CN103903259A - Objective three-dimensional image quality evaluation method based on structure and texture separation - Google Patents

Abstract

The invention discloses a method for objectively evaluating the quality of stereoscopic images based on structural texture separation. Firstly, the left viewpoint image and the right viewpoint image of the original undistorted stereoscopic image, the left viewpoint image and the right viewpoint image of the distorted stereoscopic image to be evaluated are respectively The structure and texture of the right view image are separated to obtain their respective structure images and texture images, and then the gradient similarity is used to evaluate the structure images of the left view image and the right view image respectively, and the structure similarity is used to evaluate the left view image and the right view image respectively. The texture image is evaluated, and the image quality prediction value of the distorted stereo image to be evaluated is obtained through fusion; the advantage is that the structure image and texture image obtained by decomposition can well represent the influence of image structure and texture information on image quality , so that the evaluation results are more in line with the human visual system, thus effectively improving the correlation between the objective evaluation results and subjective perception.

Description

An Objective Evaluation Method of Stereo Image Quality Based on Structural-Texture Separation

technical field

The invention relates to an image quality evaluation method, in particular to an objective evaluation method of stereoscopic image quality based on structure texture separation.

Background technique

With the rapid development of image coding technology and stereoscopic display technology, stereoscopic image technology has received more and more attention and applications, and has become a current research hotspot. Stereoscopic image technology utilizes the principle of binocular parallax of the human eye. Both eyes independently receive left and right viewpoint images from the same scene, and form binocular parallax through brain fusion, so as to enjoy stereoscopic images with a sense of depth and realism. . Due to the influence of acquisition system, storage compression and transmission equipment, stereoscopic images will inevitably introduce a series of distortions. Compared with single-channel images, stereoscopic images need to ensure the image quality of two channels at the same time, so the quality of Evaluation is very important. However, there is currently no effective objective evaluation method to evaluate the stereoscopic image quality. Therefore, it is of great significance to establish an effective objective evaluation model for stereoscopic image quality.

The current objective evaluation method of stereoscopic image quality is to directly apply the planar image quality evaluation method to evaluate the quality of stereoscopic images, or to evaluate the depth perception of stereoscopic images by evaluating the quality of disparity maps. However, the process of fusion of stereoscopic images to produce stereoscopic It is not an extension of the simple planar image quality evaluation method, and the human eye does not directly view the disparity map, so it is not very accurate to evaluate the depth perception of the stereoscopic image with the quality of the disparity map. Therefore, how to effectively simulate the process of binocular stereo perception in the process of stereo image quality evaluation, and how to analyze the influence mechanism of different distortion types on the quality of stereo perception, so that the evaluation results can more objectively reflect the human visual system, are all important. It is a problem that needs to be studied and solved in the process of objective quality evaluation of stereoscopic images.

Contents of the invention

The technical problem to be solved by the present invention is to provide an objective evaluation method of stereoscopic image quality based on structure texture separation that can effectively improve the correlation between objective evaluation results and subjective perception.

The technical solution adopted by the present invention to solve the above-mentioned technical problems is: a method for objectively evaluating the quality of stereoscopic images based on structural texture separation, which is characterized in that its processing process is:

First, implement structure texture separation on the left viewpoint image and the right viewpoint image of the original undistorted stereoscopic image, the left viewpoint image and the right viewpoint image of the distorted stereoscopic image to be evaluated respectively, and obtain respective structural images and texture images;

Secondly, by calculating the gradient similarity between each pixel in the structural image of the left viewpoint image of the original undistorted stereoscopic image and the corresponding pixel in the structural image of the left viewpoint image of the distorted stereoscopic image to be evaluated, Obtain the image quality objective evaluation prediction value of the structural image of the left viewpoint image of the distorted stereoscopic image to be evaluated; similarly, by calculating the difference between each pixel in the structural image of the right viewpoint image of the original undistorted stereoscopic image and to be evaluated The gradient similarity between corresponding pixels in the structural image of the right viewpoint image of the distorted stereoscopic image is obtained, and the image quality objective evaluation prediction value of the structural image of the right viewpoint image of the distorted stereoscopic image to be evaluated is obtained;

Next, by calculating each sub-block with a size of 8×8 in the texture image of the left viewpoint image of the original undistorted stereoscopic image and the corresponding size in the texture image of the left viewpoint image of the distorted stereoscopic image to be evaluated is the structural similarity between 8×8 sub-blocks, and obtain the image quality objective evaluation prediction value of the texture image of the left viewpoint image of the distorted stereo image to be evaluated; similarly, by calculating the right Structural similarity between each 8×8 sub-block in the texture image of the viewpoint image and the corresponding 8×8 sub-block in the texture image of the right viewpoint image of the distorted stereo image to be evaluated , obtaining the image quality objective evaluation prediction value of the texture image of the right viewpoint image of the distorted stereoscopic image to be evaluated;

Furthermore, the image quality objective evaluation prediction value of the structural image of the left viewpoint image and the right viewpoint image of the distorted stereoscopic image to be evaluated is fused to obtain the image quality objective evaluation prediction value of the structural image of the distorted stereoscopic image to be evaluated; Similarly, the image quality objective evaluation prediction value of the texture image of the left viewpoint image and the right viewpoint image of the distorted stereoscopic image to be evaluated is fused to obtain the image quality objective evaluation prediction value of the texture image of the distorted stereoscopic image to be evaluated;

Finally, the image quality objective evaluation prediction value of the structure image and the texture image of the distorted stereo image to be evaluated are fused to obtain the image quality objective evaluation prediction value of the distorted stereo image to be evaluated.

The stereoscopic image quality objective evaluation method based on structure texture separation of the present invention specifically comprises the following steps:

①Let S _org denote the original undistorted stereo image, let S _dis denote the distorted stereo image to be evaluated, denote the left viewpoint image of S _org as {L _org (x,y)}, and denote the right viewpoint image of S _org The image is recorded as {R _org (x,y)}, the left view image of S _dis is recorded as {L _dis (x,y)}, and the right view image of S _dis is recorded as {R _dis (x,y)} , where (x, y) represents the coordinate position of the pixel in the left-viewpoint image and the right-viewpoint image, 1≤x≤W, 1≤y≤H, W represents the width of the left-viewpoint image and the right-viewpoint image, and H represents The height of the left view image and the right view image, L _org (x, y) represents the pixel value of the pixel whose coordinate position is (x, y) in {L _org (x, y)}, R _org (x, y) Indicates the pixel value of the pixel whose coordinate position is (x, y) in {R _org (x, y)}, and L _dis (x, y) indicates that the coordinate position in {L _dis (x, y)} is (x, y) The pixel value of the pixel point of y), R _dis (x, y) represents the pixel value of the pixel point whose coordinate position is (x, y) in {R _dis (x, y)};

将{R_org(x,y)}的结构图像和纹理图像对应记为

和

将{L_dis(x,y)}的结构图像和纹理图像对应记为

和

将{Rdis(x,y)}的结构图像和纹理图像对应记为

和

其中，

表示

中坐标位置为(x,y)的像素点的像素值，

表示

中坐标位置为(x,y)的像素点的像素值，

表示

中坐标位置为(x,y)的像素点的像素值，

表示

中坐标位置为(x,y)的像素点的像素值，

表示

中坐标位置为(x,y)的像素点的像素值，

表示

中坐标位置为(x,y)的像素点的像素值，

表示

中坐标位置为(x,y)的像素点的像素值，

表示中坐标位置为(x,y)的像素点的像素值；② Implement structure and texture separation for {L _org (x, y)}, {R _org (x, y)}, {L _dis (x, y)} and {R _dis (x, y)} respectively, and obtain {L _org (x,y)}, {R _org (x,y)}, {L _dis (x,y)} and {R _dis (x,y)} respectively structure image and texture image, the {L _org ( The corresponding structure image and texture image of x, y)} are recorded as and

The structure image and texture image correspondence of {R _org (x,y)} are recorded as

and

The structure image and texture image of {L _dis (x,y)} are recorded as

and

Record the structure image and texture image correspondence of {Rdis(x,y)} as

and

in,

express

The pixel value of the pixel whose coordinate position is (x, y),

express

The pixel value of the pixel whose coordinate position is (x, y),

express

The pixel value of the pixel whose coordinate position is (x, y),

express

The pixel value of the pixel whose coordinate position is (x, y),

express

The pixel value of the pixel whose coordinate position is (x, y),

express

The pixel value of the pixel whose coordinate position is (x, y),

express

The pixel value of the pixel whose coordinate position is (x, y),

express The pixel value of the pixel point whose middle coordinate position is (x, y);

中的每个像素点与

中对应像素点之间的梯度相似性，将中坐标位置为(x,y)的像素点与

中坐标位置为(x,y)的像素点之间的梯度相似性记为

$Q_L^{\mathrm{str}}(x,y)=\frac{2\×m_{L,\mathrm{org}}^{\mathrm{str}}(x,y)\×m_{L,\mathrm{dis}}^{\mathrm{str}}(x,y)+C_1}{{\left(m_{L,\mathrm{org}}^{\mathrm{str}}(x,y)\right)}^2+{\left(m_{L,\mathrm{dis}}^{\mathrm{str}}(x,y)\right)}^2+C_1},$ 其中， $m_{L,\mathrm{org}}^{\mathrm{str}}(x,y)=\sqrt{{\left({\mathrm{gx}}_{L,\mathrm{org}}^{\mathrm{str}}(x,y)\right)}^2+{\left({\mathrm{gy}}_{L,\mathrm{org}}^{\mathrm{str}}(x,y)\right)}^2},$ $m_{L,\mathrm{dis}}^{\mathrm{str}}(x,y)=\sqrt{{\left({\mathrm{gx}}_{L,\mathrm{dis}}^{\mathrm{str}}(x,y)\right)}^2+{\left({\mathrm{gy}}_{L,\mathrm{dis}}^{\mathrm{str}}(x,y)\right)}^2},$

表示

中坐标位置为(x,y)的像素点的水平方向梯度，表示

中坐标位置为(x,y)的像素点的垂直方向梯度，

表示

中坐标位置为(x,y)的像素点的水平方向梯度，

表示

中坐标位置为(x,y)的像素点的垂直方向梯度，C₁为控制参数；然后根据

中的每个像素点与

中对应像素点之间的梯度相似性，计算

的图像质量客观评价预测值，记为

③ calculation

Each pixel in

The gradient similarity between the corresponding pixels in the The pixel point with the middle coordinate position (x, y) and

The gradient similarity between pixels whose coordinate positions are (x, y) is recorded as

$Q_L^{\mathrm{str}}(x,\mathrm{the\; y})=\frac{2\×m_{L,\mathrm{org}}^{\mathrm{str}}(x,\mathrm{the\; y})\×m_{L,\mathrm{dis}}^{\mathrm{str}}(x,\mathrm{the\; y})+C_1}{{\left(m_{L,\mathrm{org}}^{\mathrm{str}}(x,\mathrm{the\; y})\right)}^2+{\left(m_{L,\mathrm{dis}}^{\mathrm{str}}(x,\mathrm{the\; y})\right)}^2+C_1},$ in, $m_{L,\mathrm{org}}^{\mathrm{str}}(x,\mathrm{the\; y})=\sqrt{{\left({\mathrm{gx}}_{L,\mathrm{org}}^{\mathrm{str}}(x,\mathrm{the\; y})\right)}^2+{\left({\mathrm{gy}}_{L,\mathrm{org}}^{\mathrm{str}}(x,\mathrm{the\; y})\right)}^2},$ $m_{L,\mathrm{dis}}^{\mathrm{str}}(x,\mathrm{the\; y})=\sqrt{{\left({\mathrm{gx}}_{L,\mathrm{dis}}^{\mathrm{str}}(x,\mathrm{the\; y})\right)}^2+{\left({\mathrm{gy}}_{L,\mathrm{dis}}^{\mathrm{str}}(x,\mathrm{the\; y})\right)}^2},$

express

The horizontal direction gradient of the pixel point whose middle coordinate position is (x, y), express

The vertical direction gradient of the pixel point whose middle coordinate position is (x, y),

express

The horizontal direction gradient of the pixel point whose middle coordinate position is (x, y),

express

The vertical direction gradient of the pixel point whose middle coordinate position is (x, y), C ₁ is the control parameter; then according to

Each pixel in

The gradient similarity between the corresponding pixels in the calculation

The predicted value of the objective evaluation of image quality is denoted as

中的每个像素点与

性，将

中坐标位置为(x,y)的像素点与

中坐标位置为(x,y)的像素点之间的梯度相似性记为 $Q_R^{\mathrm{str}}(x,y)=\frac{2\×m_{R,\mathrm{org}}^{\mathrm{str}}(x,y)\×m_{R,\mathrm{dis}}^{\mathrm{str}}(x,y)+C_1}{{\left(m_{R,\mathrm{org}}^{\mathrm{str}}(x,y)\right)}^2+{\left(m_{R,\mathrm{dis}}^{\mathrm{str}}(x,y)\right)}^2+C_1},$ 其中， $m_{R,\mathrm{org}}^{\mathrm{str}}(x,y)=\sqrt{{\left({\mathrm{gx}}_{R,\mathrm{org}}^{\mathrm{str}}(x,y)\right)}^2+{\left({\mathrm{gy}}_{R,\mathrm{org}}^{\mathrm{str}}(x,y)\right)}^2},$ $m_{R,\mathrm{dis}}^{\mathrm{str}}(x,y)=\sqrt{{\left({\mathrm{gx}}_{R,\mathrm{dis}}^{\mathrm{str}}(x,y)\right)}^2+{\left({\mathrm{gy}}_{R,\mathrm{dis}}^{\mathrm{str}}(x,y)\right)}^2},$

表示

中坐标位置为(x,y)的像素点的水平方向梯度，

表示

中坐标位置为(x,y)的像素点的垂直方向梯度，

表示

中坐标位置为(x,y)的像素点的水平方向梯度，表示

中坐标位置为(x,y)的像素点的垂直方向梯度，C₁为控制参数；然后根据

中的每个像素点与

中对应像素点之间的梯度相似性，计算

的图像质量客观评价预测值，记为

Similarly, calculate

Each pixel in

sex, will

The pixel point with the middle coordinate position (x, y) and

The gradient similarity between pixels whose coordinate positions are (x, y) is recorded as $Q_R^{\mathrm{str}}(x,\mathrm{the\; y})=\frac{2\×m_{R,\mathrm{org}}^{\mathrm{str}}(x,\mathrm{the\; y})\×m_{R,\mathrm{dis}}^{\mathrm{str}}(x,\mathrm{the\; y})+C_1}{{\left(m_{R,\mathrm{org}}^{\mathrm{str}}(x,\mathrm{the\; y})\right)}^2+{\left(m_{R,\mathrm{dis}}^{\mathrm{str}}(x,\mathrm{the\; y})\right)}^2+C_1},$ in, $m_{R,\mathrm{org}}^{\mathrm{str}}(x,\mathrm{the\; y})=\sqrt{{\left({\mathrm{gx}}_{R,\mathrm{org}}^{\mathrm{str}}(x,\mathrm{the\; y})\right)}^2+{\left({\mathrm{gy}}_{R,\mathrm{org}}^{\mathrm{str}}(x,\mathrm{the\; y})\right)}^2},$ $m_{R,\mathrm{dis}}^{\mathrm{str}}(x,\mathrm{the\; y})=\sqrt{{\left({\mathrm{gx}}_{R,\mathrm{dis}}^{\mathrm{str}}(x,\mathrm{the\; y})\right)}^2+{\left({\mathrm{gy}}_{R,\mathrm{dis}}^{\mathrm{str}}(x,\mathrm{the\; y})\right)}^2},$

express

The horizontal direction gradient of the pixel point whose middle coordinate position is (x, y),

express

The vertical direction gradient of the pixel point whose middle coordinate position is (x, y),

express

The horizontal direction gradient of the pixel point whose middle coordinate position is (x, y), express

The vertical direction gradient of the pixel point whose middle coordinate position is (x, y), C ₁ is the control parameter; then according to

Each pixel in

The gradient similarity between the corresponding pixels in the calculation

The predicted value of the objective evaluation of image quality is denoted as

中的每个尺寸大小为8×8的子块与

中对应尺寸大小为8×8的子块之间的结构相似度，计算得到

的图像质量客观评价预测值，记为 ④ By obtaining

Each sub-block of size 8×8 in

The structural similarity between the sub-blocks corresponding to the size of 8×8 in , is calculated as

The predicted value of the objective evaluation of image quality is denoted as

中的每个尺寸大小为8×8的子块与

中对应尺寸大小为8×8的子块之间的结构相似度，计算得到的图像质量客观评价预测值，记为 Likewise, by getting

Each sub-block of size 8×8 in

The structural similarity between the sub-blocks corresponding to the size of 8×8 in , is calculated as The predicted value of the objective evaluation of image quality is denoted as

和

进行融合，得到S_dis的结构图像的图像质量客观评价预测值，记为Q_str，

其中，w_s表示

和

的权值比重；⑤ right

and

Fusion is carried out to obtain the image quality objective evaluation prediction value of the structural image of S _dis , denoted as Q _str ,

Among them, w _s means

and

weight ratio;

和

进行融合，得到S_dis的纹理图像的图像质量客观评价预测值，记为Q_tex，

其中，w_t表示

和

的权值比重；same, yes

and

Perform fusion to obtain the image quality objective evaluation prediction value of the texture image of S _dis , denoted as Q _tex ,

Among them, w _t means

and

weight ratio;

⑥Fuse Q _str and Q _tex to obtain the predicted value of S _dis image quality objective evaluation, denoted as Q, Q=w×Q _str +(1-w)×Q _tex , where w represents Q _str and S _dis weight ratio of .

