, j), denoted as E_L(i,j)， $E_L(i,j)=\frac{I_{h,\mathrm{org},L}(i,j)\&times;I_{h,\mathrm{dis},L}(i,j)+I_{v,\mathrm{org},L}(i,j)\&times;I_{v,\mathrm{dis},L}(i,j)+C_2}{\sqrt{{\left(I_{h,\mathrm{org},L}(i,j)\right)}^2+{\left(I_{v,\mathrm{org},L}(i,j)\right)}^2}\&times;\sqrt{{\left(I_{h,\mathrm{dis},L}(i,j)\right)}^2+{\left(I_{v,\mathrm{dis},L}(i,j)\right)}^2}+C_2},$ Wherein, C₂Represents a constant, C₂To avoid the use of $E_L(i,j)=\frac{I_{h,\mathrm{org},L}(i,j)\&times;I_{h,\mathrm{dis},L}(i,j)+I_{v,\mathrm{org},L}(i,j)\&times;I_{v,\mathrm{dis},L}(i,j)+C_2}{\sqrt{{\left(I_{h,\mathrm{org},L}(i,j)\right)}^2+{\left(I_{v,\mathrm{org},L}(i,j)\right)}^2}\&times;\sqrt{{\left(I_{h,\mathrm{dis},L}(i,j)\right)}^2+{\left(I_{v,\mathrm{dis},L}(i,j)\right)}^2}+C_2}$ The denominator of C is zero, and C can be taken in the practical application process₂=0.02。

Calculation of R_orgAnd R_disThe structural direction distortion mapping maps of two overlapped blocks with the same coordinate positions in 2 images are recorded as E in the coefficient matrix of the structural direction distortion mapping maps_RFor E_RThe value of the coefficient at the middle coordinate position (i, j), denoted as E_R(i,j)， $E_R(i,j)=\frac{I_{h,\mathrm{org},R}(i,j)\&times;I_{h,\mathrm{dis},R}(i,j)+I_{v,\mathrm{org},R}(i,j)\&times;I_{v,\mathrm{dis},R}(i,j)+C_2}{\sqrt{{\left(I_{h,\mathrm{org},R}(i,j)\right)}^2+{\left(I_{v,\mathrm{org},R}(i,j)\right)}^2}\&times;\sqrt{{\left(I_{h,\mathrm{dis},R}(i,j)\right)}^2+{\left(I_{v,\mathrm{dis},R}(i,j)\right)}^2}+C_2},$ C₂To avoid the use of $E_R(i,j)=\frac{I_{h,\mathrm{org},R}(i,j)\&times;I_{h,\mathrm{dis},R}(i,j)+I_{v,\mathrm{org},R}(i,j)\&times;I_{v,\mathrm{dis},R}(i,j)+C_2}{\sqrt{{\left(I_{h,\mathrm{org},R}(i,j)\right)}^2+{\left(I_{v,\mathrm{org},R}(i,j)\right)}^2}\&times;\sqrt{{\left(I_{h,\mathrm{dis},R}(i,j)\right)}^2+{\left(I_{v,\mathrm{dis},R}(i,j)\right)}^2}+C_2}$ The denominator of C is zero, and C can be taken in the practical application process₂=0.02。

⑥ calculation L_orgAnd L_disStructural distortion evaluation value of (1), denoted as Q^L，Q^L=ω₁×Q_m，L+ω₂×Q_nm，L， $Q_{m,L}=\frac{1}{N_{L,m}}\underset{i=0}{\overset{W-8}{\mathrm{\&Sigma;}}}\underset{j=0}{\overset{H-8}{\mathrm{\&Sigma;}}}(0.5\&times;(B_L(i,j)+E_L(i,j))\&times;A_L(i,j)),$ $N_{L,m}=\underset{i=0}{\overset{W-8}{\mathrm{\&Sigma;}}}\underset{j=0}{\overset{H-8}{\mathrm{\&Sigma;}}}{A}_L(i,j),$ $Q_{\mathrm{nm},L}=\frac{1}{N_{L,\mathrm{nm}}}\underset{i=0}{\overset{W-8}{\mathrm{\&Sigma;}}}\underset{j=0}{\overset{H-8}{\mathrm{\&Sigma;}}}(0.5\&times;(B_L(i,j)+E_L(i,j))\&times;(1-A_L(i,j))),$ $N_{L,\mathrm{nm}}=\underset{i=0}{\overset{W-8}{\mathrm{\&Sigma;}}}\underset{j=0}{\overset{H-8}{\mathrm{\&Sigma;}}}(1-A_L(i,j)),$ Wherein, ω is₁Representation L_orgAnd L_disWeight value of middle sensitive area, omega₂Representation L_orgAnd L_disThe weight value of the non-sensitive area.

Calculation of R_orgAnd R_disStructural distortion evaluation value of (1), denoted as Q^R，Q^R=ω′₁×Q_m，R+ω′₂×Q_nm，R， $Q_{m,R}=\frac{1}{N_{R,m}}\underset{i=0}{\overset{W-8}{\mathrm{\&Sigma;}}}\underset{j=0}{\overset{H-8}{\mathrm{\&Sigma;}}}(0.5\&times;(B_R(i,j)+E_R(i,j))\&times;A_R(i,j)),$ $N_{R,m}=\underset{i=0}{\overset{W-8}{\mathrm{\&Sigma;}}}\underset{j=0}{\overset{H-8}{\mathrm{\&Sigma;}}}{A}_R(i,j),$ $Q_{\mathrm{nm},R}=\frac{1}{N_{R,\mathrm{nm}}}\underset{i=0}{\overset{W-8}{\mathrm{\&Sigma;}}}\underset{j=0}{\overset{H-8}{\mathrm{\&Sigma;}}}(0.5\&times;(B_R(i,j)+E_R(i,j))\&times;(1-A_R(i,j))),$ $N_{R,\mathrm{nm}}=\underset{i=0}{\overset{W-8}{\mathrm{\&Sigma;}}}\underset{j=0}{\overset{H-8}{\mathrm{\&Sigma;}}}(1-A_R(i,j)),$ Wherein, omega'₁Represents R_orgAnd R_disWeight value of Medium sensitive region, ω'₂Represents R_orgAnd R_disThe weight value of the non-sensitive area.