和纹理图像

的获取过程为：The structural image of {L _org (x,y)} in the step ②

and the texture image

The acquisition process is:

②-1a. Define the current pixel to be processed in {L _org (x, y)} as the current pixel;

将以当前像素点为中心的21×21邻域窗口内的每个邻域像素点为中心的9×9邻域窗口构成的块均定义为邻域子块，将在以当前像素点为中心的21×21邻域窗口内的且以在{L_org(x,y)}中坐标位置为q的邻域像素点为中心的9×9邻域窗口构成的邻域子块记为其中，p∈Ω，q∈Ω，在此Ω表示{L_org(x,y)}中的所有像素点的坐标位置的集合，(x₂,y₂)表示当前子块

中的像素点在当前子块

中的坐标位置，1≤x₂≤9,1≤y₂≤9，

表示当前子块

坐标位置为(x₂,y₂)的像素点的像素值，(x₃,y₃)表示

中的像素点在

中的坐标位置，1≤x₃≤9,1≤y₃≤9，

表示

中坐标位置为(x₃,y₃)的像素点的像素值；②-2a. Record the coordinate position of the current pixel point in {L _org (x,y)} as p, and record each pixel in the 21×21 neighborhood window centered on the current pixel point except the current pixel point A point is defined as a neighborhood pixel point, and a block composed of a 9×9 neighborhood window centered on the current pixel point is defined as the current sub-block, and is recorded as

A block composed of a 9×9 neighborhood window centered on each neighborhood pixel in a 21×21 neighborhood window centered on the current pixel is defined as a neighborhood sub-block, and will be centered on the current pixel The neighborhood sub-block composed of a 9×9 neighborhood window centered on the neighborhood pixel point whose coordinate position is q in {L _org (x,y)} within the 21×21 neighborhood window of is denoted as Among them, p∈Ω, q∈Ω, where Ω represents the set of coordinate positions of all pixels in {L _org (x,y)}, and (x ₂ ,y ₂ ) represents the current sub-block

The pixels in the current sub-block

Coordinate position in , 1≤x ₂ ≤9, 1≤y ₂ ≤9,

represents the current subblock

The pixel value of the pixel point whose coordinate position is (x ₂ , y ₂ ), (x ₃ , y ₃ ) means

The pixels in

Coordinate position in , 1≤x ₃ ≤9, 1≤y ₃ ≤9,

express

The pixel value of the pixel point whose middle coordinate position is (x ₃ , y ₃ );

In the above step ②-2a, for any pixel in the neighborhood or any pixel in the current sub-block, assuming that the coordinate position of the pixel in {L _org (x,y)} is (x,y), If x<1 and 1≤y≤H, assign the pixel value of the pixel whose coordinate position is (1,y) in {L _org (x,y)} to the pixel; if x>W and 1≤ y≤H, then assign the pixel value of the pixel whose coordinate position is (W,y) in {L _org (x,y)} to the pixel; if 1≤x≤W and y<1, then { The pixel value of the pixel whose coordinate position is (x, 1) in L _org (x, y)} is assigned to the pixel; if 1≤x≤W and y>H, then {L _org (x,y) } assigns the pixel value of the pixel whose coordinate position is (x, H) to the pixel; if x<1 and y<1, then the coordinate position in {L _org (x,y)} is (1,1 ) is assigned the pixel value of the pixel point; if x>W and y<1, then assign the pixel value of the pixel point whose coordinate position is (W,1) in {L _org (x,y)} to The pixel; if x<1 and y>H, assign the pixel value of the pixel whose coordinate position is (1,H) in {L _org (x,y)} to the pixel; if x>W and y>H, then assign the pixel value of the pixel whose coordinate position is (W, H) in {L _org (x, y)} to the pixel;

中的每个像素点的特征矢量，将当前子块

中坐标位置为(x2,y2)的像素点的特征矢量记为

$X_{L,\mathrm{org}}^p(x_2,y_2)=[I_{L,\mathrm{org}}^p(x_2,y_2),|\frac{{\&PartialD;I}_{L,\mathrm{org}}^p(x_2,y_2)}{\&PartialD;x}|,|\frac{{\&PartialD;I}_{L,\mathrm{org}}^p(x_2,y_2)}{\&PartialD;y}|,|\frac{{\&PartialD;}^2{I}_{L,\mathrm{org}}^p(x_2,y_2)}{{\&PartialD;x}^2}|,|\frac{{\&PartialD;}^2{I}_{L,\mathrm{org}}^p(x_2,y_2)}{{\&PartialD;y}^2}|,x_2,y_2]$ ，其中，

的维数为7，符号“[]”为矢量表示符号，符号“||”为绝对值符号，

表示当前子块

中坐标位置为(x₂,y₂)的像素点的密度值，

为

在水平方向的一阶偏导数，

为

在垂直方向的一阶偏导数，

为

在水平方向的二阶偏导数，

为

在垂直方向的二阶偏导数；②-3a. Get the current sub-block

The feature vector of each pixel in the current sub-block

The feature vector of the pixel point whose coordinate position is (x2, y2) is recorded as

$x_{L,\mathrm{org}}^p(x_2,{\mathrm{the\; y}}_2)=[I_{L,\mathrm{org}}^p(x_2,{\mathrm{the\; y}}_2),|\frac{{\&PartialD;I}_{L,\mathrm{org}}^p(x_2,{\mathrm{the\; y}}_2)}{\&PartialD;x}|,|\frac{{\&PartialD;I}_{L,\mathrm{org}}^p(x_2,{\mathrm{the\; y}}_2)}{\&PartialD;\mathrm{the\; y}}|,|\frac{{\&PartialD;}^2{I}_{L,\mathrm{org}}^p(x_2,{\mathrm{the\; y}}_2)}{{\&PartialD;x}^2}|,|\frac{{\&PartialD;}^2{I}_{L,\mathrm{org}}^p(x_2,{\mathrm{the\; y}}_2)}{{\&PartialD;\mathrm{the\; y}}^2}|,x_2,{\mathrm{the\; y}}_2]$ ,in,

The dimension is 7, the symbol “[]” is a vector representation symbol, and the symbol “||” is an absolute value symbol,

represents the current subblock

The density value of the pixel point whose coordinate position is (x ₂ , y ₂ ),

for

The first partial derivative in the horizontal direction,

for

The first partial derivative in the vertical direction,

for

The second partial derivative in the horizontal direction,

for

The second partial derivative in the vertical direction;

中的每个像素点的特征矢量，计算当前子块

的协方差矩阵，记为 $C_{L,\mathrm{org}}^p=\frac{1}{7\&times;7-1}\underset{x_2=1}{\overset{9}{\mathrm{\&Sigma;}}}\underset{y_2=1}{\overset{9}{\mathrm{\&Sigma;}}}(X_{L,\mathrm{org}}^p(x_2,y_2)-{\mathrm{\&mu;}}_{L,\mathrm{org}}^p){(X_{L,\mathrm{org}}^p(x_2,y_2)-{\mathrm{\&mu;}}_{L,\mathrm{org}}^p)}^T,$ 其中，的维数为7×7，表示当前子块中的所有像素点的特征矢量的均值矢量，

为

的转置矢量；②-4a, according to the current sub-block

The feature vector of each pixel in the calculation of the current sub-block

The covariance matrix of is denoted as $C_{L,\mathrm{org}}^p=\frac{1}{7\&times;7-1}\underset{x_2=1}{\overset{9}{\mathrm{\&Sigma;}}}\underset{{\mathrm{the\; y}}_2=1}{\overset{9}{\mathrm{\&Sigma;}}}(x_{L,\mathrm{org}}^p(x_2,{\mathrm{the\; y}}_2)-{\mathrm{\&mu;}}_{L,\mathrm{org}}^p){(x_{L,\mathrm{org}}^p(x_2,{\mathrm{the\; y}}_2)-{\mathrm{\&mu;}}_{L,\mathrm{org}}^p)}^T,$ in, The dimension of is 7×7, represents the current subblock The mean vector of the feature vectors of all pixels in

for

The transpose vector;

的协方差矩阵

进行Cholesky分解，得到当前子块

的Sigma特征集，记为 $S_{L,\mathrm{org}}^p=[\sqrt{10}\&times;L^{\left(1\right)},...,\sqrt{10}\&times;L^{(i\&prime;)},...,\sqrt{10}\&times;L^{\left(7\right)},-\sqrt{10}\&times;L^{\left(1\right)},...,-\sqrt{10}\&times;L^{(i\&prime;)},...,-\sqrt{10}\&times;L^{\left(7\right)},{\mathrm{\&mu;}}_{L,\mathrm{org}}^p],$ 其中，L^T为L的转置矩阵，

的维数为7×15，符号“[]”为矢量表示符号，此处1≤i'≤7，L⁽¹⁾表示L的第1列向量，L^(i')表示L的第i'列向量，L⁽⁷⁾表示L的第7列向量；②-5a. For the current sub-block

The covariance matrix of

Perform Cholesky decomposition, get the current subblock

The Sigma feature set of is denoted as $S_{L,\mathrm{org}}^p=[\sqrt{10}\&times;L^{\left(1\right)},...,\sqrt{10}\&times;L^{(i\&prime;)},...,\sqrt{10}\&times;L^{\left(7\right)},-\sqrt{10}\&times;L^{\left(1\right)},...,-\sqrt{10}\&times;L^{(i\&prime;)},...,-\sqrt{10}\&times;L^{\left(7\right)},{\mathrm{\&mu;}}_{L,\mathrm{org}}^p],$ Among them, L ^T is the transpose matrix of L,

The dimension of is 7×15, and the symbol "[]" is a vector representation symbol, where 1≤i'≤7, L ⁽¹⁾ represents the first column vector of L, and L ^(i') represents the i'th column of L Column vector, L ⁽⁷⁾ represents the 7th column vector of L;

的维数为7×15；②-6a. Use the same operation as step ②-3a to step ②-5a to obtain the Sigma feature set of the neighborhood sub-block composed of a 9×9 neighborhood window centered on each neighborhood pixel, and set The Sigma feature set is denoted as

The dimension of is 7×15;

②-7a, according to the current sub-block The Sigma feature set and the Sigma feature set of the neighborhood sub-block composed of a 9×9 neighborhood window centered on each neighborhood pixel to obtain the structural information of the current pixel, denoted as $I_{L,\mathrm{org}}^{\mathrm{str}}\left(p\right),I_{L,\mathrm{org}}^{\mathrm{str}}\left(p\right)=\frac{\underset{q\&Element;N\&prime;\left(p\right)}{\mathrm{\&Sigma;}}\exp (-\frac{{\left|\right|S_{L,\mathrm{org}}^p-S_{L,\mathrm{org}}^q\left|\right|}^2}{{2\mathrm{\&sigma;}}^2})\&times;L_{\mathrm{org}}\left(q\right)}{\underset{q\&Element;N\&prime;\left(p\right)}{\mathrm{\&Sigma;}}\exp (-\frac{{\left|\right|S_{L,\mathrm{org}}^p-S_{L,\mathrm{org}}^q\left|\right|}^2}{{2\mathrm{\&sigma;}}^2})},$ Among them, N'(p) represents the coordinates of all neighboring pixels in the 21×21 neighborhood window centered on the current pixel in {Lorg(x,y)} in { _Lorg (x,y)} A collection of positions, exp() means the exponential function with e as the base, e=2.71828183, σ means the standard deviation of the Gaussian function, the symbol "||||" is the Euclidean distance calculation symbol, L _org (q) means {L The pixel value of the pixel whose coordinate position is q in _org (x, y)};

获取当前像素点的纹理信息，记为 $I_{L,\mathrm{org}}^{\mathrm{tex}}\left(p\right),I_{L,\mathrm{org}}^{\mathrm{tex}}\left(p\right)=L_{\mathrm{org}}\left(p\right)-I_{L,\mathrm{org}}^{\mathrm{str}}\left(p\right),$ 其中，L_org(p)表示当前像素点的像素值；②-8a. According to the structure information of the current pixel point

Get the texture information of the current pixel point, denoted as $I_{L,\mathrm{org}}^{\mathrm{tex}}\left(p\right),I_{L,\mathrm{org}}^{\mathrm{tex}}\left(p\right)=L_{\mathrm{org}}\left(p\right)-I_{L,\mathrm{org}}^{\mathrm{str}}\left(p\right),$ Wherein, L _org (p) represents the pixel value of the current pixel point;

由{L_org(x,y)}中的所有像素点的纹理信息构成{L_org(x,y)}的纹理图像，记为

②-9a. Set the next pixel to be processed in {L _org (x,y)} as the current pixel, and then return to step ②-2a to continue until all pixels in {L _org (x,y)} Points are processed, and the structure information and texture information of each pixel in {L _org (x, y)} are obtained, which is composed of the structure information of all pixels in {L _org (x, y)} {L _org ( The structural image of x,y)}, denoted as

The texture image of {L _org (x, y)} is composed of the texture information of all pixels in {L _org (x, y)}, recorded as

和纹理图像相同的操作，获取{R_org(x,y)}的结构图像

和纹理图像

{L_dis(x,y)}的结构图像和纹理图像

{R_dis(x,y)}的结构图像

和纹理图像

Obtain the structure image of {L _org (x, y)} with step ②-1a to step ②-9a

and the texture image The same operation, get the structure image of {R _org (x,y)}

and the texture image

Structural image of {L _dis (x,y)} and the texture image

Structural image of {R _dis (x,y)}

and the texture image

的图像质量客观评价预测值

的获取过程为：In the step ④

The predictive value of the image quality objective evaluation

The acquisition process is:

和

划分成

个互不重叠的尺寸大小为8×8的子块，将

中当前待处理的第k个子块定义为当前第一子块，将中当前待处理的第k个子块定义为当前第二子块，其中，

k的初始值为1；④-1a, respectively

and

divided into

Non-overlapping sub-blocks of size 8×8, the

The kth sub-block currently to be processed in is defined as the current first sub-block, and the The kth sub-block currently to be processed in is defined as the current second sub-block, where,

The initial value of k is 1;

将当前第二子块记为

其中，(x₄,y₄)表示

和

中的像素点的坐标位置，1≤x₄≤8,1≤y₄≤8，

表示

中坐标位置为(x₄,y₄)的像素点的像素值，表示

中坐标位置为(x₄,y₄)的像素点的像素值；④-2a. Record the current first sub-block as

Record the current second sub-block as

Among them, (x ₄ ,y ₄ ) means

and

The coordinate position of the pixel in , 1≤x ₄ ≤8, 1≤y ₄ ≤8,

express

The pixel value of the pixel point whose middle coordinate position is (x ₄ , y ₄ ), express

The pixel value of the pixel point whose middle coordinate position is (x ₄ , y ₄ );

和 ${\mathrm{\&mu;}}_{L_{\mathrm{org}},k}=\frac{\underset{y_4=1}{\overset{8}{\mathrm{\&Sigma;}}}\underset{x_4=1}{\overset{8}{\mathrm{\&Sigma;}}}{f}_{L_{\mathrm{org}},k}(x_4,y_4)}{64},{\mathrm{\&sigma;}}_{L_{\mathrm{org}},k}=\sqrt{\frac{\underset{y_4=1}{\overset{8}{\mathrm{\&Sigma;}}}\underset{x_4=1}{\overset{8}{\mathrm{\&Sigma;}}}{(f_{L_{\mathrm{org}},k}(x_4,y_4)-{\mathrm{\&mu;}}_{L_{\mathrm{org}},k})}^2}{64}};$ ④-3a. Calculate the current first sub-block The mean and standard deviation of , corresponding to

and ${\mathrm{\&mu;}}_{L_{\mathrm{org}},k}=\frac{\underset{{\mathrm{the\; y}}_4=1}{\overset{8}{\mathrm{\&Sigma;}}}\underset{x_4=1}{\overset{8}{\mathrm{\&Sigma;}}}{f}_{L_{\mathrm{org}},k}(x_4,{\mathrm{the\; y}}_4)}{64},{\mathrm{\&sigma;}}_{L_{\mathrm{org}},k}=\sqrt{\frac{\underset{{\mathrm{the\; y}}_4=1}{\overset{8}{\mathrm{\&Sigma;}}}\underset{x_4=1}{\overset{8}{\mathrm{\&Sigma;}}}{(f_{L_{\mathrm{org}},k}(x_4,{\mathrm{the\; y}}_4)-{\mathrm{\&mu;}}_{L_{\mathrm{org}},k})}^2}{64}};$

的均值和标准差，对应记为

和

${\mathrm{\&mu;}}_{L_{\mathrm{dis}},k}=\frac{\underset{y_4=1}{\overset{8}{\mathrm{\&Sigma;}}}\underset{x_4=1}{\overset{8}{\mathrm{\&Sigma;}}}{f}_{L_{\mathrm{dis}},k}(x_4,y_4)}{64},{\mathrm{\&sigma;}}_{L_{\mathrm{dis}},k}=\sqrt{\frac{\underset{y_4=1}{\overset{8}{\mathrm{\&Sigma;}}}\underset{x_4=1}{\overset{8}{\mathrm{\&Sigma;}}}{(f_{L_{\mathrm{dis}},k}(x_4,y_4)-{\mathrm{\&mu;}}_{L_{\mathrm{dis}},k})}^2}{64}};$ Similarly, calculate the current second sub-block

The mean and standard deviation of , corresponding to

and

${\mathrm{\&mu;}}_{L_{\mathrm{dis}},k}=\frac{\underset{{\mathrm{the\; y}}_4=1}{\overset{8}{\mathrm{\&Sigma;}}}\underset{x_4=1}{\overset{8}{\mathrm{\&Sigma;}}}{f}_{L_{\mathrm{dis}},k}(x_4,{\mathrm{the\; y}}_4)}{64},{\mathrm{\&sigma;}}_{L_{\mathrm{dis}},k}=\sqrt{\frac{\underset{{\mathrm{the\; y}}_4=1}{\overset{8}{\mathrm{\&Sigma;}}}\underset{x_4=1}{\overset{8}{\mathrm{\&Sigma;}}}{(f_{L_{\mathrm{dis}},k}(x_4,{\mathrm{the\; y}}_4)-{\mathrm{\&mu;}}_{L_{\mathrm{dis}},k})}^2}{64}};$

之间的结构相似度，记为

$Q_{L,k}^{\mathrm{tex}}=\frac{4\&times;({\mathrm{\&sigma;}}_{L_{\mathrm{org}},k}\&times;{\mathrm{\&sigma;}}_{L_{\mathrm{dis}},k})\&times;({\mathrm{\&mu;}}_{L_{\mathrm{org}},k}\&times;{\mathrm{\&mu;}}_{L_{\mathrm{dis}},k})+C_2}{({\left({\mathrm{\&sigma;}}_{L_{\mathrm{org}},k}\right)}^2+{\left({\mathrm{\&sigma;}}_{L_{\mathrm{dis}},k}\right)}^2)+({\left({\mathrm{\&mu;}}_{L_{\mathrm{org}},k}\right)}^2+{\left({\mathrm{\&mu;}}_{L_{\mathrm{dis}},k}\right)}^2)+C_2},$ 其中，C₂为控制参数；④-4a. Calculate the current first sub-block with the current second subblock

The structural similarity between

$Q_{L,k}^{\mathrm{tex}}=\frac{4\&times;({\mathrm{\&sigma;}}_{L_{\mathrm{org}},k}\&times;{\mathrm{\&sigma;}}_{L_{\mathrm{dis}},k})\&times;({\mathrm{\&mu;}}_{L_{\mathrm{org}},k}\&times;{\mathrm{\&mu;}}_{L_{\mathrm{dis}},k})+C_2}{({\left({\mathrm{\&sigma;}}_{L_{\mathrm{org}},k}\right)}^2+{\left({\mathrm{\&sigma;}}_{L_{\mathrm{dis}},k}\right)}^2)+({\left({\mathrm{\&mu;}}_{L_{\mathrm{org}},k}\right)}^2+{\left({\mathrm{\&mu;}}_{L_{\mathrm{dis}},k}\right)}^2)+C_2},$ Wherein, C ₂ is a control parameter;

中下一个待处理的子块作为当前第一子块，将

中下一个待处理的子块作为当前第二子块，然后返回步骤④-2a继续执行，直至

和

中的所有子块均处理完毕，得到

中的每个子块与

中对应子块之间的结构相似度，其中，k=k+1中的“=”为赋值符号；④-5a, let k=k+1, the

In the next sub-block to be processed as the current first sub-block, the

The next sub-block to be processed is used as the current second sub-block, and then returns to step ④-2a to continue until

and

All sub-blocks in are processed, resulting in

Each subblock in

The structural similarity between corresponding sub-blocks in k=k+1, where "=" is an assignment symbol;

中的每个子块与

中对应子块之间的结构相似度，计算

的图像质量客观评价预测值，记为

④-6a, according to

Each subblock in

The structural similarity between the corresponding sub-blocks in the calculation

The predicted value of objective evaluation of image quality is denoted as

的图像质量客观评价预测值

的获取过程为：In the step ④

The predictive value of the image quality objective evaluation

The acquisition process is:

和划分成

个互不重叠的尺寸大小为8×8的子块，将

中当前待处理的第k个子块定义为当前第一子块，将

中当前待处理的第k个子块定义为当前第二子块，其中，

k的初始值为1；④-1b, respectively

and divided into

Non-overlapping sub-blocks of size 8×8, the

The kth sub-block currently to be processed in is defined as the current first sub-block, and the

The kth sub-block currently to be processed in is defined as the current second sub-block, where,

The initial value of k is 1;