In the present embodiment, fig. 3 shows a block diagram of implementation of left viewpoint image quality evaluation. A distorted stereo image set composed of 312 distorted stereo images is established by using 12 undistorted stereo images as shown in fig. 2a to 2l, and subjective quality evaluation is performed on the 312 distorted stereo images by using a known subjective quality evaluation method to obtain an average subjective score Difference (DMOS) of the 312 distorted stereo images, that is, a subjective quality score of each distorted stereo image. DMOS is a difference between a subjective score Mean (MOS) and a full score (100), that is, DMOS =100-MOS, and thus, the larger the DMOS value, the worse the quality of a distorted stereoscopic image, the smaller the DMOS value, the better the quality of a distorted stereoscopic image, and the DMOS has a value range of [0,100 ″]On the other hand, the Q corresponding to each distorted stereoscopic image is calculated for the 312 distorted stereoscopic images according to steps ① to ⑥ of the method of the present invention_m,LAnd Q_nm，L(ii) a Then using Q^L=ω₁×Q_m，L+(1-ω₁)×Q_nm,LPerforming four-parameter L logistic function nonlinear fitting to obtain α and omega₁The value is obtained. Here, 3 common objective parameters of the evaluation method for evaluating image quality are used as evaluation indexes, namely, Pearson Correlation Coefficient (CC), Spearman Correlation Coefficient (SROCC) and Mean square Error Coefficient (RMSE) under nonlinear regression condition, CC reflects the accuracy of an objective model of a distorted stereo image evaluation function, and SROCC reflects the monotonicity between the objective model and subjective perception. RMSE reflects the accuracy of its prediction. The higher the CC and SROCC values areThe better the correlation between the bright stereo image objective evaluation method and DMOS, the lower the RMSE value, the better the correlation between the stereo image objective evaluation method and DMOS, FIG. 4a shows that the difference is α and ω₁The CC performance variation between left view image quality and subjective perceptual quality for the lower 312 stereo images, and fig. 4b gives the difference α and ω₁The SROCC performance variation between left view image quality and subjective perceptual quality for the lower 312 stereo images, fig. 4c gives the difference α and ω₁The RMSE performance variation between the left viewpoint image quality and the subjective perceptual quality of the lower 312 stereo images, and by analyzing fig. 4a, 4b and 4c, it can be known that CC and SROCC will follow ω₁The value becomes larger and the RMSE becomes larger with ω₁The larger and smaller values indicate that the left view image quality is mainly determined by the quality of the sensitive area, while the change of α value has little impact on the performance between left view image quality and subjective perception fig. 5a gives the difference between ω and₁=1、ω₂where =0, the CC performance variation between left view image quality and subjective perceptual quality for 312 stereo images at different α, and fig. 5b shows the variation between ω and the subjective perceptual quality₁=1、ω₂In the case of =0, the SROCC performance variation between the left view image quality and the subjective perceptual quality of 312 stereo images at different α, and fig. 5c shows the SROCC performance variation at ω₁=1、ω₂Where =0, the RMSE performance between the left viewpoint image quality and the subjective perceptual quality of 312 stereo images at different α is varied, and analyzing fig. 5a, 5b, and 5c, it can be seen that the CC, SROCC, and RMSE values all fluctuate in percentile, but have a peak₁=1，α=2.1。

⑦ according to Q^LAnd Q^RCalculating a distorted stereo image S to be evaluated_disRelative to the original undistorted stereo image S_orgThe spatial frequency similarity measure of (2), denoted as Q_F，Q_F=β₁×Q^L+(1-β₁)×Q^RWherein, β₁Represents Q^LThe weight of (2).

In this embodiment, β of step ⑦₁The acquisition process comprises the following steps:

⑦ -1, using n undistorted stereo images to build a distorted stereo image set under different distortion types and different distortion degrees, wherein the distorted stereo image set comprises a plurality of distorted stereo images, wherein n is more than or equal to 1.

⑦ -2, obtaining the average subjective score difference of each distorted stereo image in the distorted stereo image set by a subjective quality evaluation method, and marking as DMOS, DMOS =100-MOS, wherein MOS represents the subjective score mean, DMOS ∈ [0,100 ].

⑦ -3, according to the operation procedures of step ① to step ⑥, an evaluation value Q of a sensitive area of a left viewpoint image of each distorted stereoscopic image in the distorted stereoscopic image set with respect to a left viewpoint image of a corresponding undistorted stereoscopic image is calculated_m,LAnd evaluation value Q of non-sensitive region_nm，L。

⑦ -4, fitting the mean subjective score difference DMOS and corresponding Q of distorted stereo images in the set of distorted stereo images by a mathematical fitting method_m，LAnd Q_nm，LThereby obtaining β₁The value is obtained.

In this embodiment, β₁Determine Q^LThe contribution to the stereo image quality, for blockiness, is about half of the sum of the quality of the left viewpoint image and the quality of the right viewpoint image, and for blur distortion, the stereo image quality depends mainly on the viewpoint with better quality₁First, the Q corresponding to each distorted stereoscopic image is calculated according to steps ① to ⑥ of the method of the present invention for the 312 distorted stereoscopic images^LAnd Q^RThen using four parameter fitting to obtain β₁The difference β is given in fig. 6a₁The variation of CC performance between the quality of the left and right view images and the subjective perceived quality under (fig. 6 a) gives a difference β₁The SROCC performance variation between the quality of the left and right view images at hand and the subjective perceived quality, figure 6a shows the difference β₁Quality of left and right viewpoint imagesAnalysis of FIGS. 6a, 6b, and 6c for the change in RMSE performance from subjective perceptual quality shows that the variation with β₁The change in value for CC, SROCC and RMSE was modest, fluctuating in percentiles, but peaked here, β was taken₁=0.5。

⑧ calculation L_orgAnd R_orgIs used as a matrix for absolute difference image

Is shown, i.e.

Calculation L_disAnd R_disIs used as a matrix for absolute difference image

Is shown, i.e.Wherein, "| |" is an absolute value symbol.

⑨ will beAnd

the 2 images are respectively divided into

A non-overlapping block of size 8 × 8, and thenAnd

all the blocks in the 2 images are respectively subjected to singular value decomposition to obtain

Corresponding singular value map composed of singular value matrix of each block thereof anda corresponding singular value mapping map composed of the singular value matrix of each block thereof is to

The coefficient matrix of the singular value mapping graph obtained after singular value decomposition is recorded as G_orgFor G_orgThe coordinate position in the singular value matrix of the nth block is the singular value at (p, q), which is recorded as

Will be provided withThe coefficient matrix of the singular value mapping graph obtained after singular value decomposition is recorded as G_disFor G_disThe coordinate position in the singular value matrix of the nth block is the singular value at (p, q), which is recorded as

Wherein, W_LRTo represent

And

width of (H)_LRTo represent

And

is high in the direction of the horizontal axis, $0\&le;n\&le;\frac{W_{\mathrm{LR}}\&times;{\mathrm{<figure-callout\; id="8"\; label="H\; LR"\; filenames="HDA00001627421000072.png,HDA00001627421000082.png"\; state="\{\{state\}\}">H</figure-callout>}}_{\mathrm{LR}}}{8\&times;8}-1,$ 0≤p≤7,0≤q≤7。

here, in order to reduce the computational complexity, a block of 8 × 8 has no repeated columns or repeated rows with its nearest left or right block or top or bottom block, i.e., blocks do not overlap with blocks.