将当前第二子块记为

其中，(x₄,y₄)表示和

中的像素点的坐标位置，1≤x₄≤8,1≤y₄≤8，

表示

中坐标位置为(x₄,y₄)的像素点的像素值，

表示中坐标位置为(x₄,y₄)的像素点的像素值；④-2b. Record the current first sub-block as

Record the current second sub-block as

Among them, (x ₄ ,y ₄ ) means and

The coordinate position of the pixel in , 1≤x ₄ ≤8, 1≤y ₄ ≤8,

express

The pixel value of the pixel point whose middle coordinate position is (x ₄ , y ₄ ),

express The pixel value of the pixel point whose middle coordinate position is (x ₄ , y ₄ );

的均值和标准差，对应记为

和

${\mathrm{\&mu;}}_{R_{\mathrm{org}},k}=\frac{\underset{y_4=1}{\overset{8}{\mathrm{\&Sigma;}}}\underset{x_4=1}{\overset{8}{\mathrm{\&Sigma;}}}{f}_{R_{\mathrm{org}},k}(x_4,y_4)}{64},{\mathrm{\&sigma;}}_{R_{\mathrm{org}},k}=\sqrt{\frac{\underset{y_4=1}{\overset{8}{\mathrm{\&Sigma;}}}\underset{x_4=1}{\overset{8}{\mathrm{\&Sigma;}}}{(f_{R_{\mathrm{org}},k}(x_4,y_4)-{\mathrm{\&mu;}}_{R_{\mathrm{org}},k})}^2}{64}};$ ④-3b. Calculate the current first sub-block

The mean and standard deviation of , corresponding to

and

${\mathrm{\&mu;}}_{R_{\mathrm{org}},k}=\frac{\underset{{\mathrm{the\; y}}_4=1}{\overset{8}{\mathrm{\&Sigma;}}}\underset{x_4=1}{\overset{8}{\mathrm{\&Sigma;}}}{f}_{R_{\mathrm{org}},k}(x_4,{\mathrm{the\; y}}_4)}{64},{\mathrm{\&sigma;}}_{R_{\mathrm{org}},k}=\sqrt{\frac{\underset{{\mathrm{the\; y}}_4=1}{\overset{8}{\mathrm{\&Sigma;}}}\underset{x_4=1}{\overset{8}{\mathrm{\&Sigma;}}}{(f_{R_{\mathrm{org}},k}(x_4,{\mathrm{the\; y}}_4)-{\mathrm{\&mu;}}_{R_{\mathrm{org}},k})}^2}{64}};$

的均值和标准差，对应记为

和

${\mathrm{\&mu;}}_{R_{\mathrm{dis}},k}=\frac{\underset{y_4=1}{\overset{8}{\mathrm{\&Sigma;}}}\underset{x_4=1}{\overset{8}{\mathrm{\&Sigma;}}}{f}_{R_{\mathrm{dis}},k}(x_4,y_4)}{64},{\mathrm{\&sigma;}}_{R_{\mathrm{dis}},k}=\sqrt{\frac{\underset{y_4=1}{\overset{8}{\mathrm{\&Sigma;}}}\underset{x_4=1}{\overset{8}{\mathrm{\&Sigma;}}}{(f_{R_{\mathrm{dis}},k}(x_4,y_4)-{\mathrm{\&mu;}}_{R_{\mathrm{dis}},k})}^2}{64}};$ Similarly, calculate the current second sub-block

The mean and standard deviation of , corresponding to

and

${\mathrm{\&mu;}}_{R_{\mathrm{dis}},k}=\frac{\underset{{\mathrm{the\; y}}_4=1}{\overset{8}{\mathrm{\&Sigma;}}}\underset{x_4=1}{\overset{8}{\mathrm{\&Sigma;}}}{f}_{R_{\mathrm{dis}},k}(x_4,{\mathrm{the\; y}}_4)}{64},{\mathrm{\&sigma;}}_{R_{\mathrm{dis}},k}=\sqrt{\frac{\underset{{\mathrm{the\; y}}_4=1}{\overset{8}{\mathrm{\&Sigma;}}}\underset{x_4=1}{\overset{8}{\mathrm{\&Sigma;}}}{(f_{R_{\mathrm{dis}},k}(x_4,{\mathrm{the\; y}}_4)-{\mathrm{\&mu;}}_{R_{\mathrm{dis}},k})}^2}{64}};$

与当前第二子块

之间的结构相似度，记为 $Q_{R,k}^{\mathrm{tex}}=\frac{4\&times;({\mathrm{\&sigma;}}_{R_{\mathrm{org}},k}\&times;{\mathrm{\&sigma;}}_{R_{\mathrm{dis}},k})\&times;({\mathrm{\&mu;}}_{R_{\mathrm{org}},k}\&times;{\mathrm{\&mu;}}_{R_{\mathrm{dis}},k})+C_2}{({\left({\mathrm{\&sigma;}}_{R_{\mathrm{org}}k}\right)}^2+{\left({\mathrm{\&sigma;}}_{R_{\mathrm{dis}},k}\right)}^2)+({\left({\mathrm{\&mu;}}_{R_{\mathrm{org}},k}\right)}^2+{\left({\mathrm{\&mu;}}_{R_{\mathrm{dis}},k}\right)}^2)+C_2},$ 其中，C₂为控制参数；④-4b. Calculate the current first sub-block

with the current second subblock

The structural similarity between $Q_{R,k}^{\mathrm{tex}}=\frac{4\&times;({\mathrm{\&sigma;}}_{R_{\mathrm{org}},k}\&times;{\mathrm{\&sigma;}}_{R_{\mathrm{dis}},k})\&times;({\mathrm{\&mu;}}_{R_{\mathrm{org}},k}\&times;{\mathrm{\&mu;}}_{R_{\mathrm{dis}},k})+C_2}{({\left({\mathrm{\&sigma;}}_{R_{\mathrm{org}}k}\right)}^2+{\left({\mathrm{\&sigma;}}_{R_{\mathrm{dis}},k}\right)}^2)+({\left({\mathrm{\&mu;}}_{R_{\mathrm{org}},k}\right)}^2+{\left({\mathrm{\&mu;}}_{R_{\mathrm{dis}},k}\right)}^2)+C_2},$ Wherein, C ₂ is a control parameter;

中下一个待处理的子块作为当前第一子块，将

中下一个待处理的子块作为当前第二子块，然后返回步骤④-2b继续执行，直至

和

中的所有子块均处理完毕，得到

中的每个子块与

中对应子块之间的结构相似度，其中，k=k+1中的“=”为赋值符号；④-5b, let k=k+1, the

In the next sub-block to be processed as the current first sub-block, the

The next sub-block to be processed is used as the current second sub-block, and then returns to step ④-2b to continue until

and

All sub-blocks in are processed, resulting in

Each subblock in

The structural similarity between corresponding sub-blocks in k=k+1, where "=" is an assignment symbol;

中的每个子块与

中对应子块之间的结构相似度，计算

的图像质量客观评价预测值，记为

④-6b, according to

Each subblock in

The structural similarity between the corresponding sub-blocks in the calculation

The predicted value of the objective evaluation of image quality is denoted as

Compared with the prior art, the present invention has the advantages of:

1) The method of the present invention considers that distortion will lead to the loss of image structure or texture information, so the distorted stereoscopic image is separated into a structural image and a texture image, and different parameters are used to separate the structural images and texture images of the left viewpoint image and the right viewpoint image. The image quality objective evaluation prediction value of the texture image is fused, which can better reflect the quality change of the stereo image and make the evaluation result more in line with the human visual system.

2) The method of the present invention uses gradient similarity to evaluate structural images, and uses structural similarity to evaluate texture images, which can well characterize the impact of loss of structure and texture information on image quality, thereby effectively improving objective evaluation Correlation of results to subjective perception.

Description of drawings

Fig. 1 is the overall realization block diagram of the inventive method;

Fig. 2 is the scatter diagram of the image quality objective evaluation prediction value and the average subjective score difference of each distorted stereoscopic image in the Ningbo University stereoscopic image database obtained by the inventive method;

Fig. 3 is a scatter diagram of the difference between the image quality objective evaluation prediction value and the average subjective evaluation value of each distorted stereo image in the LIVE stereo image database obtained by the method of the present invention.

Detailed ways

The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

A kind of stereoscopic image quality objective evaluation method based on structural texture separation that the present invention proposes, its overall realization block diagram is as shown in Figure 1, and its processing process is:

First, implement structure texture separation on the left viewpoint image and the right viewpoint image of the original undistorted stereoscopic image, the left viewpoint image and the right viewpoint image of the distorted stereoscopic image to be evaluated respectively, and obtain respective structural images and texture images;

Secondly, by calculating the gradient similarity between each pixel in the structural image of the left viewpoint image of the original undistorted stereoscopic image and the corresponding pixel in the structural image of the left viewpoint image of the distorted stereoscopic image to be evaluated, Obtain the image quality objective evaluation prediction value of the structural image of the left viewpoint image of the distorted stereoscopic image to be evaluated; similarly, by calculating the difference between each pixel in the structural image of the right viewpoint image of the original undistorted stereoscopic image and to be evaluated The gradient similarity between corresponding pixels in the structural image of the right viewpoint image of the distorted stereoscopic image is obtained, and the image quality objective evaluation prediction value of the structural image of the right viewpoint image of the distorted stereoscopic image to be evaluated is obtained;

Next, by calculating each sub-block with a size of 8×8 in the texture image of the left viewpoint image of the original undistorted stereoscopic image and the corresponding size in the texture image of the left viewpoint image of the distorted stereoscopic image to be evaluated is the structural similarity between 8×8 sub-blocks, and obtain the image quality objective evaluation prediction value of the texture image of the left viewpoint image of the distorted stereo image to be evaluated; similarly, by calculating the right Structural similarity between each 8×8 sub-block in the texture image of the viewpoint image and the corresponding 8×8 sub-block in the texture image of the right viewpoint image of the distorted stereo image to be evaluated , obtaining the image quality objective evaluation prediction value of the texture image of the right viewpoint image of the distorted stereoscopic image to be evaluated;

Furthermore, the image quality objective evaluation prediction value of the structural image of the left viewpoint image and the right viewpoint image of the distorted stereoscopic image to be evaluated is fused to obtain the image quality objective evaluation prediction value of the structural image of the distorted stereoscopic image to be evaluated; Similarly, the image quality objective evaluation prediction value of the texture image of the left viewpoint image and the right viewpoint image of the distorted stereoscopic image to be evaluated is fused to obtain the image quality objective evaluation prediction value of the texture image of the distorted stereoscopic image to be evaluated;

Finally, the image quality objective evaluation prediction value of the structure image and the texture image of the distorted stereo image to be evaluated are fused to obtain the image quality objective evaluation prediction value of the distorted stereo image to be evaluated.

The stereoscopic image quality objective evaluation method based on structure texture separation of the present invention, it specifically comprises the following steps:

①Let S _org denote the original undistorted stereo image, let S _dis denote the distorted stereo image to be evaluated, denote the left viewpoint image of S _org as {L _org (x,y)}, and denote the right viewpoint image of S _org The image is recorded as {R _org (x,y)}, the left view image of S _dis is recorded as {L _dis (x,y)}, and the right view image of S _dis is recorded as {R _dis (x,y)} , where (x, y) represents the coordinate position of the pixel in the left-viewpoint image and the right-viewpoint image, 1≤x≤W, 1≤y≤H, W represents the width of the left-viewpoint image and the right-viewpoint image, and H represents The height of the left view image and the right view image, L _org (x, y) represents the pixel value of the pixel whose coordinate position is (x, y) in {L _org (x, y)}, R _org (x, y) Indicates the pixel value of the pixel whose coordinate position is (x, y) in {R _org (x, y)}, and L _dis (x, y) indicates that the coordinate position in {L _dis (x, y)} is (x, y) y), and R _dis (x, y) represents the pixel value of the pixel whose coordinate position is (x, y) in {R _dis (x, y)}.

Here, the stereoscopic image database of Ningbo University and the LIVE stereoscopic image database are used to analyze the correlation between the image quality objective evaluation prediction value and the average subjective score difference of the distorted stereoscopic image obtained in this embodiment. The stereoscopic image library of Ningbo University consists of 12 undistorted stereoscopic images, 60 distorted stereoscopic images under JPEG compression with different degrees of distortion, 60 distorted stereoscopic images under JPEG2000 compression, and 60 distorted stereoscopic images under Gaussian blur Stereo images, 60 distorted stereo images in the case of Gaussian white noise, and 72 distorted stereo images in the case of H.264 encoding distortion. The LIVE stereoscopic image library consists of 20 undistorted stereoscopic images, 80 distorted stereoscopic images under JPEG compression with different distortion levels, 80 distorted stereoscopic images under JPEG2000 compression, and 45 distorted stereoscopic images under Gaussian blur Stereo images, 80 distorted stereo images in the case of Gaussian white noise, and 80 distorted stereo images in the case of Fast Fading distortion.

和

将{R_org(x,y)}的结构图像和纹理图像对应记为

和

将{L_dis(x,y)}的结构图像和纹理图像对应记为

和

将{R_dis(x,y)}的结构图像和纹理图像对应记为

和

其中，

表示

中坐标位置为(x,y)的像素点的像素值，

表示

中坐标位置为(x,y)的像素点的像素值，

表示

中坐标位置为(x,y)的像素点的像素值，

表示

中坐标位置为(x,y)的像素点的像素值，

表示

中坐标位置为(x,y)的像素点的像素值，

表示

中坐标位置为(x,y)的像素点的像素值，

表示

中坐标位置为(x,y)的像素点的像素值，表示

中坐标位置为(x,y)的像素点的像素值。② Implement structure and texture separation for {L _org (x, y)}, {R _org (x, y)}, {L _dis (x, y)} and {R _dis (x, y)} respectively, and obtain {L _org (x,y)}, {R _org (x,y)}, {L _dis (x,y)} and {R _dis (x,y)} respectively structure image and texture image, the {L _org ( The corresponding structure image and texture image of x,y)} are recorded as

and

The structure image and texture image correspondence of {R _org (x,y)} are recorded as

and

The structure image and texture image of {L _dis (x,y)} are recorded as

and

The structure image and texture image of {R _dis (x,y)} are recorded as

and

in,

express

The pixel value of the pixel whose coordinate position is (x, y),

express

The pixel value of the pixel whose coordinate position is (x, y),

express

The pixel value of the pixel whose coordinate position is (x, y),

express

The pixel value of the pixel whose coordinate position is (x, y),

express

The pixel value of the pixel whose coordinate position is (x, y),

express

The pixel value of the pixel whose coordinate position is (x, y),

express

The pixel value of the pixel whose coordinate position is (x, y), express

The pixel value of the pixel whose middle coordinate position is (x, y).

的获取过程为：In this specific embodiment, the structure image of {L _org (x, y)} in step ② and the texture image

The acquisition process is:

②-1a. Define the current pixel point to be processed in {L _org (x,y)} as the current pixel point.

将以当前像素点为中心的21×21邻域窗口内的每个邻域像素点为中心的9×9邻域窗口构成的块均定义为邻域子块，将在以当前像素点为中心的21×21邻域窗口内的且以在{L_org(x,y)}中坐标位置为q的邻域像素点为中心的9×9邻域窗口构成的邻域子块记为

其中，p∈Ω，q∈Ω，在此Ω表示{L_org(x,y)}中的所有像素点的坐标位置的集合，(x₂,y₂)表示当前子块中的像素点在当前子块

中的坐标位置，1≤x₂≤9,1≤y₂≤9，

表示当前子块

中坐标位置为(x₂,y₂)的像素点的像素值，(x₃,y₃)表示中的像素点在中的坐标位置，1≤x₃≤9,1≤y₃≤9，表示

中坐标位置为(x₃,y₃)的像素点的像素值。②-2a. Record the coordinate position of the current pixel point in {L _org (x,y)} as p, and record each pixel in the 21×21 neighborhood window centered on the current pixel point except the current pixel point A point is defined as a neighborhood pixel point, and a block composed of a 9×9 neighborhood window centered on the current pixel point is defined as the current sub-block, and is recorded as

A block composed of a 9×9 neighborhood window centered on each neighborhood pixel in a 21×21 neighborhood window centered on the current pixel is defined as a neighborhood sub-block, and will be centered on the current pixel The neighborhood sub-block composed of a 9×9 neighborhood window centered on the neighborhood pixel point whose coordinate position is q in {L _org (x,y)} within the 21×21 neighborhood window of is denoted as

Among them, p∈Ω, q∈Ω, where Ω represents the set of coordinate positions of all pixels in {L _org (x,y)}, and (x ₂ ,y ₂ ) represents the current sub-block The pixels in the current sub-block

Coordinate position in , 1≤x ₂ ≤9, 1≤y ₂ ≤9,

represents the current subblock

The pixel value of the pixel point whose middle coordinate position is (x ₂ , y ₂ ), (x ₃ , y ₃ ) means The pixels in Coordinate position in , 1≤x ₃ ≤9, 1≤y ₃ ≤9, express

The pixel value of the pixel point whose middle coordinate position is (x ₃ , y ₃ ).

In the above step ②-2a, for any pixel in the current sub-block, assume that the coordinate position of the pixel in {L _org (x,y)} is (x, y), if x<1 and 1≤ y≤H, then assign the pixel value of the pixel whose coordinate position is (1,y) in {L _org (x,y)} to the pixel; if x>W and 1≤y≤H, then { The pixel value of the pixel whose coordinate position is (W, y) in L _org (x, y)} is assigned to the pixel; if 1≤x≤W and y<1, then {L _org (x, y) } assigns the pixel value of the pixel whose coordinate position is (x, 1) to the pixel; if 1≤x≤W and y>H, then the coordinate position in {L _org (x,y)} is (x ,H) to assign the pixel value of the pixel point to the pixel point; if x<1 and y<1, then the pixel value of the pixel point whose coordinate position is (1,1) in {L _org (x,y)} Assign a value to the pixel; if x>W and y<1, then assign the pixel value of the pixel whose coordinate position is (W,1) in {L _org (x,y)} to the pixel; if x< 1 and y>H, then assign the pixel value of the pixel whose coordinate position is (1,H) in {L _org (x,y)} to the pixel; if x>W and y>H, then assign { The pixel value of the pixel whose coordinate position is (W, H) in L _org (x, y)} is assigned to the pixel; similarly, for any neighboring pixel, it is also the same as any pixel in the above-mentioned current sub-block Points do the same operation, so that the pixel value of the neighboring pixel point beyond the image boundary is replaced by the pixel value of the nearest boundary pixel point. That is, in the above step ②-2a, if the coordinate position of a certain pixel in the block formed by the 9×9 neighborhood window centered on the current pixel exceeds the boundary of {L _org (x,y)}, then the The pixel value of the pixel point is replaced by the pixel value of the nearest border pixel point; if the coordinate position of the neighboring pixel point in the 21×21 neighborhood window centered on the current pixel point exceeds {L _org (x,y) }, the pixel value of the neighborhood pixel is replaced by the pixel value of the nearest border pixel; if the current pixel is the center of each neighborhood pixel in the 21×21 neighborhood window If the coordinate position of a pixel in the block formed by the 9×9 neighborhood window exceeds the boundary of {L _org (x,y)}, the pixel value of the pixel is replaced by the pixel value of the nearest boundary pixel .