⑩ calculation

Corresponding singular value map and

the singular value deviation evaluation value of the corresponding singular value map, denoted as K, $K=\frac{64}{W_{\mathrm{LR}}\&times;H_{\mathrm{LR}}}\&times;\underset{n=0}{\overset{W_{\mathrm{LR}}\&times;H_{\mathrm{LR}}/64-1}{\mathrm{\&Sigma;}}}\frac{\underset{p=0}{\overset{7}{\mathrm{\&Sigma;}}}(G_{\mathrm{org}}^n(p,p)\&times;|G_{\mathrm{org}}^n(p,p)-G_{\mathrm{dis}}^n(p,p)\left|\right)}{\underset{p=0}{\overset{7}{\mathrm{\&Sigma;}}}{G}_{\mathrm{org}}^n(p,p)},$ wherein the content of the first and second substances,

represents G_orgThe coordinate position in the singular value matrix of the nth block is the singular value at (p, p),

represents G_disThe coordinate position in the singular value matrix of the nth block is the singular value at (p, p).

To pair

And

respectively carrying out singular value decomposition to respectively obtain

And2 orthogonal matrixes and 1 singular value matrix corresponding to each other are used for generating a plurality of singular value matrixes 2 orthogonal matrixes obtained after singular value decomposition are respectively recorded as chi_orgAnd V_orgWill be

The singular value matrix obtained after singular value decomposition is recorded as O_org，

Will be provided with

2 orthogonal matrixes obtained after singular value decomposition are respectively recorded as chi_disAnd V_disWill beThe singular value matrix obtained after singular value decomposition is recorded as O_dis，

Respectively calculate

And

2 residual matrix images of the images after the singular value deprivation

The residual matrix image after the singular value deprivation is marked as X_org，X_org=χ_org×Λ×V_orgWill beThe residual matrix image after the singular value deprivation is marked as X_dis，X_dis=χ_dis×Λ×V_disWhere Λ denotes the identity matrix, Λ size and O_orgAnd O_disAre consistent in size.

Calculating X_orgAnd X_disThe mean deviation ratio of (D) is recorded as

Wherein X represents X_orgAnd X_disThe abscissa of the pixel point in (1) and y represents X_orgAnd X_disThe ordinate of the pixel point in (1).

Calculating a distorted stereo image S to be evaluated_disRelative to the original undistorted stereo image S_orgThe stereo perception evaluation metric of (1), denoted as Q_S，

Wherein τ represents a constant for adjusting K and

at Q_SPlays an important role in.

In this embodiment, the absolute difference image of the 312 distorted stereoscopic images and the 10 undistorted stereoscopic images is first obtained, and then steps ⑧ to ⑧ of the method according to the invention

Calculating to obtain the corresponding K sum of each distorted stereo image

Here, the magnitude of τ determines the importance of the singular value bias and the residual information in the depth perception evaluation. FIG. 7a shows the CC performance variation between the stereo perceived quality and the subjective perception of 312 distorted stereo images at different τ, FIG. 7b shows the SROCC performance variation between the stereo perceived quality and the subjective perception of 312 distorted stereo images at different τ, FIG. 7c shows the RMSE performance variation between the stereo perceived quality and the subjective perception of 312 distorted stereo images at different τ, and τ is [ -164 ] in FIGS. 7a, 7b, and 7c]In-range variation, as shown in fig. 7a, 7b and 7c, it can be seen that the variation of CC, SROCC and RMSE and τ all have an extreme value and are approximately the same in position, where τ is-8.

According to Q_FAnd Q_SCalculating a distorted stereoscopic image S to be evaluated_disThe image quality evaluation score of (1), denoted as Q, Q = Q_F×（Q_S）^ρWhere ρ represents a weight coefficient value.

In this embodiment, the 312 distorted stereoscopic images are processed according to steps ① to ① of the method of the present invention

Calculating to obtain Q corresponding to each distorted stereo image_FAnd Q_SThen adopt Q = Q_F×（Q_S）^ρPerforming four-parameter L logistic function nonlinear fitting to obtain rho, wherein the rho value determines the quality of left and right viewpoint images and the contribution of the stereo perception quality in the stereo image quality_FAnd Q_SThe values are all reduced along with the deepening of the distortion degree of the stereo image, so the value range of the rho value is more than 0. FIG. 8a shows 312 stereo images at different values of ρThe CC performance variation between the quality of the stereo image and the subjective perception quality is given in fig. 8b, the SROCC performance variation between the quality of the 312 stereo images and the subjective perception quality under different rho values is given in fig. 8c, the RMSE performance variation between the quality of the 312 stereo images and the subjective perception quality under different rho values is given in fig. 8a, fig. 8b and fig. 8c, it can be known that the consistency between the objective evaluation model of stereo image quality and the subjective perception is affected when the rho value is too large or too small, and the CC, SROCC and RMSE values have extreme points and the same approximate position when the rho value varies, where rho = 0.3.

The image quality evaluation function Q = Q of the distorted stereoscopic image obtained in this embodiment was analyzed_F×（Q_S）^0.3The correlation between the final evaluation result of (a) and the subjective score DMOS. First, the image quality evaluation function Q = Q of the distorted stereoscopic image obtained in the present embodiment_F×（Q_S）^0.3The method comprises the steps of calculating an output value Q of a final stereo image quality evaluation result, performing L objective function nonlinear fitting on the output value Q, and finally obtaining a performance index value between a stereo objective evaluation model and subjective perception, wherein 4 common objective parameters of an evaluation image quality evaluation method are used as evaluation indexes, namely CC, SROCC, constant Ratio (Outlier Ratio, OR) and RMSE.OR reflect the dispersion degree of the stereo image quality objective rating model, namely the proportion of the number of distorted stereo images of which the difference between the evaluation value after four-parameter fitting and DMOS is larger than a certain threshold value in all distorted stereo images is given in table 1, evaluation performance CC, SROCC, OR and RMSE coefficients are given in table 1, and the data in table 1 show that the image quality evaluation function Q = Q of the distorted stereo images obtained according to the embodiment_F×（Q_S）^0.3The correlation between the output value Q of the final evaluation result obtained by calculation and the subjective evaluation DMOS is very high, the CC value and the SROCC value both exceed 0.92, and the RMSE value is lower than 6.5, which shows that the objective evaluation result is more consistent with the result of subjective perception of human eyes, and the effectiveness of the method is demonstrated. Table 1 correlation between image quality evaluation score and subjective score of distorted stereoscopic image obtained by this embodiment

	Gblur	JP2K	JPEG	WN	H264	ALL
							Number of	60	60	60	60	72	312
CC	0.9658	0.9479	0.9533	0.9554	0.9767	0.9235
							SROCC	0.9655	0.9489	0.9524	0.9274	0.9545	0.9430
OR	0	0	0	0	0	0
							RMSE	5.4719	3.8180	4.3010	4.6151	3.0135	6.5890