中的每个像素点的特征矢量，将当前子块

中坐标位置为(x2,y2)的像素点的特征矢量记为

$X_{L,\mathrm{org}}^p(x_2,y_2)=[I_{L,\mathrm{org}}^p(x_2,y_2),|\frac{{\&PartialD;I}_{L,\mathrm{org}}^p(x_2,y_2)}{\&PartialD;x}|,|\frac{{\&PartialD;I}_{L,\mathrm{org}}^p(x_2,y_2)}{\&PartialD;y}|,|\frac{{\&PartialD;}^2{I}_{L,\mathrm{org}}^p(x_2,y_2)}{{\&PartialD;x}^2}|,|\frac{{\&PartialD;}^2{I}_{L,\mathrm{org}}^p(x_2,y_2)}{{\&PartialD;y}^2}|,x_2,y_2]$ 其中，

的维数为7，符号“[]”为矢量表示符号，符号“||”为绝对值符号，表示当前子块

中坐标位置为(x₂,y₂)的像素点的密度值，为

在水平方向的一阶偏导数，为

在垂直方向的一阶偏导数，

为在水平方向的二阶偏导数，

为在垂直方向的二阶偏导数。②-3a. Get the current sub-block

The feature vector of each pixel in the current sub-block

The feature vector of the pixel point whose coordinate position is (x2, y2) is recorded as

$x_{L,\mathrm{org}}^p(x_2,{\mathrm{the\; y}}_2)=[I_{L,\mathrm{org}}^p(x_2,{\mathrm{the\; y}}_2),|\frac{{\&PartialD;I}_{L,\mathrm{org}}^p(x_2,{\mathrm{the\; y}}_2)}{\&PartialD;x}|,|\frac{{\&PartialD;I}_{L,\mathrm{org}}^p(x_2,{\mathrm{the\; y}}_2)}{\&PartialD;\mathrm{the\; y}}|,|\frac{{\&PartialD;}^2{I}_{L,\mathrm{org}}^p(x_2,{\mathrm{the\; y}}_2)}{{\&PartialD;x}^2}|,|\frac{{\&PartialD;}^2{I}_{L,\mathrm{org}}^p(x_2,{\mathrm{the\; y}}_2)}{{\&PartialD;\mathrm{the\; y}}^2}|,x_2,{\mathrm{the\; y}}_2]$ in,

The dimension is 7, the symbol “[]” is a vector representation symbol, and the symbol “||” is an absolute value symbol, represents the current subblock

The density value of the pixel point whose coordinate position is (x ₂ , y ₂ ), for

The first partial derivative in the horizontal direction, for

The first partial derivative in the vertical direction,

for The second partial derivative in the horizontal direction,

for Second order partial derivatives in the vertical direction.

中的每个像素点的特征矢量，计算当前子块

的协方差矩阵，记为

$C_{L,\mathrm{org}}^p=\frac{1}{7\&times;7-1}\underset{x_2=1}{\overset{9}{\mathrm{\&Sigma;}}}\underset{y_2=1}{\overset{9}{\mathrm{\&Sigma;}}}(X_{L,\mathrm{org}}^p(x_2,y_2)-{\mathrm{\&mu;}}_{L,\mathrm{org}}^p){(X_{L,\mathrm{org}}^p(x_2,y_2)-{\mathrm{\&mu;}}_{L,\mathrm{org}}^p)}^T,$ 其中，

的维数为7×7，

表示当前子块

中的所有像素点的特征矢量的均值矢量，

为

的转置矢量。②-4a, according to the current sub-block

The feature vector of each pixel in the current sub-block is calculated

The covariance matrix of is denoted as

$C_{L,\mathrm{org}}^p=\frac{1}{7\&times;7-1}\underset{x_2=1}{\overset{9}{\mathrm{\&Sigma;}}}\underset{{\mathrm{the\; y}}_2=1}{\overset{9}{\mathrm{\&Sigma;}}}(x_{L,\mathrm{org}}^p(x_2,{\mathrm{the\; y}}_2)-{\mathrm{\&mu;}}_{L,\mathrm{org}}^p){(x_{L,\mathrm{org}}^p(x_2,{\mathrm{the\; y}}_2)-{\mathrm{\&mu;}}_{L,\mathrm{org}}^p)}^T,$ in,

The dimension of is 7×7,

represents the current subblock

The mean vector of the feature vectors of all pixels in

for

The transpose vector of .

的协方差矩阵

进行Cholesky分解，

得到当前子块

的Sigma特征集，记为 $S_{L,\mathrm{org}}^p=[\sqrt{10}\&times;L^{\left(1\right)},...,\sqrt{10}\&times;L^{(i\&prime;)},...,\sqrt{10}\&times;L^{\left(7\right)},-\sqrt{10}\&times;L^{\left(1\right)},...,-\sqrt{10}\&times;L^{(i\&prime;)},...,-\sqrt{10}\&times;L^{\left(7\right)},{\mathrm{\&mu;}}_{L,\mathrm{org}}^p],$ 其中，L^T为L的转置矩阵，

的维数为7×15，符号“[]”为矢量表示符号，此处1≤i'≤7，L⁽¹⁾表示L的第1列向量，L^(i')表示L的第i'列向量，L⁽⁷⁾表示L的第7列向量。②-5a. For the current sub-block

The covariance matrix of

Perform Cholesky decomposition,

get the current subblock

The Sigma feature set of is denoted as $S_{L,\mathrm{org}}^p=[\sqrt{10}\&times;L^{\left(1\right)},...,\sqrt{10}\&times;L^{(i\&prime;)},...,\sqrt{10}\&times;L^{\left(7\right)},-\sqrt{10}\&times;L^{\left(1\right)},...,-\sqrt{10}\&times;L^{(i\&prime;)},...,-\sqrt{10}\&times;L^{\left(7\right)},{\mathrm{\&mu;}}_{L,\mathrm{org}}^p],$ Among them, L ^T is the transpose matrix of L,

The dimension of is 7×15, and the symbol "[]" is a vector representation symbol, where 1≤i'≤7, L ⁽¹⁾ represents the first column vector of L, and L ^(i') represents the i'th column of L Column vector, L ⁽⁷⁾ indicates the 7th column vector of L.

的Sigma特征集记为

的维数为7×15。②-6a. Use the same operation as step ②-3a to step ②-5a to obtain the Sigma feature set of the neighborhood sub-block composed of a 9×9 neighborhood window centered on each neighborhood pixel, and set

The Sigma feature set is denoted as

The dimension of is 7×15.

和以每个邻域像素点为中心的9×9邻域窗口构成的邻域子块的Sigma特征集，获取当前像素点的结构信息，记为 $I_{L,\mathrm{org}}^{\mathrm{str}}\left(p\right),I_{L,\mathrm{org}}^{\mathrm{str}}\left(p\right)=\frac{\underset{q\&Element;N\&prime;\left(p\right)}{\mathrm{\&Sigma;}}\exp (-\frac{{\left|\right|S_{L,\mathrm{org}}^p-S_{L,\mathrm{org}}^q\left|\right|}^2}{{2\mathrm{\&sigma;}}^2})\&times;L_{\mathrm{org}}\left(q\right)}{\underset{q\&Element;N\&prime;\left(p\right)}{\mathrm{\&Sigma;}}\exp (-\frac{{\left|\right|S_{L,\mathrm{org}}^p-S_{L,\mathrm{org}}^q\left|\right|}^2}{{2\mathrm{\&sigma;}}^2})},$ 其中，N'(p)表示{L_org(x,y)}中以当前像素点为中心的21×21邻域窗口内的所有邻域像素点在{L_org(x,y)}中的坐标位置的集合，exp()表示以e为底的指数函数，e=2.71828183，σ表示高斯函数的标准差，在本实施例中取σ=0.06，符号“｜｜｜｜”为欧氏距离计算符号，L_org(q)表示{L_org(x,y)}中坐标位置为q的像素点的像素值。②-7a, according to the current sub-block The Sigma feature set

and the Sigma feature set of the neighborhood sub-block composed of a 9×9 neighborhood window centered on each neighborhood pixel to obtain the structural information of the current pixel, denoted as $I_{L,\mathrm{org}}^{\mathrm{str}}\left(p\right),I_{L,\mathrm{org}}^{\mathrm{str}}\left(p\right)=\frac{\underset{q\&Element;N\&prime;\left(p\right)}{\mathrm{\&Sigma;}}\exp (-\frac{{\left|\right|S_{L,\mathrm{org}}^p-S_{L,\mathrm{org}}^q\left|\right|}^2}{{2\mathrm{\&sigma;}}^2})\&times;L_{\mathrm{org}}\left(q\right)}{\underset{q\&Element;N\&prime;\left(p\right)}{\mathrm{\&Sigma;}}\exp (-\frac{{\left|\right|S_{L,\mathrm{org}}^p-S_{L,\mathrm{org}}^q\left|\right|}^2}{{2\mathrm{\&sigma;}}^2})},$ Among them, N'(p) means that all the neighboring pixels in {L _org (x, y)} in the 21×21 neighborhood window centered on the current pixel point in {L _org (x, y)} A collection of coordinate positions, exp() represents an exponential function with e as the base, e=2.71828183, σ represents the standard deviation of the Gaussian function, in this embodiment, σ=0.06, and the symbol "||||" is the Euclidean distance Calculation symbol, L _org (q) represents the pixel value of the pixel whose coordinate position is q in {L _org (x,y)}.

获取当前像素点的纹理信息，记为

其中，L_org(p)表示当前像素点的像素值。②-8a. According to the structure information of the current pixel point

Get the texture information of the current pixel point, denoted as

Wherein, L _org (p) represents the pixel value of the current pixel point.

由{L_org(x,y)}中的所有像素点的纹理信息构成{L_org(x,y)}的纹理图像，记为

②-9a. Set the next pixel to be processed in {L _org (x,y)} as the current pixel, and then return to step ②-2a to continue until all pixels in {L _org (x,y)} Points are processed, and the structure information and texture information of each pixel in {L _org (x, y)} are obtained, which is composed of the structure information of all pixels in {L _org (x, y)} {L _org ( The structural image of x,y)}, denoted as

The texture image of {L _org (x, y)} is composed of the texture information of all pixels in {L _org (x, y)}, recorded as

和纹理图像

相同的操作，获取{R_org(x,y)}的结构图像

和纹理图像

{L_dis(x,y)}的结构图像

和纹理图像

{R_dis(x,y)}的结构图像

和纹理图像

即：步骤②中{R_org(x,y)}的结构图像和纹理图像

的获取过程为：Obtain the structure image of {L _org (x, y)} with step ②-1a to step ②-9a

and the texture image

The same operation, get the structure image of {R _org (x,y)}

and the texture image

Structural image of {L _dis (x,y)}

and the texture image

Structural image of {R _dis (x,y)}

and the texture image

That is: the structural image of {R _org (x,y)} in step ② and the texture image

The acquisition process is:

②-1b. Define the current pixel to be processed in {R _org (x, y)} as the current pixel.

将以当前像素点为中心的21×21邻域窗口内的每个邻域像素点为中心的9×9邻域窗口构成的块均定义为邻域子块，将在以当前像素点为中心的21×21邻域窗口内的且以在{R_org(x,y)}中坐标位置为q的邻域像素点为中心的9×9邻域窗口构成的邻域子块记为

其中，p∈Ω，q∈Ω，在此Ω表示{R_org(x,y)}中的所有像素点的坐标位置的集合，(x₂,y₂)表示当前子块

中的像素点在当前子块

中的坐标位置，1≤x₂≤9,1≤y₂≤9，

表示当前子块

中坐标位置为(x₂,y₂)的像素点的像素值，(x₃,y₃)表示

中的像素点在中的坐标位置，1≤x₃≤9,1≤y₃≤9，

表示

中坐标位置为(x₃,y₃)的像素点的像素值。②-2b. Record the coordinate position of the current pixel in {R _org (x, y)} as p, and record each pixel in the 21×21 neighborhood window centered on the current pixel except the current pixel A point is defined as a neighborhood pixel point, and a block composed of a 9×9 neighborhood window centered on the current pixel point is defined as the current sub-block, and is recorded as

A block composed of a 9×9 neighborhood window centered on each neighborhood pixel in a 21×21 neighborhood window centered on the current pixel is defined as a neighborhood sub-block, and will be centered on the current pixel The neighborhood sub-block composed of a 9×9 neighborhood window centered at the neighborhood pixel point whose coordinate position is q in {R _org (x,y)} within the 21×21 neighborhood window of is denoted as

Among them, p∈Ω, q∈Ω, where Ω represents the set of coordinate positions of all pixels in {R _org (x,y)}, and (x ₂ ,y ₂ ) represents the current sub-block

The pixels in the current sub-block

Coordinate position in , 1≤x ₂ ≤9, 1≤y ₂ ≤9,

represents the current subblock

The pixel value of the pixel point whose middle coordinate position is (x ₂ , y ₂ ), (x ₃ , y ₃ ) means

The pixels in Coordinate position in , 1≤x ₃ ≤9, 1≤y ₃ ≤9,

express

The pixel value of the pixel point whose middle coordinate position is (x ₃ , y ₃ ).

In the above step ②-2b, if the coordinate position of a certain pixel in the block formed by the 9×9 neighborhood window centered on the current pixel exceeds the boundary of {R _org (x,y)}, then the pixel The pixel value of the point is replaced by the pixel value of the nearest border pixel point; if the coordinate position of the neighboring pixel point in the 21×21 neighborhood window centered on the current pixel point exceeds {R _org (x,y)} , the pixel value of the neighborhood pixel is replaced by the pixel value of the nearest border pixel; if each neighborhood pixel in the 21×21 neighborhood window with the current pixel as the center If the coordinate position of a certain pixel in the block formed by the ×9 neighborhood window exceeds the boundary of {R _org (x, y)}, the pixel value of the pixel is replaced by the pixel value of the nearest boundary pixel.

中的每个像素点的特征矢量，将当前子块

中坐标位置为(x2,y2)的像素点的特征矢量记为

$X_{L,\mathrm{org}}^p(x_2,y_2)=[I_{L,\mathrm{org}}^p(x_2,y_2),|\frac{{\&PartialD;I}_{L,\mathrm{org}}^p(x_2,y_2)}{\&PartialD;x}|,|\frac{{\&PartialD;I}_{L,\mathrm{org}}^p(x_2,y_2)}{\&PartialD;y}|,|\frac{{\&PartialD;}^2{I}_{L,\mathrm{org}}^p(x_2,y_2)}{{\&PartialD;x}^2}|,|\frac{{\&PartialD;}^2{I}_{L,\mathrm{org}}^p(x_2,y_2)}{{\&PartialD;y}^2}|,x_2,y_2]$ 其中，

的维数为7，符号“[]”为矢量表示符号，符号“||”为绝对值符号，表示当前子块

中坐标位置为(x2,y2)的像素点的密度值，

为

在水平方向的一阶偏导数，

为

在垂直方向的一阶偏导数，

为

在水平方向的二阶偏导数，为

在垂直方向的二阶偏导数。②-3b. Get the current sub-block

The feature vector of each pixel in the current sub-block

The feature vector of the pixel point whose coordinate position is (x2, y2) is recorded as

$x_{L,\mathrm{org}}^p(x_2,{\mathrm{the\; y}}_2)=[I_{L,\mathrm{org}}^p(x_2,{\mathrm{the\; y}}_2),|\frac{{\&PartialD;I}_{L,\mathrm{org}}^p(x_2,{\mathrm{the\; y}}_2)}{\&PartialD;x}|,|\frac{{\&PartialD;I}_{L,\mathrm{org}}^p(x_2,{\mathrm{the\; y}}_2)}{\&PartialD;\mathrm{the\; y}}|,|\frac{{\&PartialD;}^2{I}_{L,\mathrm{org}}^p(x_2,{\mathrm{the\; y}}_2)}{{\&PartialD;x}^2}|,|\frac{{\&PartialD;}^2{I}_{L,\mathrm{org}}^p(x_2,{\mathrm{the\; y}}_2)}{{\&PartialD;\mathrm{the\; y}}^2}|,x_2,{\mathrm{the\; y}}_2]$ in,

The dimension is 7, the symbol “[]” is a vector representation symbol, and the symbol “||” is an absolute value symbol, represents the current subblock

The density value of the pixel point whose middle coordinate position is (x2, y2),

for

The first partial derivative in the horizontal direction,

for

The first partial derivative in the vertical direction,

for

The second partial derivative in the horizontal direction, for

Second order partial derivatives in the vertical direction.

中的每个像素点的特征矢量，计算当前子块

的协方差矩阵，记为

$C_{R,\mathrm{org}}^p=\frac{1}{7\&times;7-1}\underset{x_2=1}{\overset{9}{\mathrm{\&Sigma;}}}\underset{y_2=1}{\overset{9}{\mathrm{\&Sigma;}}}(X_{R,\mathrm{org}}^p(x_2,y_2)-{\mathrm{\&mu;}}_{R,\mathrm{org}}^p){(X_{R,\mathrm{org}}^p(x_2,y_2)-{\mathrm{\&mu;}}_{R,\mathrm{org}}^p)}^T,$ 其中，

的维数为7×7，表示当前子块

中的所有像素点的特征矢量的均值矢量，

为

的转置矢量。②-4b, according to the current sub-block

The feature vector of each pixel in the current sub-block is calculated

The covariance matrix of is denoted as

$C_{R,\mathrm{org}}^p=\frac{1}{7\&times;7-1}\underset{x_2=1}{\overset{9}{\mathrm{\&Sigma;}}}\underset{{\mathrm{the\; y}}_2=1}{\overset{9}{\mathrm{\&Sigma;}}}(x_{R,\mathrm{org}}^p(x_2,{\mathrm{the\; y}}_2)-{\mathrm{\&mu;}}_{R,\mathrm{org}}^p){(x_{R,\mathrm{org}}^p(x_2,{\mathrm{the\; y}}_2)-{\mathrm{\&mu;}}_{R,\mathrm{org}}^p)}^T,$ in,

The dimension of is 7×7, represents the current subblock

The mean vector of the feature vectors of all pixels in

for

The transpose vector of .

的协方差矩阵

进行Cholesky分解，

得到当前子块的Sigma特征集，记为

$S_{R,\mathrm{org}}^p=[\sqrt{10}\&times;L^{\left(1\right)},...,\sqrt{10}\&times;L^{(i\&prime;)},...,\sqrt{10}\&times;L^{\left(7\right)},-\sqrt{10}\&times;L^{\left(1\right)},...,-\sqrt{10}\&times;L^{(i\&prime;)},...,-\sqrt{10}\&times;L^{\left(7\right)},{\mathrm{\&mu;}}_{R,\mathrm{org}}^p],$ 其中，L^T为L的转置矩阵，

的维数为7×15，符号“[]”为矢量表示符号，此处1≤i'≤7，L⁽¹⁾表示L的第1列向量，L^(i')表示L的第i'列向量，L⁽⁷⁾表示L的第7列向量。②-5b. For the current sub-block

The covariance matrix of

Perform Cholesky decomposition,

get the current subblock The Sigma feature set of is denoted as

$S_{R,\mathrm{org}}^p=[\sqrt{10}\&times;L^{\left(1\right)},...,\sqrt{10}\&times;L^{(i\&prime;)},...,\sqrt{10}\&times;L^{\left(7\right)},-\sqrt{10}\&times;L^{\left(1\right)},...,-\sqrt{10}\&times;L^{(i\&prime;)},...,-\sqrt{10}\&times;L^{\left(7\right)},{\mathrm{\&mu;}}_{R,\mathrm{org}}^p],$ Among them, L ^T is the transpose matrix of L,

The dimension of is 7×15, and the symbol "[]" is a vector representation symbol, where 1≤i'≤7, L ⁽¹⁾ represents the first column vector of L, and L ^(i') represents the i'th column of L Column vector, L ⁽⁷⁾ indicates the 7th column vector of L.

的Sigma特征集记为

的维数为7×15。②-6b. Use the same operation as step ②-3b to step ②-5b to obtain the Sigma feature set of the neighborhood sub-block composed of a 9×9 neighborhood window centered on each neighborhood pixel, and set

The Sigma feature set is denoted as

The dimension of is 7×15.

的Sigma特征集

和以每个邻域像素点为中心的9×9邻域窗口构成的邻域子块的Sigma特征集，获取当前像素点的结构信息，记为 $I_{R,\mathrm{org}}^{\mathrm{str}}\left(p\right),I_{R,\mathrm{org}}^{\mathrm{str}}\left(p\right)=\frac{\underset{q\&Element;N\&prime;\left(p\right)}{\mathrm{\&Sigma;}}\exp (-\frac{{\left|\right|S_{R,\mathrm{org}}^p-S_{R,\mathrm{org}}^q\left|\right|}^2}{{2\mathrm{\&sigma;}}^2})\&times;L_{\mathrm{org}}\left(q\right)}{\underset{q\&Element;N\&prime;\left(p\right)}{\mathrm{\&Sigma;}}\exp (-\frac{{\left|\right|S_{R,\mathrm{org}}^p-S_{R,\mathrm{org}}^q\left|\right|}^2}{{2\mathrm{\&sigma;}}^2})},$ 其中，N'(p)表示{R_org(x,y)}中以当前像素点为中心的21×21邻域窗口内的所有邻域像素点在{R_org(x,y)}中的坐标位置的集合，exp()表示以e为底的指数函数，e=2.71828183，σ表示高斯函数的标准差，在本实施例中取σ=0.06，符号“||||”为欧氏距离计算符号，R_org(q)表示{R_org(x,y)}中坐标位置为q的像素点的像素值。②-7b, according to the current sub-block

The Sigma feature set

and the Sigma feature set of the neighborhood sub-block composed of a 9×9 neighborhood window centered on each neighborhood pixel to obtain the structural information of the current pixel, denoted as $I_{R,\mathrm{org}}^{\mathrm{str}}\left(p\right),I_{R,\mathrm{org}}^{\mathrm{str}}\left(p\right)=\frac{\underset{q\&Element;N\&prime;\left(p\right)}{\mathrm{\&Sigma;}}\exp (-\frac{{\left|\right|S_{R,\mathrm{org}}^p-S_{R,\mathrm{org}}^q\left|\right|}^2}{{2\mathrm{\&sigma;}}^2})\&times;L_{\mathrm{org}}\left(q\right)}{\underset{q\&Element;N\&prime;\left(p\right)}{\mathrm{\&Sigma;}}\exp (-\frac{{\left|\right|S_{R,\mathrm{org}}^p-S_{R,\mathrm{org}}^q\left|\right|}^2}{{2\mathrm{\&sigma;}}^2})},$ Among them, N'(p) represents all the neighboring pixels in {R _org (x, y)} in {R _org (x, y)} in the 21×21 neighborhood window centered on the current pixel A collection of coordinate positions, exp() represents an exponential function with e as the base, e=2.71828183, σ represents the standard deviation of the Gaussian function, in this embodiment, σ=0.06, and the symbol "||||" is the Euclidean distance Calculation symbol, R _org (q) represents the pixel value of the pixel at the coordinate position q in {R _org (x,y)}.

获取当前像素点的纹理信息，记为

其中，R_org(p)表示当前像素点的像素值。②-8b. According to the structure information of the current pixel point

Get the texture information of the current pixel point, denoted as

Wherein, R _org (p) represents the pixel value of the current pixel point.

由{R_org(x,y)}中的所有像素点的纹理信息构成{R_org(x,y)}的纹理图像，记为

②-9b. Set the next pixel to be processed in {R _org (x,y)} as the current pixel, and then return to step ②-2b to continue until all pixels in {R _org (x,y)} Point processing is completed, and the structure information and texture information of each pixel in {R _org (x, y)} are obtained, which is composed of the structure information of all pixels in {R _org (x, y)} {R _org ( The structural image of x,y)}, denoted as

The texture image of {R _org (x, y)} is composed of the texture information of all pixels in {R _org (x, y)}, recorded as

Structural image of {L _dis (x,y)} in step ② and the texture image The acquisition process is:

②-1c. Define the current pixel to be processed in {L _dis (x, y)} as the current pixel.

将以当前像素点为中心的21×21邻域窗口内的每个邻域像素点为中心的9×9邻域窗口构成的块均定义为邻域子块，将在以当前像素点为中心的21×21邻域窗口内的且以在{L_dis(x,y)}中坐标位置为q的邻域像素点为中心的9×9邻域窗口构成的邻域子块记为

其中，p∈Ω，q∈Ω，在此Ω表示{L_dis(x,y)}中的所有像素点的坐标位置的集合，(x₂,y₂)表示当前子块

中的像素点在当前子块

中的坐标位置，1≤x₂≤9,1≤y₂≤9，

表示当前子块

中坐标位置为(x₂,y₂)的像素点的像素值，(x₃,y₃)表示

中的像素点在

中的坐标位置，1≤x₃≤9,1≤y₃≤9，表示

中坐标位置为(x₃,y₃)的像素点的像素值。②-2c. Record the coordinate position of the current pixel point in {L _dis (x,y)} as p, and record each pixel in the 21×21 neighborhood window centered on the current pixel point except the current pixel point A point is defined as a neighborhood pixel point, and a block composed of a 9×9 neighborhood window centered on the current pixel point is defined as the current sub-block, and is recorded as

A block composed of a 9×9 neighborhood window centered on each neighborhood pixel in a 21×21 neighborhood window centered on the current pixel is defined as a neighborhood sub-block, and will be centered on the current pixel In the 21×21 neighborhood window of {L _dis (x,y)}, the neighborhood sub-block composed of a 9×9 neighborhood window centered on the neighborhood pixel whose coordinate position is q in {L dis (x,y)} is denoted as

Among them, p∈Ω, q∈Ω, where Ω represents the set of coordinate positions of all pixels in {L _dis (x,y)}, and (x ₂ ,y ₂ ) represents the current sub-block

The pixels in the current sub-block

Coordinate position in , 1≤x ₂ ≤9, 1≤y ₂ ≤9,

represents the current subblock

The pixel value of the pixel point whose middle coordinate position is (x ₂ , y ₂ ), (x ₃ , y ₃ ) means

The pixels in

Coordinate position in , 1≤x ₃ ≤9, 1≤y ₃ ≤9, express

The pixel value of the pixel point whose middle coordinate position is (x ₃ , y ₃ ).

In the above steps ②-2c, if the coordinate position of a certain pixel in the block formed by the 9×9 neighborhood window centered on the current pixel exceeds the boundary of {L _dis (x,y)}, then the pixel The pixel value of the point is replaced by the pixel value of the nearest border pixel point; if the coordinate position of the neighboring pixel point in the 21×21 neighborhood window centered on the current pixel point exceeds {L _dis (x,y)} boundary, the pixel value of the neighborhood pixel is replaced by the pixel value of the nearest border pixel; If the coordinate position of a certain pixel in the block formed by the ×9 neighborhood window exceeds the boundary of {L _dis (x, y)}, the pixel value of the pixel is replaced by the pixel value of the nearest boundary pixel.

中的每个像素点的特征矢量，将当前子块

中坐标位置为(x₂,y₂)的像素点的特征矢量记为 $X_{L,\mathrm{dis}}^p(x_2,y_2)=[I_{L,\mathrm{dis}}^p(x_2,y_2),|\frac{{\&PartialD;I}_{L,\mathrm{dis}}^p(x_2,y_2)}{\&PartialD;x}|,|\frac{{\&PartialD;I}_{L,\mathrm{dis}}^p(x_2,y_2)}{\&PartialD;y}|,|\frac{{\&PartialD;}^2{I}_{L,\mathrm{dis}}^p(x_2,y_2)}{{\&PartialD;x}^2}|,|\frac{{\&PartialD;}^2{I}_{L,\mathrm{dis}}^p(x_2,y_2)}{{\&PartialD;y}^2}|,x_2,y_2]$ ，其中，

的维数为7，符号“[]”为矢量表示符号，符号“||”为绝对值符号，

表示当前子块

中坐标位置为(x₂,y₂)的像素点的密度值，

为在水平方向的一阶偏导数，

为在垂直方向的一阶偏导数，

为

在水平方向的二阶偏导数，

为

在垂直方向的二阶偏导数。②-3c. Get the current sub-block

The feature vector of each pixel in the current sub-block

The feature vector of the pixel point whose coordinate position is (x ₂ , y ₂ ) is denoted as $x_{L,\mathrm{dis}}^p(x_2,{\mathrm{the\; y}}_2)=[I_{L,\mathrm{dis}}^p(x_2,{\mathrm{the\; y}}_2),|\frac{{\&PartialD;I}_{L,\mathrm{dis}}^p(x_2,{\mathrm{the\; y}}_2)}{\&PartialD;x}|,|\frac{{\&PartialD;I}_{L,\mathrm{dis}}^p(x_2,{\mathrm{the\; y}}_2)}{\&PartialD;\mathrm{the\; y}}|,|\frac{{\&PartialD;}^2{I}_{L,\mathrm{dis}}^p(x_2,{\mathrm{the\; y}}_2)}{{\&PartialD;x}^2}|,|\frac{{\&PartialD;}^2{I}_{L,\mathrm{dis}}^p(x_2,{\mathrm{the\; y}}_2)}{{\&PartialD;\mathrm{the\; y}}^2}|,x_2,{\mathrm{the\; y}}_2]$ ,in,

The dimension is 7, the symbol “[]” is a vector representation symbol, and the symbol “||” is an absolute value symbol,

represents the current subblock

The density value of the pixel point whose coordinate position is (x ₂ , y ₂ ),

for The first partial derivative in the horizontal direction,

for The first partial derivative in the vertical direction,

for

The second partial derivative in the horizontal direction,

for

Second order partial derivatives in the vertical direction.

中的每个像素点的特征矢量，计算当前子块

的协方差矩阵，记为

$C_{L,\mathrm{dis}}^p=\frac{1}{7\&times;7-1}\underset{x_2=1}{\overset{9}{\mathrm{\&Sigma;}}}\underset{y_2=1}{\overset{9}{\mathrm{\&Sigma;}}}(X_{L,\mathrm{dis}}^p(x_2,y_2)-{\mathrm{\&mu;}}_{L,\mathrm{dis}}^p){(X_{L,\mathrm{dis}}^p(x_2,y_2)-{\mathrm{\&mu;}}_{L,\mathrm{dis}}^p)}^T,$ 其中，

的维数为7×7，表示当前子块

中的所有像素点的特征矢量的均值矢量，

为的转置矢量。②-4c, according to the current sub-block

The feature vector of each pixel in the calculation of the current sub-block

The covariance matrix of is denoted as

$C_{L,\mathrm{dis}}^p=\frac{1}{7\&times;7-1}\underset{x_2=1}{\overset{9}{\mathrm{\&Sigma;}}}\underset{{\mathrm{the\; y}}_2=1}{\overset{9}{\mathrm{\&Sigma;}}}(x_{L,\mathrm{dis}}^p(x_2,{\mathrm{the\; y}}_2)-{\mathrm{\&mu;}}_{L,\mathrm{dis}}^p){(x_{L,\mathrm{dis}}^p(x_2,{\mathrm{the\; y}}_2)-{\mathrm{\&mu;}}_{L,\mathrm{dis}}^p)}^T,$ in,

The dimension of is 7×7, represents the current subblock

The mean vector of the feature vectors of all pixels in

for The transpose vector of .

的协方差矩阵进行Cholesky分解，

得到当前子块

的Sigma特征集，记为

$S_{L,\mathrm{dis}}^p=[\sqrt{10}\&times;L^{\left(1\right)},...,\sqrt{10}\&times;L^{(i\&prime;)},...,\sqrt{10}\&times;L^{\left(7\right)},-\sqrt{10}\&times;L^{\left(1\right)},...,-\sqrt{10}\&times;L^{(i\&prime;)},...,-\sqrt{10}\&times;L^{\left(7\right)},{\mathrm{\&mu;}}_{L,\mathrm{dis}}^p],$ 其中，L^T为L的转置矩阵，

的维数为7×15，符号“[]”为矢量表示符号，此处1≤i'≤7，L⁽¹⁾表示L的第1列向量，L^(i')表示L的第i'列向量，L⁽⁷⁾表示L的第7列向量。②-5c, for the current sub-block

The covariance matrix of Perform Cholesky decomposition,

get the current subblock

The Sigma feature set of is denoted as

$S_{L,\mathrm{dis}}^p=[\sqrt{10}\&times;L^{\left(1\right)},...,\sqrt{10}\&times;L^{(i\&prime;)},...,\sqrt{10}\&times;L^{\left(7\right)},-\sqrt{10}\&times;L^{\left(1\right)},...,-\sqrt{10}\&times;L^{(i\&prime;)},...,-\sqrt{10}\&times;L^{\left(7\right)},{\mathrm{\&mu;}}_{L,\mathrm{dis}}^p],$ Among them, L ^T is the transpose matrix of L,

The dimension of is 7×15, and the symbol "[]" is a vector representation symbol, where 1≤i'≤7, L ⁽¹⁾ represents the first column vector of L, and L ^(i') represents the i'th column of L Column vector, L ⁽⁷⁾ indicates the 7th column vector of L.

的Sigma特征集记为

的维数为7×15。②-6c. Use the same operation as step ②-3c to step ②-5c to obtain the Sigma feature set of the neighborhood sub-block composed of a 9×9 neighborhood window centered on each neighborhood pixel, and set

The Sigma feature set is denoted as

The dimension of is 7×15.

的Sigma特征集

和以每个邻域像素点为中心的9×9邻域窗口构成的邻域子块的Sigma特征集，获取当前像素点的结构信息，记为 $I_{L,\mathrm{dis}}^{\mathrm{str}}\left(p\right),I_{L,\mathrm{dis}}^{\mathrm{str}}\left(p\right)=\frac{\underset{q\&Element;N\&prime;\left(p\right)}{\mathrm{\&Sigma;}}\exp (-\frac{{\left|\right|S_{L,\mathrm{dis}}^p-S_{L,\mathrm{dis}}^q\left|\right|}^2}{{2\mathrm{\&sigma;}}^2})\&times;L_{\mathrm{dis}}\left(q\right)}{\underset{q\&Element;N\&prime;\left(p\right)}{\mathrm{\&Sigma;}}\exp (-\frac{{\left|\right|S_{L,\mathrm{dis}}^p-S_{L,\mathrm{dis}}^q\left|\right|}^2}{{2\mathrm{\&sigma;}}^2})},$ 其中，N'(p)表示{L_dis(x,y)}中以当前像素点为中心的21×21邻域窗口内的所有邻域像素点在{L_dis(x,y)}中的坐标位置的集合，exp()表示以e为底的指数函数，e=2.71828183，σ表示高斯函数的标准差，在本实施例中取σ=0.06，符号“||||”为欧氏距离计算符号，L_dis(q)表示{L_dis(x,y)}中坐标位置为q的像素点的像素值。②-7c, according to the current sub-block

The Sigma feature set

and the Sigma feature set of the neighborhood sub-block composed of a 9×9 neighborhood window centered on each neighborhood pixel to obtain the structural information of the current pixel, denoted as $I_{L,\mathrm{dis}}^{\mathrm{str}}\left(p\right),I_{L,\mathrm{dis}}^{\mathrm{str}}\left(p\right)=\frac{\underset{q\&Element;N\&prime;\left(p\right)}{\mathrm{\&Sigma;}}\exp (-\frac{{\left|\right|S_{L,\mathrm{dis}}^p-S_{L,\mathrm{dis}}^q\left|\right|}^2}{{2\mathrm{\&sigma;}}^2})\&times;L_{\mathrm{dis}}\left(q\right)}{\underset{q\&Element;N\&prime;\left(p\right)}{\mathrm{\&Sigma;}}\exp (-\frac{{\left|\right|S_{L,\mathrm{dis}}^p-S_{L,\mathrm{dis}}^q\left|\right|}^2}{{2\mathrm{\&sigma;}}^2})},$ Among them, N'(p) means that all neighboring pixels in {L _dis (x, y)} in the 21×21 neighborhood window centered on the current pixel in {L _dis (x, y)} A collection of coordinate positions, exp() represents an exponential function with e as the base, e=2.71828183, σ represents the standard deviation of the Gaussian function, in this embodiment, σ=0.06, and the symbol "||||" is the Euclidean distance Calculation symbol, L _dis (q) represents the pixel value of the pixel point whose coordinate position is q in {L _dis (x,y)}.

获取当前像素点的纹理信息，记为 $I_{L,\mathrm{dis}}^{\mathrm{tex}}\left(p\right),I_{L,\mathrm{dis}}^{\mathrm{tex}}\left(p\right)=L_{\mathrm{dis}}\left(p\right)-I_{L,\mathrm{dis}}^{\mathrm{str}}\left(p\right),$ 其中，L_dis(p)表示当前像素点的像素值。②-8c, according to the structure information of the current pixel

Get the texture information of the current pixel point, denoted as $I_{L,\mathrm{dis}}^{\mathrm{tex}}\left(p\right),I_{L,\mathrm{dis}}^{\mathrm{tex}}\left(p\right)=L_{\mathrm{dis}}\left(p\right)-I_{L,\mathrm{dis}}^{\mathrm{str}}\left(p\right),$ Wherein, L _dis (p) represents the pixel value of the current pixel point.

由{L_dis(x,y)}中的所有像素点的纹理信息构成{L_dis(x,y)}的纹理图像，记为

②-9c. Set the next pixel to be processed in {L _dis (x,y)} as the current pixel, and then return to step ②-2c to continue until all pixels in {L _dis (x,y)} Point processing is completed, and the structure information and texture information of each pixel in {L _dis (x, y)} are obtained, which is composed of the structure information of all pixels in {L _dis (x, y)} {L _dis ( The structural image of x,y)}, denoted as

The texture image of {L _dis (x, y)} is composed of the texture information of all pixels in {L _dis (x, y)}, denoted as

和纹理图像

的获取过程为：Structural image of {R _dis (x,y)} in step ②

and the texture image

The acquisition process is:

②-1d. Define the current pixel point to be processed in {R _dis (x, y)} as the current pixel point.

将以当前像素点为中心的21×21邻域窗口内的每个邻域像素点为中心的9×9邻域窗口构成的块均定义为邻域子块，将在以当前像素点为中心的21×21邻域窗口内的且以在{R_dis(x,y)}中坐标位置为q的邻域像素点为中心的9×9邻域窗口构成的邻域子块记为

其中，p∈Ω，q∈Ω，在此Ω表示{R_dis(x,y)}中的所有像素点的坐标位置的集合，(x₂,y₂)表示当前子块

中的像素点在当前子块中的坐标位置，1≤x₂≤9,1≤y₂≤9，表示当前子块

中坐标位置为(x₂,y₂)的像素点的像素值，(x₃,y₃)表示

中的像素点在

中的坐标位置，1≤x₃≤9,1≤y₃≤9，表示

中坐标位置为(x₃,y₃)的像素点的像素值。②-2d. Record the coordinate position of the current pixel in {R _dis (x,y)} as p, and record each pixel in the 21×21 neighborhood window centered on the current pixel except the current pixel A point is defined as a neighborhood pixel point, and a block composed of a 9×9 neighborhood window centered on the current pixel point is defined as the current sub-block, and is recorded as

A block composed of a 9×9 neighborhood window centered on each neighborhood pixel in a 21×21 neighborhood window centered on the current pixel is defined as a neighborhood sub-block, and will be centered on the current pixel In the 21×21 neighborhood window of {R _dis (x,y)}, the neighborhood sub-block composed of a 9×9 neighborhood window centered on the neighborhood pixel whose coordinate position is q in {R dis (x,y)} is denoted as

Among them, p∈Ω, q∈Ω, where Ω represents the set of coordinate positions of all pixels in {R _dis (x,y)}, and (x ₂ ,y ₂ ) represents the current sub-block

The pixels in the current sub-block Coordinate position in , 1≤x ₂ ≤9, 1≤y ₂ ≤9, represents the current subblock

The pixel value of the pixel point whose middle coordinate position is (x ₂ , y ₂ ), (x ₃ , y ₃ ) means

The pixels in

Coordinate position in , 1≤x ₃ ≤9, 1≤y ₃ ≤9, express

The pixel value of the pixel point whose middle coordinate position is (x ₃ , y ₃ ).