Claims (3)

1. A three-dimensional image objective quality evaluation method based on structural distortion is characterized by comprising the following steps:

① order S_orgFor original undistorted stereo image, let S_disFor the distorted stereo image to be evaluated, the original undistorted stereo image S is taken_orgIs recorded as L_orgThe original undistorted stereo image S is processed_orgIs recorded as R_orgDistorted to be evaluatedStereo image S_disIs recorded as L_disDistorted stereoscopic image S to be evaluated_disIs recorded as R_dis；

② pairs L_orgAnd L_dis、R_orgAnd R_disThe 4 images were divided into regions to obtain L_orgAnd L_dis、R_orgAnd R_disThe sensitive area matrix mapping map corresponding to each of the 4 images will L_orgAnd L_disRespectively carrying out region division to obtain coefficient matrixes of respectively corresponding sensitive region matrix mapping graphs, and recording the coefficient matrixes as A_LFor A_LThe value of the coefficient at the middle coordinate position (i, j), which is denoted as A_L(i,j)，

R is to be_orgAnd R_disRespectively carrying out region division to obtain coefficient matrixes of respectively corresponding sensitive region matrix mapping graphs, and recording the coefficient matrixes as A_RFor A_RThe value of the coefficient at the middle coordinate position (i, j), which is denoted as A_R(i,j)，

Wherein, 0 is not less than i not more than (W-8), 0 is not less than j not more than (H-8), W represents L_org、L_dis、R_orgAnd R_disH denotes L_org、L_dis、R_orgAnd R_disHigh of (d);

③ mixing L_orgAnd L_disThe 2 images were divided into (W-7) × (H-7) overlapping blocks of size 8 × 8, respectively, and then L was calculated_orgAnd L_disThe structural amplitude distortion mapping maps of two overlapped blocks with the same coordinate positions in 2 images are recorded as B in the coefficient matrix of the structural amplitude distortion mapping maps_LFor B_LThe value of the coefficient at the middle coordinate position (i, j), which is denoted as B_L(i,j)， $B_L(i,j)=\frac{2\&times;{\mathrm{\&sigma;}}_{\mathrm{org},\mathrm{dis},L}(i,j)+C_1}{{\left({\mathrm{\&sigma;}}_{\mathrm{org},L}(i,j)\right)}^2+{\left({\mathrm{\&sigma;}}_{\mathrm{dis},L}(i,j)\right)}^2+C_1},$ Wherein, B_L(i, j) also denotes L_orgOverlapping block L of size 8 × 8 with middle upper left coordinate position (i, j)_disThe upper-left coordinate position is the structural amplitude distortion value of the overlapped block with the size of 8 × 8 (i, j), ${\mathrm{\&sigma;}}_{\mathrm{org},L}(i,j)=\sqrt{\frac{1}{64}\underset{x=0}{\overset{7}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{7}{\mathrm{\&Sigma;}}}{(L_{\mathrm{org}}(i+x,j+y)-U_{\mathrm{org},L}(i,j))}^2},$ $U_{\mathrm{org},L}(i,j)=\frac{1}{64}\underset{x=0}{\overset{7}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{7}{\mathrm{\&Sigma;}}}{L}_{\mathrm{org}}(i+x,j+y),$ ${\mathrm{\&sigma;}}_{\mathrm{dis},L}(i,j)=\sqrt{\frac{1}{64}\underset{x=0}{\overset{7}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{7}{\mathrm{\&Sigma;}}}{(L_{\mathrm{dis}}(i+x,j+y)-U_{\mathrm{dis},L}(i,j))}^2},$ $U_{\mathrm{dis},L}(i,j)=\frac{1}{64}\underset{x=0}{\overset{7}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{7}{\mathrm{\&Sigma;}}}{L}_{\mathrm{dis}}(i+x,j+y),$ ${\mathrm{\&sigma;}}_{\mathrm{org},\mathrm{dis},L}(i,j)=\frac{1}{64}\underset{x=0}{\overset{7}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{7}{\mathrm{\&Sigma;}}}((L_{\mathrm{org}}(i+x,j+y)-U_{\mathrm{org},L}(i,j))\&times;(L_{\mathrm{dis}}(i+x,j+y)-U_{\mathrm{dis},L}(i,j))),$ L_org(i + x, j + y) denotes L_orgThe pixel value of the pixel point with the middle coordinate position of (i + x, j + y), L_dis(i + x, j + y) denotes L_disThe pixel value C of the pixel point with the middle coordinate position of (i + x, j + y)₁Represents a constant, where 0. ltoreq. i.ltoreq.W-8, 0. ltoreq. j.ltoreq.H-8;

r is to be_orgAnd R_disThe 2 images were each divided into (W-7) × (H-7) overlapping blocks of size 8 × 8, and R was calculated_orgAnd R_disThe structural amplitude distortion mapping maps of two overlapped blocks with the same coordinate positions in 2 images are recorded as B in the coefficient matrix of the structural amplitude distortion mapping maps_RFor B_RThe value of the coefficient at the middle coordinate position (i, j), which is denoted as B_R(i,j)， $B_R(i,j)=\frac{2\&times;{\mathrm{\&sigma;}}_{\mathrm{org},\mathrm{dis},R}(i,j)+C_1}{{\left({\mathrm{\&sigma;}}_{\mathrm{org},R}(i,j)\right)}^2+{\left({\mathrm{\&sigma;}}_{\mathrm{dis},R}(i,j)\right)}^2+C_1},$ Wherein, B_R(i, j) also represents R_orgThe size of the overlapped block with the upper left corner coordinate position of (i, j) being 8 × 8 and R_disThe upper-left coordinate position is the structural amplitude distortion value of the overlapped block with the size of 8 × 8 (i, j), ${\mathrm{\&sigma;}}_{\mathrm{org},R}(i,j)=\sqrt{\frac{1}{64}\underset{x=0}{\overset{7}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{7}{\mathrm{\&Sigma;}}}{(R_{\mathrm{org}}(i+x,j+y)-U_{\mathrm{org},R}(i,j))}^2},$ $U_{\mathrm{org},R}(i,j)=\frac{1}{64}\underset{x=0}{\overset{7}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{7}{\mathrm{\&Sigma;}}}{R}_{\mathrm{org}}(i+x,j+y),$ ${\mathrm{\&sigma;}}_{\mathrm{dis},R}(i,j)=\sqrt{\frac{1}{64}\underset{x=0}{\overset{7}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{7}{\mathrm{\&Sigma;}}}{(R_{\mathrm{dis}}(i+x,j+y)-U_{\mathrm{dis},R}(i,j))}^2},$ $U_{\mathrm{dis},R}(i,j)=\frac{1}{64}\underset{x=0}{\overset{7}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{7}{\mathrm{\&Sigma;}}}{R}_{\mathrm{dis}}(i+x,j+y),$ ${\mathrm{\&sigma;}}_{\mathrm{org},\mathrm{dis},R}(i,j)=\frac{1}{64}\underset{x=0}{\overset{7}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{7}{\mathrm{\&Sigma;}}}((R_{\mathrm{org}}(i+x,j+y)-U_{\mathrm{org},R}(i,j))\&times;(R_{\mathrm{dis}}(i+x,j+y)-U_{\mathrm{dis},R}(i,j))),$ R_org(i + x, j + y) represents R_orgThe pixel value R of the pixel point with the middle coordinate position of (i + x, j + y)_dis(i + x, j + y) represents R_disThe pixel value C of the pixel point with the middle coordinate position of (i + x, j + y)₁Represents a constant, where 0. ltoreq. i.ltoreq.W-8, 0. ltoreq. j.ltoreq.H-8;