In the above step ②-2d, if the coordinate position of a certain pixel in the block formed by the 9×9 neighborhood window centered on the current pixel exceeds the boundary of {R _dis (x,y)}, then the pixel The pixel value of the point is replaced by the pixel value of the nearest border pixel point; if the coordinate position of the neighboring pixel point in the 21×21 neighborhood window centered on the current pixel point exceeds {R _dis (x,y)} , the pixel value of the neighborhood pixel is replaced by the pixel value of the nearest border pixel; if each neighborhood pixel in the 21×21 neighborhood window with the current pixel as the center If the coordinate position of a certain pixel in the block formed by the ×9 neighborhood window exceeds the boundary of {R _dis (x, y)}, the pixel value of the pixel is replaced by the pixel value of the nearest boundary pixel.

中的每个像素点的特征矢量，将当前子块

中坐标位置为(x₂,y₂)的像素点的特征矢量记为

$X_{R,\mathrm{dis}}^p(x_2,y_2)=[I_{R,\mathrm{dis}}^p(x_2,y_2),|\frac{{\&PartialD;I}_{R,\mathrm{dis}}^p(x_2,y_2)}{\&PartialD;x}|,|\frac{{\&PartialD;I}_{R,\mathrm{dis}}^p(x_2,y_2)}{\&PartialD;y}|,|\frac{{\&PartialD;}^2{I}_{R,\mathrm{dis}}^p(x_2,y_2)}{{\&PartialD;x}^2}|,|\frac{{\&PartialD;}^2{I}_{R,\mathrm{dis}}^p(x_2,y_2)}{{\&PartialD;y}^2}|,x_2,y_2]$ ，其中，

的维数为7，符号“[]”为矢量表示符号，符号“||”为绝对值符号，表示当前子块

中坐标位置为(x₂,y₂)的像素点的密度值，

为

在水平方向的一阶偏导数，

为

在垂直方向的一阶偏导数，为

在水平方向的二阶偏导数，

为

在垂直方向的二阶偏导数。②-3d. Get the current sub-block

The feature vector of each pixel in the current sub-block

The feature vector of the pixel point whose coordinate position is (x ₂ , y ₂ ) is denoted as

$x_{R,\mathrm{dis}}^p(x_2,{\mathrm{the\; y}}_2)=[I_{R,\mathrm{dis}}^p(x_2,{\mathrm{the\; y}}_2),|\frac{{\&PartialD;I}_{R,\mathrm{dis}}^p(x_2,{\mathrm{the\; y}}_2)}{\&PartialD;x}|,|\frac{{\&PartialD;I}_{R,\mathrm{dis}}^p(x_2,{\mathrm{the\; y}}_2)}{\&PartialD;\mathrm{the\; y}}|,|\frac{{\&PartialD;}^2{I}_{R,\mathrm{dis}}^p(x_2,{\mathrm{the\; y}}_2)}{{\&PartialD;x}^2}|,|\frac{{\&PartialD;}^2{I}_{R,\mathrm{dis}}^p(x_2,{\mathrm{the\; y}}_2)}{{\&PartialD;\mathrm{the\; y}}^2}|,x_2,{\mathrm{the\; y}}_2]$ ,in,

The dimension is 7, the symbol “[]” is a vector representation symbol, and the symbol “||” is an absolute value symbol, represents the current subblock

The density value of the pixel point whose coordinate position is (x ₂ , y ₂ ),

for

The first partial derivative in the horizontal direction,

for

The first partial derivative in the vertical direction, for

The second partial derivative in the horizontal direction,

for

Second order partial derivatives in the vertical direction.

中的每个像素点的特征矢量，计算当前子块

的协方差矩阵，记为

$C_{R,\mathrm{dis}}^p=\frac{1}{7\&times;7-1}\underset{x_2=1}{\overset{9}{\mathrm{\&Sigma;}}}\underset{y_2=1}{\overset{9}{\mathrm{\&Sigma;}}}(X_{R,\mathrm{dis}}^p(x_2,y_2)-{\mathrm{\&mu;}}_{R,\mathrm{dis}}^p){(X_{R,\mathrm{dis}}^p(x_2,y_2)-{\mathrm{\&mu;}}_{R,\mathrm{dis}}^p)}^T,$ 其中，

的维数为7×7，

表示当前子块中的所有像素点的特征矢量的均值矢量，

为的转置矢量。②-4d, according to the current sub-block

The feature vector of each pixel in the current sub-block is calculated

The covariance matrix of is denoted as

$C_{R,\mathrm{dis}}^p=\frac{1}{7\&times;7-1}\underset{x_2=1}{\overset{9}{\mathrm{\&Sigma;}}}\underset{{\mathrm{the\; y}}_2=1}{\overset{9}{\mathrm{\&Sigma;}}}(x_{R,\mathrm{dis}}^p(x_2,{\mathrm{the\; y}}_2)-{\mathrm{\&mu;}}_{R,\mathrm{dis}}^p){(x_{R,\mathrm{dis}}^p(x_2,{\mathrm{the\; y}}_2)-{\mathrm{\&mu;}}_{R,\mathrm{dis}}^p)}^T,$ in,

The dimension of is 7×7,

represents the current subblock The mean vector of the feature vectors of all pixels in

for The transpose vector of .

的协方差矩阵进行Cholesky分解，

得到当前子块

的Sigma特征集，记为

$S_{R,\mathrm{dis}}^p=[\sqrt{10}\&times;L^{\left(1\right)},...,\sqrt{10}\&times;L^{(i\&prime;)},...,\sqrt{10}\&times;L^{\left(7\right)},-\sqrt{10}\&times;L^{\left(1\right)},...,-\sqrt{10}\&times;L^{(i\&prime;)},...,-\sqrt{10}\&times;L^{\left(7\right)},{\mathrm{\&mu;}}_{R,\mathrm{dis}}^p],$ 其中，L^T为L的转置矩阵，

的维数为7×15，符号“[]”为矢量表示符号，此处1≤i'≤7，L⁽¹⁾表示L的第1列向量，L^(i')表示L的第i'列向量，L⁽⁷⁾表示L的第7列向量。②-5d, for the current sub-block

The covariance matrix of Perform Cholesky decomposition,

get the current subblock

The Sigma feature set of is denoted as

$S_{R,\mathrm{dis}}^p=[\sqrt{10}\&times;L^{\left(1\right)},...,\sqrt{10}\&times;L^{(i\&prime;)},...,\sqrt{10}\&times;L^{\left(7\right)},-\sqrt{10}\&times;L^{\left(1\right)},...,-\sqrt{10}\&times;L^{(i\&prime;)},...,-\sqrt{10}\&times;L^{\left(7\right)},{\mathrm{\&mu;}}_{R,\mathrm{dis}}^p],$ Among them, L ^T is the transpose matrix of L,

The dimension of is 7×15, and the symbol "[]" is a vector representation symbol, where 1≤i'≤7, L ⁽¹⁾ represents the first column vector of L, and L ^(i') represents the i'th column of L Column vector, L ⁽⁷⁾ indicates the 7th column vector of L.

的Sigma特征集记为

的维数为7×15。②-6d. Using the same operation as step ②-3d to step ②-5d, obtain the Sigma feature set of the neighborhood sub-block composed of a 9×9 neighborhood window centered on each neighborhood pixel, and set

The Sigma feature set is denoted as

The dimension of is 7×15.

的Sigma特征集和以每个邻域像素点为中心的9×9邻域窗口构成的邻域子块的Sigma特征集，获取当前像素点的结构信息，记为 $I_{R,\mathrm{dis}}^{\mathrm{str}}\left(p\right),I_{R,\mathrm{dis}}^{\mathrm{str}}\left(p\right)=\frac{\underset{q\&Element;N\&prime;\left(p\right)}{\mathrm{\&Sigma;}}\exp (-\frac{{\left|\right|S_{R,\mathrm{dis}}^p-S_{R,\mathrm{dis}}^q\left|\right|}^2}{{2\mathrm{\&sigma;}}^2})\&times;L_{\mathrm{dis}}\left(q\right)}{\underset{q\&Element;N\&prime;\left(p\right)}{\mathrm{\&Sigma;}}\exp (-\frac{{\left|\right|S_{R,\mathrm{dis}}^p-S_{R,\mathrm{dis}}^q\left|\right|}^2}{{2\mathrm{\&sigma;}}^2})},$ 其中，N'(p)表示{R_dis(x,y)}中以当前像素点为中心的21×21邻域窗口内的所有邻域像素点在{R_dis(x,y)}中的坐标位置的集合，exp()表示以e为底的指数函数，e=2.71828183，σ表示高斯函数的标准差，在本实施例中取σ=0.06，符号“||||”为欧氏距离计算符号，R_dis(q)表示{R_dis(x,y)}中坐标位置为q的像素点的像素值。②-7d, according to the current sub-block

The Sigma feature set and the Sigma feature set of the neighborhood sub-block composed of a 9×9 neighborhood window centered on each neighborhood pixel to obtain the structural information of the current pixel, denoted as $I_{R,\mathrm{dis}}^{\mathrm{str}}\left(p\right),I_{R,\mathrm{dis}}^{\mathrm{str}}\left(p\right)=\frac{\underset{q\&Element;N\&prime;\left(p\right)}{\mathrm{\&Sigma;}}\exp (-\frac{{\left|\right|S_{R,\mathrm{dis}}^p-S_{R,\mathrm{dis}}^q\left|\right|}^2}{{2\mathrm{\&sigma;}}^2})\&times;L_{\mathrm{dis}}\left(q\right)}{\underset{q\&Element;N\&prime;\left(p\right)}{\mathrm{\&Sigma;}}\exp (-\frac{{\left|\right|S_{R,\mathrm{dis}}^p-S_{R,\mathrm{dis}}^q\left|\right|}^2}{{2\mathrm{\&sigma;}}^2})},$ Among them, N'(p) means that all the neighboring pixels in {R _dis (x, y)} in the 21×21 neighborhood window centered on the current pixel in {R _dis (x, y)} A collection of coordinate positions, exp() represents an exponential function with e as the base, e=2.71828183, σ represents the standard deviation of the Gaussian function, in this embodiment, σ=0.06, and the symbol "||||" is the Euclidean distance Calculation symbol, R _dis (q) represents the pixel value of the pixel whose coordinate position is q in {R _dis (x,y)}.

获取当前像素点的纹理信息，记为 $I_{R,\mathrm{dis}}^{\mathrm{tex}}\left(p\right),I_{R,\mathrm{dis}}^{\mathrm{tex}}\left(p\right)=R_{\mathrm{dis}}\left(p\right)-I_{R,\mathrm{dis}}^{\mathrm{str}}\left(p\right),$ 其中，R_dis(p)表示当前像素点的像素值。②-8d, according to the structure information of the current pixel

Get the texture information of the current pixel point, denoted as $I_{R,\mathrm{dis}}^{\mathrm{tex}}\left(p\right),I_{R,\mathrm{dis}}^{\mathrm{tex}}\left(p\right)=R_{\mathrm{dis}}\left(p\right)-I_{R,\mathrm{dis}}^{\mathrm{str}}\left(p\right),$ Wherein, R _dis (p) represents the pixel value of the current pixel point.

②-9d. Set the next pixel to be processed in {R _dis (x, y)} as the current pixel, and then return to step ②-2d to continue until all pixels in {R _dis (x, y)} Point processing is completed, and the structure information and texture information of each pixel in {R _dis (x, y)} are obtained, which is composed of the structure information of all pixels in {R _dis (x, y)} {R _dis ( The structural image of x,y)}, denoted as The texture image of {R _dis (x, y)} is composed of the texture information of all pixels in {R _dis (x, y)}, recorded as

中的每个像素点与

中对应像素点之间的梯度相似性，将中坐标位置为(x,y)的像素点与

中坐标位置为(x,y)的像素点之间的梯度相似性记为

$Q_L^{\mathrm{str}}(x,y)=\frac{2\&times;m_{L,\mathrm{org}}^{\mathrm{str}}(x,y)\&times;m_{L,\mathrm{dis}}^{\mathrm{str}}(x,y)+C_1}{{\left(m_{L,\mathrm{org}}^{\mathrm{str}}(x,y)\right)}^2+{\left(m_{L,\mathrm{dis}}^{\mathrm{str}}(x,y)\right)}^2+C_1},$ 其中， $m_{L,\mathrm{org}}^{\mathrm{str}}(x,y)=\sqrt{{\left({\mathrm{gx}}_{L,\mathrm{org}}^{\mathrm{str}}(x,y)\right)}^2+{\left({\mathrm{gy}}_{L,\mathrm{org}}^{\mathrm{str}}(x,y)\right)}^2},$ $m_{L,\mathrm{dis}}^{\mathrm{str}}(x,y)=\sqrt{{\left({\mathrm{gx}}_{L,\mathrm{dis}}^{\mathrm{str}}(x,y)\right)}^2+{\left({\mathrm{gy}}_{L,\mathrm{dis}}^{\mathrm{str}}(x,y)\right)}^2},$

表示

中坐标位置为(x,y)的像素点的水平方向梯度，

表示

中坐标位置为(x,y)的像素点的垂直方向梯度，

表示

中坐标位置为(x,y)的像素点的水平方向梯度，

表示

中坐标位置为(x,y)的像素点的垂直方向梯度，C₁为控制参数，在本实施例中取C₁=0.0026；然后根据

中的每个像素点与

中对应像素点之间的梯度相似性，计算

的图像质量客观评价预测值，记为

$Q_L^{\mathrm{str}}=\frac{\underset{x=1}{\overset{W}{\mathrm{\&Sigma;}}}\underset{y=1}{\overset{H}{\mathrm{\&Sigma;}}}{Q}_L^{\mathrm{str}}(x,y)}{W\&times;H}.$ ③Compared with the original image, the structural image is more stable because the detailed information such as texture is separated from the original image, so the method of the present invention calculates

Each pixel in

The gradient similarity between the corresponding pixels in the The pixel point with the middle coordinate position (x, y) and

The gradient similarity between pixels whose coordinate positions are (x, y) is recorded as

$Q_L^{\mathrm{str}}(x,\mathrm{the\; y})=\frac{2\&times;m_{L,\mathrm{org}}^{\mathrm{str}}(x,\mathrm{the\; y})\&times;m_{L,\mathrm{dis}}^{\mathrm{str}}(x,\mathrm{the\; y})+C_1}{{\left(m_{L,\mathrm{org}}^{\mathrm{str}}(x,\mathrm{the\; y})\right)}^2+{\left(m_{L,\mathrm{dis}}^{\mathrm{str}}(x,\mathrm{the\; y})\right)}^2+C_1},$ in, $m_{L,\mathrm{org}}^{\mathrm{str}}(x,\mathrm{the\; y})=\sqrt{{\left({\mathrm{gx}}_{L,\mathrm{org}}^{\mathrm{str}}(x,\mathrm{the\; y})\right)}^2+{\left({\mathrm{gy}}_{L,\mathrm{org}}^{\mathrm{str}}(x,\mathrm{the\; y})\right)}^2},$ $m_{L,\mathrm{dis}}^{\mathrm{str}}(x,\mathrm{the\; y})=\sqrt{{\left({\mathrm{gx}}_{L,\mathrm{dis}}^{\mathrm{str}}(x,\mathrm{the\; y})\right)}^2+{\left({\mathrm{gy}}_{L,\mathrm{dis}}^{\mathrm{str}}(x,\mathrm{the\; y})\right)}^2},$

express

The horizontal direction gradient of the pixel point whose middle coordinate position is (x, y),

express

The vertical direction gradient of the pixel point whose middle coordinate position is (x, y),

express

The horizontal direction gradient of the pixel point whose middle coordinate position is (x, y),

express

Middle coordinate position is the vertical direction gradient of the pixel point of (x, y), C ₁ is control parameter, gets C ₁ =0.0026 in the present embodiment; Then according to

Each pixel in

The gradient similarity between the corresponding pixels in the calculation

The predicted value of the objective evaluation of image quality is denoted as

$Q_L^{\mathrm{str}}=\frac{\underset{x=1}{\overset{W}{\mathrm{\&Sigma;}}}\underset{\mathrm{the\; y}=1}{\overset{h}{\mathrm{\&Sigma;}}}{Q}_L^{\mathrm{str}}(x,\mathrm{the\; y})}{W\&times;h}.$

中对应像素点之间的梯度相似性，将中坐标位置为(x,y)的像素点与

中坐标位置为(x,y)的像素点之间的梯度相似性记为

$Q_R^{\mathrm{str}}(x,y)=\frac{2\&times;m_{R,\mathrm{org}}^{\mathrm{str}}(x,y)\&times;m_{R,\mathrm{dis}}^{\mathrm{str}}(x,y)+C_1}{{\left(m_{R,\mathrm{org}}^{\mathrm{str}}(x,y)\right)}^2+{\left(m_{R,\mathrm{dis}}^{\mathrm{str}}(x,y)\right)}^2+C_1},$ 其中， $m_{R,\mathrm{org}}^{\mathrm{str}}(x,y)=\sqrt{{\left({\mathrm{gx}}_{R,\mathrm{org}}^{\mathrm{str}}(x,y)\right)}^2+{\left({\mathrm{gy}}_{R,\mathrm{org}}^{\mathrm{str}}(x,y)\right)}^2},$ $m_{R,\mathrm{dis}}^{\mathrm{str}}(x,y)=\sqrt{{\left({\mathrm{gx}}_{R,\mathrm{dis}}^{\mathrm{str}}(x,y)\right)}^2+{\left({\mathrm{gy}}_{R,\mathrm{dis}}^{\mathrm{str}}(x,y)\right)}^2},$

表示

中坐标位置为(x,y)的像素点的水平方向梯度，

表示

中坐标位置为(x,y)的像素点的垂直方向梯度，表示

中坐标位置为(x,y)的像素点的水平方向梯度，

表示

中坐标位置为(x,y)的像素点的垂直方向梯度，C₁为控制参数，在本实施例中取C₁=0.0026；然后根据

中的每个像素点与

中对应像素点之间的梯度相似性，计算

的图像质量客观评价预测值，记为

$Q_R^{\mathrm{str}}=\frac{\underset{x=1}{\overset{W}{\mathrm{\&Sigma;}}}\underset{y=1}{\overset{H}{\mathrm{\&Sigma;}}}{Q}_R^{\mathrm{str}}(x,y)}{W\&times;H}.$ Similarly, calculate Each pixel in

The gradient similarity between the corresponding pixels in the The pixel point with the middle coordinate position (x, y) and

The gradient similarity between pixels whose coordinate positions are (x, y) is recorded as

$Q_R^{\mathrm{str}}(x,\mathrm{the\; y})=\frac{2\&times;m_{R,\mathrm{org}}^{\mathrm{str}}(x,\mathrm{the\; y})\&times;m_{R,\mathrm{dis}}^{\mathrm{str}}(x,\mathrm{the\; y})+C_1}{{\left(m_{R,\mathrm{org}}^{\mathrm{str}}(x,\mathrm{the\; y})\right)}^2+{\left(m_{R,\mathrm{dis}}^{\mathrm{str}}(x,\mathrm{the\; y})\right)}^2+C_1},$ in, $m_{R,\mathrm{org}}^{\mathrm{str}}(x,\mathrm{the\; y})=\sqrt{{\left({\mathrm{gx}}_{R,\mathrm{org}}^{\mathrm{str}}(x,\mathrm{the\; y})\right)}^2+{\left({\mathrm{gy}}_{R,\mathrm{org}}^{\mathrm{str}}(x,\mathrm{the\; y})\right)}^2},$ $m_{R,\mathrm{dis}}^{\mathrm{str}}(x,\mathrm{the\; y})=\sqrt{{\left({\mathrm{gx}}_{R,\mathrm{dis}}^{\mathrm{str}}(x,\mathrm{the\; y})\right)}^2+{\left({\mathrm{gy}}_{R,\mathrm{dis}}^{\mathrm{str}}(x,\mathrm{the\; y})\right)}^2},$

express

The horizontal direction gradient of the pixel point whose middle coordinate position is (x, y),

express

The vertical direction gradient of the pixel point whose middle coordinate position is (x, y), express

The horizontal direction gradient of the pixel point whose middle coordinate position is (x, y),

express

Middle coordinate position is the vertical direction gradient of the pixel point of (x, y), C ₁ is control parameter, gets C ₁ =0.0026 in the present embodiment; Then according to

Each pixel in

The gradient similarity between the corresponding pixels in the calculation

The predicted value of objective evaluation of image quality is denoted as

$Q_R^{\mathrm{str}}=\frac{\underset{x=1}{\overset{W}{\mathrm{\&Sigma;}}}\underset{\mathrm{the\; y}=1}{\overset{h}{\mathrm{\&Sigma;}}}{Q}_R^{\mathrm{str}}(x,\mathrm{the\; y})}{W\&times;h}.$

的图像质量客观评价预测值，记为

4. Because the mean value and standard deviation information can evaluate the change of image detail information well, so the method of the present invention obtains Each sub-block of size 8×8 in The structural similarity between the sub-blocks corresponding to the size of 8×8 in , is calculated as

The predicted value of the objective evaluation of image quality is denoted as

的图像质量客观评价预测值

的获取过程为：In this specific embodiment, in step ④

The predictive value of the image quality objective evaluation

The acquisition process is:

和

划分成

个互不重叠的尺寸大小为8×8的子块，将中当前待处理的第k个子块定义为当前第一子块，将

中当前待处理的第k个子块定义为当前第二子块，其中，

k的初始值为1。④-1a, respectively

and

divided into

Non-overlapping sub-blocks of size 8×8, the The kth sub-block currently to be processed in is defined as the current first sub-block, and the

The kth sub-block currently to be processed in is defined as the current second sub-block, where,

The initial value of k is 1.

其中，(x₄,y₄)表示

和中的像素点的坐标位置，1≤x₄≤8,1≤y₄≤8，

表示

中坐标位置为(x₄,y₄)的像素点的像素值，

表示中坐标位置为(x₄,y₄)的像素点的像素值。④-2a. Record the current first sub-block as Record the current second sub-block as

Among them, (x ₄ ,y ₄ ) means

and The coordinate position of the pixel in , 1≤x ₄ ≤8, 1≤y ₄ ≤8,

express

The pixel value of the pixel point whose middle coordinate position is (x ₄ , y ₄ ),

express The pixel value of the pixel point whose middle coordinate position is (x ₄ , y ₄ ).

的均值和标准差，对应记为

和

${\mathrm{\&mu;}}_{L_{\mathrm{org}},k}=\frac{\underset{y_4=1}{\overset{8}{\mathrm{\&Sigma;}}}\underset{x_4=1}{\overset{8}{\mathrm{\&Sigma;}}}{f}_{L_{\mathrm{org}},k}(x_4,y_4)}{64},{\mathrm{\&sigma;}}_{L_{\mathrm{org}},k}=\sqrt{\frac{\underset{y_4=1}{\overset{8}{\mathrm{\&Sigma;}}}\underset{x_4=1}{\overset{8}{\mathrm{\&Sigma;}}}{(f_{L_{\mathrm{org}},k}(x_4,y_4)-{\mathrm{\&mu;}}_{L_{\mathrm{org}},k})}^2}{64}}.$ ④-3a. Calculate the current first sub-block

The mean and standard deviation of , corresponding to

and

${\mathrm{\&mu;}}_{L_{\mathrm{org}},k}=\frac{\underset{{\mathrm{the\; y}}_4=1}{\overset{8}{\mathrm{\&Sigma;}}}\underset{x_4=1}{\overset{8}{\mathrm{\&Sigma;}}}{f}_{L_{\mathrm{org}},k}(x_4,{\mathrm{the\; y}}_4)}{64},{\mathrm{\&sigma;}}_{L_{\mathrm{org}},k}=\sqrt{\frac{\underset{{\mathrm{the\; y}}_4=1}{\overset{8}{\mathrm{\&Sigma;}}}\underset{x_4=1}{\overset{8}{\mathrm{\&Sigma;}}}{(f_{L_{\mathrm{org}},k}(x_4,{\mathrm{the\; y}}_4)-{\mathrm{\&mu;}}_{L_{\mathrm{org}},k})}^2}{64}}.$

的均值和标准差，对应记为

和 ${\mathrm{\&mu;}}_{L_{\mathrm{dis}},k}=\frac{\underset{y_4=1}{\overset{8}{\mathrm{\&Sigma;}}}\underset{x_4=1}{\overset{8}{\mathrm{\&Sigma;}}}{f}_{L_{\mathrm{dis}},k}(x_4,y_4)}{64},{\mathrm{\&sigma;}}_{L_{\mathrm{dis}},k}=\sqrt{\frac{\underset{y_4=1}{\overset{8}{\mathrm{\&Sigma;}}}\underset{x_4=1}{\overset{8}{\mathrm{\&Sigma;}}}{(f_{L_{\mathrm{dis}},k}(x_4,y_4)-{\mathrm{\&mu;}}_{L_{\mathrm{dis}},k})}^2}{64}}.$ Similarly, calculate the current second sub-block

The mean and standard deviation of , corresponding to

and ${\mathrm{\&mu;}}_{L_{\mathrm{dis}},k}=\frac{\underset{{\mathrm{the\; y}}_4=1}{\overset{8}{\mathrm{\&Sigma;}}}\underset{x_4=1}{\overset{8}{\mathrm{\&Sigma;}}}{f}_{L_{\mathrm{dis}},k}(x_4,{\mathrm{the\; y}}_4)}{64},{\mathrm{\&sigma;}}_{L_{\mathrm{dis}},k}=\sqrt{\frac{\underset{{\mathrm{the\; y}}_4=1}{\overset{8}{\mathrm{\&Sigma;}}}\underset{x_4=1}{\overset{8}{\mathrm{\&Sigma;}}}{(f_{L_{\mathrm{dis}},k}(x_4,{\mathrm{the\; y}}_4)-{\mathrm{\&mu;}}_{L_{\mathrm{dis}},k})}^2}{64}}.$

与当前第二子块

之间的结构相似度，记为

$Q_{L,k}^{\mathrm{tex}}=\frac{4\&times;({\mathrm{\&sigma;}}_{L_{\mathrm{org}},k}\&times;{\mathrm{\&sigma;}}_{L_{\mathrm{dis}},k})\&times;({\mathrm{\&mu;}}_{L_{\mathrm{org}},k}\&times;{\mathrm{\&mu;}}_{L_{\mathrm{dis}},k})+C_2}{({\left({\mathrm{\&sigma;}}_{L_{\mathrm{org}},k}\right)}^2+{\left({\mathrm{\&sigma;}}_{L_{\mathrm{dis}},k}\right)}^2)+({\left({\mathrm{\&mu;}}_{L_{\mathrm{org}},k}\right)}^2+{\left({\mathrm{\&mu;}}_{L_{\mathrm{dis}},k}\right)}^2)+C_2},$ 其中，C₂为控制参数，在本实施例中取C₂=0.85。④-4a. Calculate the current first sub-block

with the current second subblock

The structural similarity between

$Q_{L,k}^{\mathrm{tex}}=\frac{4\&times;({\mathrm{\&sigma;}}_{L_{\mathrm{org}},k}\&times;{\mathrm{\&sigma;}}_{L_{\mathrm{dis}},k})\&times;({\mathrm{\&mu;}}_{L_{\mathrm{org}},k}\&times;{\mathrm{\&mu;}}_{L_{\mathrm{dis}},k})+C_2}{({\left({\mathrm{\&sigma;}}_{L_{\mathrm{org}},k}\right)}^2+{\left({\mathrm{\&sigma;}}_{L_{\mathrm{dis}},k}\right)}^2)+({\left({\mathrm{\&mu;}}_{L_{\mathrm{org}},k}\right)}^2+{\left({\mathrm{\&mu;}}_{L_{\mathrm{dis}},k}\right)}^2)+C_2},$ Wherein, C ₂ is a control parameter, and in this embodiment, C ₂ =0.85.

中下一个待处理的子块作为当前第一子块，将

中下一个待处理的子块作为当前第二子块，然后返回步骤④-2a继续执行，直至

和

中的所有子块均处理完毕，得到

中的每个子块与

中对应子块之间的结构相似度，其中，k=k+1中的“=”为赋值符号。④-5a, let k=k+1, the

In the next sub-block to be processed as the current first sub-block, the

The next sub-block to be processed is used as the current second sub-block, and then returns to step ④-2a to continue until

and

All sub-blocks in are processed, resulting in

Each subblock in

The structural similarity between corresponding sub-blocks in k=k+1, where "=" in k=k+1 is an assignment symbol.

中的每个子块与

中对应子块之间的结构相似度，计算

的图像质量客观评价预测值，记为

④-6a, according to

Each subblock in

The structural similarity between the corresponding sub-blocks in the calculation

The predicted value of objective evaluation of image quality is denoted as

中的每个尺寸大小为8×8的子块与

中对应尺寸大小为8×8的子块之间的结构相似度，计算得到

的图像质量客观评价预测值，记为

Likewise, by getting

Each sub-block of size 8×8 in

The structural similarity between the sub-blocks corresponding to the size of 8×8 in , is calculated as

The predicted value of the objective evaluation of image quality is denoted as

的图像质量客观评价预测值

的获取过程为：In this specific embodiment, in the described step ④

The predictive value of the image quality objective evaluation

The acquisition process is:

和

划分成

个互不重叠的尺寸大小为8×8的子块，将

中当前待处理的第k个子块定义为当前第一子块，将

中当前待处理的第k个子块定义为当前第二子块，其中，

k的初始值为1。④-1b, respectively

and

divided into

Non-overlapping sub-blocks of size 8×8, the

The kth sub-block currently to be processed in is defined as the current first sub-block, and the

The kth sub-block currently to be processed in is defined as the current second sub-block, where,

The initial value of k is 1.

将当前第二子块记为

其中，(x₄,y₄)表示和

中的像素点的坐标位置，1≤x₄≤8,1≤y₄≤8，

表示

中坐标位置为(x₄,y₄)的像素点的像素值，

表示

中坐标位置为(x₄,y₄)的像素点的像素值。④-2b. Record the current first sub-block as

Record the current second sub-block as

Among them, (x ₄ ,y ₄ ) means and

The coordinate position of the pixel in , 1≤x ₄ ≤8, 1≤y ₄ ≤8,

express

The pixel value of the pixel point whose middle coordinate position is (x ₄ , y ₄ ),

express

The pixel value of the pixel point whose middle coordinate position is (x ₄ , y ₄ ).

的均值和标准差，对应记为

和

${\mathrm{\&mu;}}_{R_{\mathrm{org}},k}=\frac{\underset{y_4=1}{\overset{8}{\mathrm{\&Sigma;}}}\underset{x_4=1}{\overset{8}{\mathrm{\&Sigma;}}}{f}_{R_{\mathrm{org}},k}(x_4,y_4)}{64},{\mathrm{\&sigma;}}_{R_{\mathrm{org}},k}=\sqrt{\frac{\underset{y_4=1}{\overset{8}{\mathrm{\&Sigma;}}}\underset{x_4=1}{\overset{8}{\mathrm{\&Sigma;}}}{(f_{R_{\mathrm{org}},k}(x_4,y_4)-{\mathrm{\&mu;}}_{R_{\mathrm{org}},k})}^2}{64}};$ ④-3b. Calculate the current first sub-block

The mean and standard deviation of , corresponding to

and

${\mathrm{\&mu;}}_{R_{\mathrm{org}},k}=\frac{\underset{{\mathrm{the\; y}}_4=1}{\overset{8}{\mathrm{\&Sigma;}}}\underset{x_4=1}{\overset{8}{\mathrm{\&Sigma;}}}{f}_{R_{\mathrm{org}},k}(x_4,{\mathrm{the\; y}}_4)}{64},{\mathrm{\&sigma;}}_{R_{\mathrm{org}},k}=\sqrt{\frac{\underset{{\mathrm{the\; y}}_4=1}{\overset{8}{\mathrm{\&Sigma;}}}\underset{x_4=1}{\overset{8}{\mathrm{\&Sigma;}}}{(f_{R_{\mathrm{org}},k}(x_4,{\mathrm{the\; y}}_4)-{\mathrm{\&mu;}}_{R_{\mathrm{org}},k})}^2}{64}};$

的均值和标准差，对应记为

和

${\mathrm{\&mu;}}_{R_{\mathrm{dis}},k}=\frac{\underset{y_4=1}{\overset{8}{\mathrm{\&Sigma;}}}\underset{x_4=1}{\overset{8}{\mathrm{\&Sigma;}}}{f}_{R_{\mathrm{dis}},k}(x_4,y_4)}{64},{\mathrm{\&sigma;}}_{R_{\mathrm{dis}},k}=\sqrt{\frac{\underset{y_4=1}{\overset{8}{\mathrm{\&Sigma;}}}\underset{x_4=1}{\overset{8}{\mathrm{\&Sigma;}}}{(f_{R_{\mathrm{dis}},k}(x_4,y_4)-{\mathrm{\&mu;}}_{R_{\mathrm{dis}},k})}^2}{64}}.$ Similarly, calculate the current second sub-block

The mean and standard deviation of , corresponding to

and

${\mathrm{\&mu;}}_{R_{\mathrm{dis}},k}=\frac{\underset{{\mathrm{the\; y}}_4=1}{\overset{8}{\mathrm{\&Sigma;}}}\underset{x_4=1}{\overset{8}{\mathrm{\&Sigma;}}}{f}_{R_{\mathrm{dis}},k}(x_4,{\mathrm{the\; y}}_4)}{64},{\mathrm{\&sigma;}}_{R_{\mathrm{dis}},k}=\sqrt{\frac{\underset{{\mathrm{the\; y}}_4=1}{\overset{8}{\mathrm{\&Sigma;}}}\underset{x_4=1}{\overset{8}{\mathrm{\&Sigma;}}}{(f_{R_{\mathrm{dis}},k}(x_4,{\mathrm{the\; y}}_4)-{\mathrm{\&mu;}}_{R_{\mathrm{dis}},k})}^2}{64}}.$

之间的结构相似度，记为

$Q_{R,k}^{\mathrm{tex}}=\frac{4\&times;({\mathrm{\&sigma;}}_{R_{\mathrm{org}},k}\&times;{\mathrm{\&sigma;}}_{R_{\mathrm{dis}},k})\&times;({\mathrm{\&mu;}}_{R_{\mathrm{org}},k}\&times;{\mathrm{\&mu;}}_{R_{\mathrm{dis}},k})+C_2}{({\left({\mathrm{\&sigma;}}_{R_{\mathrm{org}},k}\right)}^2+{\left({\mathrm{\&sigma;}}_{R_{\mathrm{dis}},k}\right)}^2)+({\left({\mathrm{\&mu;}}_{R_{\mathrm{org}},k}\right)}^2+{\left({\mathrm{\&mu;}}_{R_{\mathrm{dis}},k}\right)}^2)+C_2},$ 其中，C₂为控制参数，在本实施例中取C₂=0.85。④-4b. Calculate the current first sub-block with the current second subblock

The structural similarity between

$Q_{R,k}^{\mathrm{tex}}=\frac{4\&times;({\mathrm{\&sigma;}}_{R_{\mathrm{org}},k}\&times;{\mathrm{\&sigma;}}_{R_{\mathrm{dis}},k})\&times;({\mathrm{\&mu;}}_{R_{\mathrm{org}},k}\&times;{\mathrm{\&mu;}}_{R_{\mathrm{dis}},k})+C_2}{({\left({\mathrm{\&sigma;}}_{R_{\mathrm{org}},k}\right)}^2+{\left({\mathrm{\&sigma;}}_{R_{\mathrm{dis}},k}\right)}^2)+({\left({\mathrm{\&mu;}}_{R_{\mathrm{org}},k}\right)}^2+{\left({\mathrm{\&mu;}}_{R_{\mathrm{dis}},k}\right)}^2)+C_2},$ Wherein, C ₂ is a control parameter, and in this embodiment, C ₂ =0.85.

中下一个待处理的子块作为当前第一子块，将中下一个待处理的子块作为当前第二子块，然后返回步骤④-2b继续执行，直至

和

中的所有子块均处理完毕，得到

中的每个子块与

中对应子块之间的结构相似度，其中，k=k+1中的“=”为赋值符号。④-5b, let k=k+1, the

In the next sub-block to be processed as the current first sub-block, the The next sub-block to be processed is used as the current second sub-block, and then returns to step ④-2b to continue until

and

All sub-blocks in are processed, resulting in

Each subblock in

The structural similarity between corresponding sub-blocks in k=k+1, where "=" in k=k+1 is an assignment symbol.

中的每个子块与

中对应子块之间的结构相似度，计算

的图像质量客观评价预测值，记为

④-6b, according to

Each subblock in

The structural similarity between the corresponding sub-blocks in the calculation

The predicted value of the objective evaluation of image quality is denoted as

和

进行融合，得到S_dis的结构图像的图像质量客观评价预测值，记为Q_str，

其中，w_s表示

和

的权值比重，在本实施例中，对于宁波大学立体图像库，取w_s=0.980；对于LIVE立体图像库，取w_s=0.629。⑤ right

and

Fusion is carried out to obtain the image quality objective evaluation prediction value of the structural image of S _dis , denoted as Q _str ,

Among them, w _s means

and

In this embodiment, w _s =0.980 for the Ningbo University stereoscopic image database; w _s =0.629 for the LIVE stereoscopic image database.

进行融合，得到S_dis的纹理图像的图像质量客观评价预测值，记为Q_tex，

其中，w_t表示

和

的权值比重，在本实施例中，对于宁波大学立体图像库，取w_t=0.888；对于LIVE立体图像库，取w_t=0.503。same, yes and

Perform fusion to obtain the image quality objective evaluation prediction value of the texture image of S _dis , denoted as Q _tex ,

Among them, w _t means

and

In this embodiment, for the stereoscopic image database of Ningbo University, w _t =0.888; for the LIVE stereoscopic image database, w _t =0.503.

⑥Fuse Q _str and Q _tex to obtain the predicted value of S _dis image quality objective evaluation, denoted as Q, Q=w×Q _str +(1-w)×Q _tex , where w represents Q _str and S _dis In this embodiment, w=0.882 is used for the stereoscopic image library of Ningbo University; w=0.838 is used for the LIVE stereoscopic image library.