④ pairs L_orgAnd L_disRespectively carrying out Sobel operator processing in the horizontal direction and Sobel operator processing in the vertical direction on the 2 images to respectively obtain L_orgAnd L_disL corresponding to the horizontal direction gradient matrix mapping map and the vertical direction gradient matrix mapping map of each of the 2 images_orgThe coefficient matrix of the corresponding horizontal gradient matrix mapping graph obtained after horizontal Sobel operator processing is carried out is marked as I_h，org，LFor I_h，org,LThe value of the coefficient at the middle coordinate position (I, j), which is denoted as I_h，org，L(i,j)， $\begin{array}{c}{I}_{h,\mathrm{org},L}(i,j)=L_{\mathrm{org}}(i+2,j)+2L_{\mathrm{org}}(i+2,j+1)+L_{\mathrm{org}}(i+2,j+2)\\ -L_{\mathrm{org}}(i,j)-2L_{\mathrm{org}}(i,j+1)-L_{\mathrm{org}}(i,j+2)\end{array},$ L will be mixed_orgThe coefficient matrix of the corresponding vertical gradient matrix mapping graph obtained after the vertical Sobel operator is implemented is marked as I_v，org，LFor I_v，org,LThe value of the coefficient at the middle coordinate position (I, j), which is denoted as I_v，org,L(i,j)， $\begin{array}{c}{I}_{v,\mathrm{org},L}(i,j)=L_{\mathrm{org}}(i,j+2)+2L_{\mathrm{org}}(i+1,j+2)+L_{\mathrm{org}}(i+2,j+2)\\ -L_{\mathrm{org}}(i,j)-2L_{\mathrm{org}}(i+1,j)-L_{\mathrm{org}}(i+2,j)\end{array},$ L will be mixed_disThe coefficient matrix of the corresponding horizontal gradient matrix mapping graph obtained after horizontal Sobel operator processing is carried out is marked as I_h，dis，LFor I_h，dis，LThe value of the coefficient at the middle coordinate position (I, j), which is denoted as I_h，dis,L(i,j)， $\begin{array}{c}{I}_{h,\mathrm{dis},L}(i,j)=L_{\mathrm{dis}}(i+2,j)+2L_{\mathrm{dis}}(i+2,j+1)+L_{\mathrm{dis}}(i+2,j+2)\\ -L_{\mathrm{dis}}(i,j)-2L_{\mathrm{dis}}(i,j+1)-L_{\mathrm{dis}}(i,j+2)\end{array},$ L will be mixed_disThe coefficient matrix of the corresponding vertical gradient matrix mapping graph obtained after the vertical Sobel operator is implemented is marked as I_v，dis，LFor I_v，dis，LThe value of the coefficient at the middle coordinate position (I, j), which is denoted as I_v，dis，L(i,j)， $\begin{array}{c}{I}_{v,\mathrm{dis},L}(i,j)=L_{\mathrm{dis}}(i,j+2)+2L_{\mathrm{dis}}(i+1,j+2)+L_{\mathrm{dis}}(i+2,j+2)\\ -L_{\mathrm{dis}}(i,j)-2L_{\mathrm{dis}}(i+1,j)-L_{\mathrm{dis}}(i+2,j)\end{array},$ Wherein, L_org(i+2,j)、L_org(i+2,j+1)、L_org(i+2,j+2)、L_org(i,j)、L_org(i,j+1)、L_org(i,j+2)、L_org(i+1,j+2)、L_org(i +1, j) are respectively corresponded to L_orgThe pixel values of the pixel points with the middle coordinate positions of (i +2, j), (i +2, j +1), (i +2, j +2), (i, j +1), (i, j +2), (i +1, j +2), and (i +1, j), L_dis(i+2,j)、L_dis(i+2,j+1)、L_dis(i+2,j+2)、L_dis(i,j)、L_dis(i,j+1)、L_dis(i,j+2)、L_dis(i+1,j+2)、L_dis(i +1, j) are respectively corresponded to L_disThe middle coordinate position is the pixel value of the pixel point of (i +2, j), (i +2, j +1), (i +2, j +2), (i, j +1), (i, j +2), (i +1, j +2), or (i +1, j);

to R_orgAnd R_disRespectively carrying out Sobel operator processing in the horizontal direction and the vertical direction on the 2 images to respectively obtain R_orgAnd R_dis2 imagesA horizontal direction gradient matrix map and a vertical direction gradient matrix map corresponding to each other, and R is_orgThe coefficient matrix of the corresponding horizontal gradient matrix mapping graph obtained after horizontal Sobel operator processing is carried out is marked as I_h，org,RFor I_h，org,RThe value of the coefficient at the middle coordinate position (I, j), which is denoted as I_h，org，R(i,j)， $\begin{array}{c}{I}_{h,\mathrm{org},R}(i,j)=R_{\mathrm{org}}(i+2,j)+2R_{\mathrm{org}}(i+2,j+1)+R_{\mathrm{org}}(i+2,j+2)\\ -R_{\mathrm{org}}(i,j)-2R_{\mathrm{org}}(i,j+1)-R_{\mathrm{org}}(i,j+2)\end{array},$ R is to be_orgThe coefficient matrix of the corresponding vertical gradient matrix mapping graph obtained after the vertical Sobel operator is implemented is marked as I_v，org，RFor I_v，org，RThe value of the coefficient at the middle coordinate position (I, j), which is denoted as I_v，org,R(i,j)， $\begin{array}{c}{I}_{v,\mathrm{org},R}(i,j)=R_{\mathrm{org}}(i,j+2)+2R_{\mathrm{org}}(i+1,j+2)+R_{\mathrm{org}}(i+2,j+2)\\ -R_{\mathrm{org}}(i,j)-2R_{\mathrm{org}}(i+1,j)-R_{\mathrm{org}}(i+2,j)\end{array},$ R is to be_disThe coefficient matrix of the corresponding horizontal gradient matrix mapping graph obtained after horizontal Sobel operator processing is carried out is marked as I_h，dis，RFor I_h，dis，RThe value of the coefficient at the middle coordinate position (I, j), which is denoted as I_h，dis,R(i,j)， $\begin{array}{c}{I}_{h,\mathrm{dis},R}(i,j)=R_{\mathrm{dis}}(i+2,j)+2R_{\mathrm{dis}}(i+2,j+1)+R_{\mathrm{dis}}(i+2,j+2)\\ -R_{\mathrm{dis}}(i,j)-2R_{\mathrm{dis}}(i,j+1)-R_{\mathrm{dis}}(i,j+2)\end{array},$ R is to be_disThe coefficient matrix of the corresponding vertical gradient matrix mapping graph obtained after the vertical Sobel operator is implemented is marked as I_v，dis，RFor I_v，dis，RThe value of the coefficient at the middle coordinate position (I, j), which is denoted as I_v，dis,R(i,j)， $\begin{array}{c}{I}_{v,\mathrm{dis},R}(i,j)=R_{\mathrm{dis}}(i,j+2)+2R_{\mathrm{dis}}(i+1,j+2)+R_{\mathrm{dis}}(i+2,j+2)\\ -R_{\mathrm{dis}}(i,j)-2R_{\mathrm{dis}}(i+1,j)-R_{\mathrm{dis}}(i+2,j)\end{array},$ Wherein R is_org(i+2,j)、R_org(i+2,j+1)、R_org(i+2,j+2)、R_org(i,j)、R_org(i,j+1)、R_org(i,j+2)、R_org(i+1,j+2)、R_org(i +1, j) each represents R_orgThe middle coordinate positions are (i +2, j), (i +2, j +1), (i +2, j +2), (i, j +1), (i, j +2), (i +1, j +2) and (i)Pixel value of a +1, j) pixel point, R_dis(i+2,j)、R_dis(i+2,j+1)、R_dis(i+2,j+2)、R_dis(i,j)、R_dis(i,j+1)、R_dis(i,j+2)、R_dis(i+1,j+2)、R_dis(i +1, j) each represents R_disThe middle coordinate position is the pixel value of the pixel point of (i +2, j), (i +2, j +1), (i +2, j +2), (i, j +1), (i, j +2), (i +1, j +2), or (i +1, j);

⑤ calculation L_orgAnd L_disThe structural direction distortion mapping maps of two overlapped blocks with the same coordinate positions in 2 images are recorded as E in the coefficient matrix of the structural direction distortion mapping maps_LFor E_LThe value of the coefficient at the middle coordinate position (i, j), denoted as E_L(i,j)， $E_L(i,j)=\frac{I_{h,\mathrm{org},L}(i,j)\&times;I_{h,\mathrm{dis},L}(i,j)+I_{v,\mathrm{org},L}(i,j)\&times;I_{v,\mathrm{dis},L}(i,j)+C_2}{\sqrt{{\left(I_{h,\mathrm{org},L}(i,j)\right)}^2+{\left(I_{v,\mathrm{org},L}(i,j)\right)}^2}\&times;\sqrt{{\left(I_{h,\mathrm{dis},L}(i,j)\right)}^2+{\left(I_{v,\mathrm{dis},L}(i,j)\right)}^2}+C_2},$ Wherein, C₂Represents a constant;

calculation of R_orgAnd R_disThe structural direction distortion mapping maps of two overlapped blocks with the same coordinate positions in 2 images are recorded as E in the coefficient matrix of the structural direction distortion mapping maps_RFor E_RThe value of the coefficient at the middle coordinate position (i, j), denoted as E_R(i,j)， $E_R(i,j)=\frac{I_{h,\mathrm{org},R}(i,j)\&times;I_{h,\mathrm{dis},R}(i,j)+I_{v,\mathrm{org},R}(i,j)\&times;I_{v,\mathrm{dis},R}(i,j)+C_2}{\sqrt{{\left(I_{h,\mathrm{org},R}(i,j)\right)}^2+{\left(I_{v,\mathrm{org},R}(i,j)\right)}^2}\&times;\sqrt{{\left(I_{h,\mathrm{dis},R}(i,j)\right)}^2+{\left(I_{v,\mathrm{dis},R}(i,j)\right)}^2}+C_2};$

⑥ calculation L_orgAnd L_disStructural distortion evaluation value of (1), denoted as Q^L，Q^L=ω₁×Q_m，L+ω₂×Q_nm，L， $Q_{m,L}=\frac{1}{N_{L,m}}\underset{i=0}{\overset{W-8}{\mathrm{\&Sigma;}}}\underset{j=0}{\overset{H-8}{\mathrm{\&Sigma;}}}(0.5\&times;(B_L(i,j)+E_L(i,j))\&times;A_L(i,j)),$ $N_{L,m}=\underset{i=0}{\overset{W-8}{\mathrm{\&Sigma;}}}\underset{j=0}{\overset{H-8}{\mathrm{\&Sigma;}}}{A}_L(i,j),$ $Q_{\mathrm{nm},L}=\frac{1}{N_{L,\mathrm{nm}}}\underset{i=0}{\overset{W-8}{\mathrm{\&Sigma;}}}\underset{j=0}{\overset{H-8}{\mathrm{\&Sigma;}}}(0.5\&times;(B_L(i,j)+E_L(i,j))\&times;(1-A_L(i,j))),$ $N_{L,\mathrm{nm}}=\underset{i=0}{\overset{W-8}{\mathrm{\&Sigma;}}}\underset{j=0}{\overset{H-8}{\mathrm{\&Sigma;}}}(1-A_L(i,j)),$ Wherein, ω is₁Representation L_orgAnd L_disWeight value of middle sensitive area, omega₂Representation L_orgAnd L_disThe weight value of the middle non-sensitive area;

calculation of R_orgAnd R_disStructural distortion evaluation value of (1), denoted as Q^R，Q^R=ω′₁×Q_m，R+ω′₂×Q_nm，R， $Q_{m,R}=\frac{1}{N_{R,m}}\underset{i=0}{\overset{W-8}{\mathrm{\&Sigma;}}}\underset{j=0}{\overset{H-8}{\mathrm{\&Sigma;}}}(0.5\&times;(B_R(i,j)+E_R(i,j))\&times;A_R(i,j)),$ $N_{R,m}=\underset{i=0}{\overset{W-8}{\mathrm{\&Sigma;}}}\underset{j=0}{\overset{H-8}{\mathrm{\&Sigma;}}}{A}_R(i,j),$ $Q_{\mathrm{nm},R}=\frac{1}{N_{R,\mathrm{nm}}}\underset{i=0}{\overset{W-8}{\mathrm{\&Sigma;}}}\underset{j=0}{\overset{H-8}{\mathrm{\&Sigma;}}}(0.5\&times;(B_R(i,j)+E_R(i,j))\&times;(1-A_R(i,j))),$ $N_{R,\mathrm{nm}}=\underset{i=0}{\overset{W-8}{\mathrm{\&Sigma;}}}\underset{j=0}{\overset{H-8}{\mathrm{\&Sigma;}}}(1-A_R(i,j)),$ Wherein, omega'₁Represents R_orgAnd R_disWeight value of Medium sensitive region, ω'₂Represents R_orgAnd R_disThe weight value of the middle non-sensitive area;

⑦ according to Q^LAnd Q^RCalculating a distorted stereo image S to be evaluated_disRelative to the original undistorted stereo image S_orgThe spatial frequency similarity measure of (2), denoted as Q_F，Q_F=β₁×Q^L+(1-β₁)×Q^RWherein, β₁Represents Q^LThe weight of (2);

⑧ calculation L_orgAnd R_orgIs expressed in matrix form as

Calculation L_disAnd R_disIs expressed in matrix form as

Wherein, "|" is an absolute value symbol;

⑨ will be

And

the 2 images are respectively divided into

A non-overlapping block of size 8 × 8, and then

And

all the blocks in the 2 images are respectively subjected to singular value decomposition to obtain

Corresponding singular value map composed of singular value matrix of each block thereof and

a corresponding singular value mapping map composed of the singular value matrix of each block thereof is to

The coefficient matrix of the singular value mapping graph obtained after singular value decomposition is recorded as G_orgFor G_orgThe coordinate position in the singular value matrix of the nth block is the singular value at (p, q), which is recorded as

Will be provided with

The coefficient matrix of the singular value mapping graph obtained after singular value decomposition is recorded as G_disFor G_disThe coordinate position in the singular value matrix of the nth block is the singular value at (p, q),it is marked as

Wherein, W_LRTo represent

And

width of (H)_LRTo represent

And

is high in the direction of the horizontal axis, $0\&le;n\&le;\frac{W_{\mathrm{LR}}\&times;H_{\mathrm{LR}}}{8\&times;8}-1,$ 0≤p≤7,0≤q≤7；

⑩ calculation

Corresponding singular value map and

the singular value deviation evaluation value of the corresponding singular value map, denoted as K, $K=\frac{64}{W_{\mathrm{LR}}\&times;H_{\mathrm{LR}}}\&times;\underset{n=0}{\overset{W_{\mathrm{LR}}\&times;H_{\mathrm{LR}}/64-1}{\mathrm{\&Sigma;}}}\frac{\underset{p=0}{\overset{7}{\mathrm{\&Sigma;}}}(G_{\mathrm{org}}^n(p,p)\&times;|G_{\mathrm{org}}^n(p,p)-G_{\mathrm{dis}}^n(p,p)\left|\right)}{\underset{p=0}{\overset{7}{\mathrm{\&Sigma;}}}{G}_{\mathrm{org}}^n(p,p)},$ wherein the content of the first and second substances,

represents G_orgThe coordinate position in the singular value matrix of the nth block is the singular value at (p, p),

represents G_disThe coordinate position in the singular value matrix of the nth block is a singular value at (p, p);

to pair

And

respectively carrying out singular value decomposition to respectively obtain

And

2 orthogonal matrixes and 1 singular value matrix corresponding to each other are used for generating a plurality of singular value matrixes

2 orthogonal matrixes obtained after singular value decomposition are respectively recorded as chi_orgAnd V_orgWill be

The singular value matrix obtained after singular value decomposition is recorded as O_org，

Will be provided with

2 orthogonal matrixes obtained after singular value decomposition are respectively recorded as chi_disAnd V_disWill be

The singular value matrix obtained after singular value decomposition is recorded as O_dis，

Respectively calculate

And

2 residual matrix images of the images after the singular value deprivation

The residual matrix image after the singular value deprivation is marked as X_org，X_org=χ_org×Λ×V_orgWill beThe residual matrix image after the singular value deprivation is marked as X_dis，X_dis=χ_dis×Λ×V_disWhere Λ denotes the identity matrix, Λ size and O_orgAnd O_disThe sizes are consistent;

calculating X_orgAnd X_disThe mean deviation ratio of (D) is recorded as

Wherein X represents X_orgAnd X_disThe abscissa of the pixel point in (1) and y represents X_orgAnd X_disThe ordinate of the pixel point in (1);

calculating a distorted stereo image S to be evaluated_disRelative to the original undistorted stereo image S_orgThe stereo perception evaluation metric of (1), denoted as Q_S，

Wherein τ represents a constant for adjusting K and

at Q_SThe importance of (1);

according to Q_FAnd Q_SCalculating a distorted stereoscopic image S to be evaluated_disThe image quality evaluation score of (1), denoted as Q, Q = Q_F×（Q_S）^ρWhere ρ represents a weight coefficient value.

2. The method for objective quality assessment of stereo images based on structural distortion as claimed in claim 1, wherein L of said step ②_orgAnd L_disCoefficient matrix A of the respective corresponding sensitive area matrix map_LThe acquisition process comprises the following steps:

② -a1, pair L_orgProcessing with Sobel operator in horizontal and vertical directions to obtain L_orgRespectively, as Z, in the horizontal direction and in the vertical direction_h，l1And Z_v，l1Then calculate L_orgIs marked as Z_l1， $Z_{l1}(x,y)=\sqrt{{\left(Z_{h,l1}(x,y)\right)}^2+{\left(Z_{v,l1}(x,y)\right)}^2},$ Wherein Z is_l1(x, y) represents Z_l1Gradient amplitude, Z, of pixel point with (x, y) as middle coordinate position_h，l1(x, y) represents Z_h，l1The horizontal gradient value Z of the pixel point with the (x, y) middle coordinate position_v，l1(x, y) represents Z_v，l1The gradient value in the vertical direction of the pixel point with the middle coordinate position (x, y) is that x is more than or equal to 1 and less than or equal to W ', y is more than or equal to 1 and less than or equal to H ', wherein W ' represents Z_l1Width of (A), H' represents Z_l1High of (d);

② -a2, pair L_disProcessing with Sobel operator in horizontal and vertical directions to obtain L_disRespectively, as Z, in the horizontal direction and in the vertical direction_h，l2And Z_v，l2Then calculateL_disIs marked as Z_l2， $Z_{l2}(x,y)=\sqrt{{\left(Z_{h,l2}(x,y)\right)}^2+{\left(Z_{v,l2}(x,y)\right)}^2},$ Wherein Z is_l2(x, y) represents Z_l2Gradient amplitude, Z, of pixel point with (x, y) as middle coordinate position_h，l2(x, y) represents Z_h，l2The horizontal gradient value Z of the pixel point with the (x, y) middle coordinate position_v，l2(x, y) represents Z_v，l2The gradient value in the vertical direction of the pixel point with the middle coordinate position (x, y) is that x is more than or equal to 1 and less than or equal to W ', y is more than or equal to 1 and less than or equal to H ', wherein W ' represents Z_l2Width of (A), H' represents Z_l2High of (d);

② -a3, calculating the threshold value T required when dividing the region, $T=\mathrm{\&alpha;}\&times;\frac{1}{W^{\&prime;}\&times;H^{\&prime;}}\&times;(\underset{x=0}{\overset{W^{\&prime;}}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{H^{\&prime;}}{\mathrm{\&Sigma;}}}{Z}_{l1}(x,y)+\underset{x=0}{\overset{W^{\&prime;}}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{H^{\&prime;}}{\mathrm{\&Sigma;}}}{Z}_{l2}(x,y)),$ wherein α is a constant, Z_l1(x, y) represents Z_l1Gradient amplitude, Z, of pixel point with (x, y) as middle coordinate position_l2(x, y) represents Z_l2The gradient amplitude of the pixel point with the middle coordinate position (x, y);

② -a4, mixing Z with_l1Ladder with pixel points of (i, j) as middle coordinate positionsThe amplitude of the degree is recorded as Z_l1(i, j) reacting Z_l2The gradient amplitude of the pixel point with the middle coordinate position (i, j) is recorded as Z_l2(i, j), judgment of Z_l1(i,j)>T or Z_l2(i,j)>If T is true, then a determination L is made_orgAnd L_disThe pixel point with the middle coordinate position (i, j) belongs to the sensitive area, and order A_L(i, j) =1, otherwise, determination L_orgAnd L_disThe pixel point with the middle coordinate position (i, j) belongs to the non-sensitive area, and order A_L(i, j) =0, wherein i is more than or equal to 0 and is less than or equal to (W-8), and j is more than or equal to 0 and is less than or equal to (H-8);

in the step ②, R_orgAnd R_disCoefficient matrix A of the respective corresponding sensitive area matrix map_RThe acquisition process comprises the following steps:

② -b1, p.R_orgPerforming Sobel operator processing in horizontal and vertical directions to obtain R_orgRespectively, as Z, in the horizontal direction and in the vertical direction_h，r1And Z_v，r1Then calculating R_orgIs marked as Z_r1， $Z_{r1}(x,y)=\sqrt{{\left(Z_{h,r1}(x,y)\right)}^2+{\left(Z_{v,r1}(x,y)\right)}^2},$ Wherein Z is_r1(x, y) represents Z_r1Gradient amplitude, Z, of pixel point with (x, y) as middle coordinate position_h，r1(x, y) represents Z_h，r1The horizontal gradient value Z of the pixel point with the (x, y) middle coordinate position_v，r1(x, y) represents Z_v，r1The gradient value in the vertical direction of the pixel point with the middle coordinate position (x, y) is that x is more than or equal to 1 and less than or equal to W ', y is more than or equal to 1 and less than or equal to H ', wherein W ' represents Z_r1Width of (A), H' represents Z_r1High of (d);

② -b2, p.R_disPerforming Sobel operator processing in horizontal and vertical directions to obtain R_disRespectively, as Z, in the horizontal direction and in the vertical direction_h，r2And Z_v,r2Then calculating R_disIs marked as Z_r2， $Z_{r2}(x,y)=\sqrt{{\left(Z_{h,r2}(x,y)\right)}^2+{\left(Z_{v,r2}(x,y)\right)}^2},$ Wherein Z is_r2(x, y) represents Z_r2Gradient amplitude, Z, of pixel point with (x, y) as middle coordinate position_h，r2(x, y) represents Z_h，r2The horizontal gradient value Z of the pixel point with the (x, y) middle coordinate position_v，r2(x, y) represents Z_v，r2The gradient value in the vertical direction of the pixel point with the middle coordinate position (x, y) is that x is more than or equal to 1 and less than or equal to W ', y is more than or equal to 1 and less than or equal to H ', wherein W ' represents Z_r2Width of (A), H' represents Z_r2High of (d);

② -b3, calculating the threshold value T' required when dividing the region, $T^{\&prime;}=\mathrm{\&alpha;}\&times;\frac{1}{W^{\&prime;}\&times;H^{\&prime;}}\&times;(\underset{x=0}{\overset{W^{\&prime;}}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{H^{\&prime;}}{\mathrm{\&Sigma;}}}{Z}_{r1}(x,y)+\underset{x=0}{\overset{W^{\&prime;}}{\mathrm{\&Sigma;}}}\underset{y=0}{\overset{H^{\&prime;}}{\mathrm{\&Sigma;}}}{Z}_{r2}(x,y)),$ wherein α is a constant, Z_r1(x, y) represents Z_r1Gradient amplitude, Z, of pixel point with (x, y) as middle coordinate position_r2(x, y) represents Z_r2The gradient amplitude of the pixel point with the middle coordinate position (x, y);

② -b4, mixing Z with_r1The gradient amplitude of the pixel point with the middle coordinate position (i, j) is recorded as Z_r1(i, j) reacting Z_r2The gradient amplitude of the pixel point with the middle coordinate position (i, j) is recorded as Z_r2(i, j), judgment of Z_r1(i,j)>T or Z_r2(i,j)>Whether T is true, and if so, determining R_orgAnd R_disThe pixel point with the middle coordinate position (i, j) belongs to the sensitive area, and order A_R(i, j) =1, otherwise, determine R_orgAnd R_disThe pixel point with the middle coordinate position (i, j) belongs to the non-sensitive area, and order A_R(i, j) =0, wherein i is more than or equal to 0 and is less than or equal to (W-8), and j is more than or equal to 0 and is less than or equal to (H-8).

3. The method for objective quality assessment of stereo images based on structural distortion as claimed in claim 1 or 2, wherein β of said step ⑦₁The acquisition process comprises the following steps:

⑦ -1, adopting n undistorted stereo images to establish a distorted stereo image set under different distortion types and different distortion degrees, wherein the distorted stereo image set comprises a plurality of distorted stereo images, and n is more than or equal to 1;

⑦ -2, obtaining an average subjective score difference of each distorted stereo image in the distorted stereo image set by using a subjective quality evaluation method, and recording the average subjective score difference as DMOS, wherein DMOS =100-MOS, wherein MOS represents the subjective score mean, DMOS ∈ [0,100 ];

⑦ -3, according to the operation procedures of step ① to step ⑥, an evaluation value Q of a sensitive area of a left viewpoint image of each distorted stereoscopic image in the distorted stereoscopic image set with respect to a left viewpoint image of a corresponding undistorted stereoscopic image is calculated_m,LAnd evaluation value Q of non-sensitive region_nm，L；

⑦ -4, fitting the mean subjective score difference DMOS and corresponding Q of distorted stereo images in the set of distorted stereo images by a mathematical fitting method_m，LAnd Q_nm，LThereby obtaining β₁The value is obtained.

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