Here, four commonly used objective parameters for evaluating image quality evaluation methods are used as evaluation indicators, namely Pearson correlation coefficient (Pearson linear correlation coefficient, PLCC) and Spearman correlation coefficient (Spearman rank order correlation coefficient, SROCC) under nonlinear regression conditions. Kendall correlation coefficient (Kendall rank-order correlation coefficient, KROCC), mean square error (root mean squared error, RMSE), PLCC and RMSE reflect the accuracy of the objective evaluation results of the distorted stereoscopic image, SROCC and KROCC reflect its monotonicity.

Utilize the method of the present invention to calculate the image quality objective evaluation prediction value of each distorted stereoscopic image in the stereoscopic image database of Ningbo University and the image quality objective evaluation prediction value of each distorted stereoscopic image in the LIVE stereoscopic image database, and then use the existing The subjective evaluation method obtained the average subjective score difference of each distorted stereo image in the stereo image database of Ningbo University and the average subjective score difference of each distorted stereo image in the LIVE stereo image database. The five-parameter Logistic function nonlinear fitting is done on the image quality objective evaluation prediction value of the distorted stereoscopic image calculated by the method of the present invention, the higher the PLCC, SROCC and KROCC values, the lower the RMSE value shows that the objective evaluation method and the average subjective rating The better the difference correlation. Table 1, table 2, table 3 and table 4 provide the Pearson correlation coefficient, Spearman correlation coefficient, Kendall correlation between the image quality objective evaluation prediction value and the average subjective rating difference of the distorted stereoscopic image obtained by the method of the present invention Coefficient and mean square error. As can be seen from Table 1, Table 2, Table 3 and Table 4, the correlation between the final image quality objective evaluation prediction value and the average subjective rating difference of the distorted stereoscopic image obtained by the method of the present invention is very high It shows that the objective evaluation result is relatively consistent with the subjective perception result of human eyes, which is enough to illustrate the effectiveness of the method of the present invention.

Fig. 2 has provided the scatter plot of the image quality objective evaluation prediction value and the average subjective rating difference of each distorted stereo image in the Ningbo University stereo image storehouse that utilizes the method of the present invention to obtain, and Fig. 3 has provided the scatter diagram utilizing the present invention The scatter diagram of the difference between the image quality objective evaluation prediction value and the average subjective evaluation value of each distorted stereo image in the LIVE stereo image database obtained by the method, the more concentrated the scatter points, the better the consistency between the objective evaluation results and the subjective perception . It can be seen from Fig. 2 and Fig. 3 that the scatter diagram obtained by the method of the present invention is relatively concentrated, and has a high degree of agreement with the subjective evaluation data.

Table 1 utilizes the image quality objective evaluation prediction value and the average subjective evaluation of the distorted stereoscopic image that the inventive method obtains

Comparison of the Pearson correlation coefficient between the difference values

Table 2 utilizes Spearman's correlation coefficient comparison between the image quality objective evaluation prediction value of the distorted stereoscopic image obtained by the method of the present invention and the average subjective rating difference

Table 3 utilizes the Kendall correlation coefficient comparison between the image quality objective evaluation prediction value of the distorted stereoscopic image obtained by the method of the present invention and the average subjective rating difference

Table 4 compares the mean square error between the image quality objective evaluation prediction value and the average subjective rating difference of the distorted stereoscopic image obtained by the method of the present invention

Claims (4)

1. A three-dimensional image quality objective evaluation method based on structure texture separation is characterized in that the processing process is as follows:

firstly, respectively implementing structure texture separation on a left viewpoint image and a right viewpoint image of an original undistorted stereo image and a left viewpoint image and a right viewpoint image of a distorted stereo image to be evaluated to obtain respective structure images and texture images;

secondly, obtaining an objective evaluation prediction value of the image quality of the structural image of the left viewpoint image of the distorted stereo image to be evaluated by calculating the gradient similarity between each pixel point in the structural image of the left viewpoint image of the original undistorted stereo image and the corresponding pixel point in the structural image of the left viewpoint image of the distorted stereo image to be evaluated; similarly, obtaining an objective evaluation prediction value of the image quality of the structural image of the right viewpoint image of the distorted stereo image to be evaluated by calculating the gradient similarity between each pixel point in the structural image of the right viewpoint image of the original undistorted stereo image and the corresponding pixel point in the structural image of the right viewpoint image of the distorted stereo image to be evaluated;

secondly, obtaining an objective image quality evaluation prediction value of the texture image of the left viewpoint image of the distorted stereo image to be evaluated by calculating the structural similarity between each subblock with the size of 8 multiplied by 8 in the texture image of the left viewpoint image of the original undistorted stereo image and the subblock with the corresponding size of 8 multiplied by 8 in the texture image of the left viewpoint image of the distorted stereo image to be evaluated; similarly, obtaining an objective evaluation prediction value of the image quality of the texture image of the right viewpoint image of the distorted stereo image to be evaluated by calculating the structural similarity between each subblock with the size of 8 × 8 in the texture image of the right viewpoint image of the original undistorted stereo image and the subblock with the corresponding size of 8 × 8 in the texture image of the right viewpoint image of the distorted stereo image to be evaluated;

thirdly, fusing the image quality objective evaluation predicted values of the structural images of the left viewpoint image and the right viewpoint image of the distorted three-dimensional image to be evaluated to obtain the image quality objective evaluation predicted value of the structural image of the distorted three-dimensional image to be evaluated; similarly, fusing the image quality objective evaluation predicted values of the texture images of the left viewpoint image and the right viewpoint image of the distorted three-dimensional image to be evaluated to obtain the image quality objective evaluation predicted value of the texture image of the distorted three-dimensional image to be evaluated;

and finally, fusing the image quality objective evaluation predicted value of the structural image and the texture image of the distorted three-dimensional image to be evaluated to obtain the image quality objective evaluation predicted value of the distorted three-dimensional image to be evaluated.

2. The objective evaluation method for stereo image quality based on structure texture separation according to claim 1, characterized in that it comprises the following steps:

making S_orgRepresenting the original undistorted stereo image, let S_disA stereoscopic image representing distortion to be evaluated, S_orgIs noted as { L_org(x, y) }, adding S_orgIs noted as { R_org(x, y) }, adding S_disIs noted as { L_dis(x, y) }, adding S_disIs noted as { R_dis(x, y) }, wherein (x, y) denotes a coordinate position of a pixel point in the left viewpoint image and the right viewpoint image, x is 1. ltoreq. x.ltoreq.W, y is 1. ltoreq. y.ltoreq.H, W denotes a width of the left viewpoint image and the right viewpoint image, H denotes a height of the left viewpoint image and the right viewpoint image, L is L_org(x, y) represents { L }_orgThe coordinate position in (x, y) } is the pixel value of the pixel point with (x, y), R_org(x, y) represents { R_orgThe pixel value L of the pixel point with the coordinate position (x, y) in (x, y) } is_dis(x, y) represents { L }_disThe coordinate position in (x, y) } is the pixel value of the pixel point with (x, y), R_dis(x, y) represents { R_disThe coordinate position in (x, y) is the pixel value of the pixel point of (x, y);

② are respectively paired with { L_org(x,y)}、{R_org(x,y)}、{L_dis(x, y) } and { R }_dis(x, y) } structural texture separation to obtain { L }_org(x,y)}、{R_org(x,y)}、{L_dis(x, y) } and { R }_dis(x, y) } respective structural and texture images, will { L_org(x, y) } structural and texture image correspondences are notedAnd

will { R_org(x, y) } structural and texture image correspondences are noted

Andwill { L_dis(x, y) } structural and texture image correspondences are noted

And

will { R_dis(x, y) } structural image and texture image correspondenceAnd

wherein,to represent

The middle coordinate position is the pixel value of the pixel point of (x, y),to representThe middle coordinate position is the pixel value of the pixel point of (x, y),

to represent

The middle coordinate position is the pixel value of the pixel point of (x, y),to represent

The middle coordinate position is the pixel value of the pixel point of (x, y),

to represent

The middle coordinate position is the pixel value of the pixel point of (x, y),

to represent

The middle coordinate position is the pixel value of the pixel point of (x, y),

to represent

The middle coordinate position is the pixel value of the pixel point of (x, y),

to represent

The middle coordinate position is the pixel value of the pixel point of (x, y);

calculating

Each pixel point in (1)

The gradient similarity between the corresponding pixel points will be

The pixel point with the (x, y) coordinate position andthe gradient similarity between pixel points with (x, y) as the middle coordinate position is recorded as

<math> <mrow> <msubsup> <mi>Q</mi> <mi>L</mi> <mi>str</mi> </msubsup> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <mn>2</mn> <mo>&times;</mo> <msubsup> <mi>m</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>org</mi> </mrow> <mi>str</mi> </msubsup> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>&times;</mo> <msubsup> <mi>m</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>dis</mi> </mrow> <mi>str</mi> </msubsup> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>C</mi> <mn>1</mn> </msub> </mrow> <mrow> <msup> <mrow> <mo>(</mo> <msubsup> <mi>m</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>org</mi> </mrow> <mi>str</mi> </msubsup> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <msubsup> <mi>m</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>dis</mi> </mrow> <mi>str</mi> </msubsup> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msub> <mi>C</mi> <mn>1</mn> </msub> </mrow> </mfrac> <mo>,</mo> </mrow> </math> Wherein, $m_{L,\mathrm{org}}^{\mathrm{str}}(x,y)=\sqrt{{\left({\mathrm{gx}}_{L,\mathrm{org}}^{\mathrm{str}}(x,y)\right)}^2+{\left({\mathrm{gy}}_{L,\mathrm{org}}^{\mathrm{str}}(x,y)\right)}^2}$ ， $m_{L,\mathrm{dis}}^{\mathrm{str}}(x,y)=\sqrt{{\left({\mathrm{gx}}_{L,\mathrm{dis}}^{\mathrm{str}}(x,y)\right)}^2+{\left({\mathrm{gy}}_{L,\mathrm{dis}}^{\mathrm{str}}(x,y)\right)}^2},$

to represent

The horizontal direction gradient of the pixel point with the middle coordinate position (x, y),to represent

The vertical direction gradient of the pixel point with the middle coordinate position (x, y),

to represent

The horizontal direction gradient of the pixel point with the middle coordinate position (x, y),

to represent

Gradient of pixel points with (x, y) as middle coordinate position in vertical direction, C₁Is a control parameter; then according to

Each pixel point in (1)

Calculating the gradient similarity between corresponding pixel points

The predicted value of objective evaluation of image quality is recorded as

Also, calculate

Each pixel point in (1)The gradient similarity between the corresponding pixel points will beThe pixel point with the (x, y) coordinate position and

the gradient similarity between pixel points with (x, y) as the middle coordinate position is recorded as

<math> <mrow> <msubsup> <mi>Q</mi> <mi>R</mi> <mi>str</mi> </msubsup> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <mn>2</mn> <mo>&times;</mo> <msubsup> <mi>m</mi> <mrow> <mi>R</mi> <mo>,</mo> <mi>org</mi> </mrow> <mi>str</mi> </msubsup> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>&times;</mo> <msubsup> <mi>m</mi> <mrow> <mi>R</mi> <mo>,</mo> <mi>dis</mi> </mrow> <mi>str</mi> </msubsup> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>C</mi> <mn>1</mn> </msub> </mrow> <mrow> <msup> <mrow> <mo>(</mo> <msubsup> <mi>m</mi> <mrow> <mi>R</mi> <mo>,</mo> <mi>org</mi> </mrow> <mi>str</mi> </msubsup> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <msubsup> <mi>m</mi> <mrow> <mi>R</mi> <mo>,</mo> <mi>dis</mi> </mrow> <mi>str</mi> </msubsup> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msub> <mi>C</mi> <mn>1</mn> </msub> </mrow> </mfrac> <mo>,</mo> </mrow> </math> Wherein,

$m_{R,\mathrm{org}}^{\mathrm{str}}(x,y)=\sqrt{{\left({\mathrm{gx}}_{R,\mathrm{org}}^{\mathrm{str}}(x,y)\right)}^2+{\left({\mathrm{gy}}_{R,\mathrm{org}}^{\mathrm{str}}(x,y)\right)}^2}$

$m_{R,\mathrm{dis}}^{\mathrm{str}}(x,y)=\sqrt{{\left({\mathrm{gx}}_{R,\mathrm{dis}}^{\mathrm{str}}(x,y)\right)}^2+{\left({\mathrm{gy}}_{R,\mathrm{dis}}^{\mathrm{str}}(x,y)\right)}^2}$ to representThe horizontal direction gradient of the pixel point with the middle coordinate position (x, y),

to represent

The vertical direction gradient of the pixel point with the middle coordinate position (x, y),

to represent

The horizontal direction gradient of the pixel point with the middle coordinate position (x, y),

to represent

Gradient of pixel points with (x, y) as middle coordinate position in vertical direction, C₁Is a control parameter; then according toEach pixel point in (1)

Calculating the gradient similarity between corresponding pixel pointsThe predicted value of objective evaluation of image quality is recorded as

ObtainingEach of the sub-blocks having a size of 8 x 8 and

calculating the structural similarity between the sub-blocks with the corresponding size of 8 multiplied by 8

The predicted value of objective evaluation of image quality is recorded as

Also, by obtaining

Each of the sub-blocks having a size of 8 x 8 and

calculating the structural similarity between the sub-blocks with the corresponding size of 8 multiplied by 8

The predicted value of objective evaluation of image quality is recorded as

Fifthly, to

And

to carry outFusing to obtain S_disThe predicted value of the objective evaluation of the image quality of the structural image is marked as Q_str，

Wherein, w_sTo representAnd

the weight proportion of (2);

also, for

And

carrying out fusion to obtain S_disThe predicted value of the texture image is recorded as Q_str，

Wherein, w_tTo represent

Andthe weight proportion of (2);

sixthly to Q_strAnd Q_texCarrying out fusion to obtain S_disThe predicted value of the objective evaluation of image quality is expressed as Q, Q = w × Q_str+(1-w)×Q_texWherein w represents Q_strAnd S_disThe weight ratio of (2).

3. The objective evaluation method for stereo image quality based on structure texture separation as claimed in claim 2, wherein in step (ii) { L }, the objective evaluation method for stereo image quality based on structure texture separation is adopted_org(x, y) } structural image

And texture images

The acquisition process comprises the following steps:

② 1a, will { L_orgDefining the current pixel point to be processed in (x, y) as the current pixel point;

2a, setting the current pixel point at { L_orgThe coordinate position in (x, y) is recorded as p, each pixel point except the current pixel point in a 21 × 21 neighborhood window with the current pixel point as the center is defined as a neighborhood pixel point, a block formed by a 9 × 9 neighborhood window with the current pixel point as the center is defined as a current sub-block, and the current sub-block is recorded as

Defining blocks formed by 9 × 9 neighborhood windows with each neighborhood pixel point in 21 × 21 neighborhood window with the current pixel point as the center as neighborhood sub-blocks, and defining the blocks in the 21 × 21 neighborhood window with the current pixel point as the center and in { L } neighborhood sub-blocks_org(x, y) in the (x, y) } neighborhood sub-block formed by 9 multiplied by 9 neighborhood window with neighborhood pixel point with coordinate position q as center

Wherein p ∈ Ω, q ∈ Ω, where Ω denotes { L ∈ Ω_org(x, y) } set of coordinate positions of all pixel points, (x)₂,y₂) Representing a current sub-block

The pixel point in (1) is in the current sub-blockCoordinate position of (1) x₂≤9,1≤y₂≤9，

Representing a current sub-block

The middle coordinate position is (x)₂,y₂) (x) pixel value of the pixel point of (c)₃,y₃) To representIs atCoordinate position of (1) x₃≤9,1≤y₃≤9，

To represent

The middle coordinate position is (x)₃,y₃) The pixel value of the pixel point of (1);

in the step 2a, for any neighborhood pixel point and any pixel point in the current sub-block, the pixel point is assumed to be in the { L [ ]_orgThe coordinate position in (x, y) } is (x, y), if x is<1 and y is more than or equal to 1 and less than or equal to H, then { L ≦ H_orgAssigning the pixel value of the pixel point with the coordinate position (1, y) in the (x, y) } to the pixel point; if x>W is 1. ltoreq. y.ltoreq.H, then { L_orgAssigning the pixel value of the pixel point with the coordinate position (W, y) in the (x, y) } to the pixel point; if x is 1. ltoreq. W and y<1, then { L }_orgAssigning the pixel value of the pixel point with the coordinate position (x,1) in the (x, y) } to the pixel point; if x is 1. ltoreq. W and y>H, then { L }_orgAssigning the pixel value of the pixel point with the coordinate position (x, H) in the (x, y) } to the pixel point; if x<1 and y<1, then { L }_orgAssigning the pixel value of the pixel point with the coordinate position (1,1) in the (x, y) } to the pixel point; if x>W and y<1, then { L }_orgAssigning the pixel value of the pixel point with the coordinate position (W,1) in the (x, y) } to the pixel point;if x<1 and y>H, then { L }_orgAssigning the pixel value of the pixel point with the coordinate position (1, H) in the (x, y) } to the pixel point; if x>W and y>H, then { L }_orgAssigning the pixel value of the pixel point with the coordinate position (W, H) in the (x, y) } to the pixel point;

② 3a, obtaining the current sub-block

The feature vector of each pixel point in (1) is used for converting the current sub-block into the current sub-block

The middle coordinate position is (x)₂,y₂) The feature vector of the pixel point is recorded as

<math> <mrow> <msubsup> <mi>X</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>org</mi> </mrow> <mi>p</mi> </msubsup> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>y</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mo>[</mo> <msubsup> <mi>I</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>org</mi> </mrow> <mi>p</mi> </msubsup> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>y</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mo>,</mo> <mo>|</mo> <mfrac> <mrow> <msubsup> <mrow> <mo>&PartialD;</mo> <mi>I</mi> </mrow> <mrow> <mi>L</mi> <mo>,</mo> <mi>org</mi> </mrow> <mi>p</mi> </msubsup> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>y</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> </mrow> <mrow> <mo>&PartialD;</mo> <mi>x</mi> </mrow> </mfrac> <mo>|</mo> <mo>,</mo> <mo>|</mo> <mfrac> <mrow> <msubsup> <mrow> <mo>&PartialD;</mo> <mi>I</mi> </mrow> <mrow> <mi>L</mi> <mo>,</mo> <mi>org</mi> </mrow> <mi>p</mi> </msubsup> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>y</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> </mrow> <mrow> <mo>&PartialD;</mo> <mi>y</mi> </mrow> </mfrac> <mo>|</mo> <mo>,</mo> <mo>|</mo> <mfrac> <mrow> <msup> <mo>&PartialD;</mo> <mn>2</mn> </msup> <msubsup> <mi>I</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>org</mi> </mrow> <mi>p</mi> </msubsup> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>y</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> </mrow> <msup> <mrow> <mo>&PartialD;</mo> <mi>x</mi> </mrow> <mn>2</mn> </msup> </mfrac> <mo>|</mo> <mo>,</mo> <mo>|</mo> <mfrac> <mrow> <msup> <mo>&PartialD;</mo> <mn>2</mn> </msup> <msubsup> <mi>I</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>org</mi> </mrow> <mi>p</mi> </msubsup> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>y</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> </mrow> <msup> <mrow> <mo>&PartialD;</mo> <mi>y</mi> </mrow> <mn>2</mn> </msup> </mfrac> <mo>|</mo> <mo>,</mo> <msub> <mi>x</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>y</mi> <mn>2</mn> </msub> <mo>]</mo> </mrow> </math> Wherein

has a dimension of 7, the symbol "[ 2 ]]"is a vector representation symbol, the symbol" | "is an absolute value symbol,

representing a current sub-block

The middle coordinate position is (x)₂,y₂) The density value of the pixel point of (a),

is composed ofThe first partial derivative in the horizontal direction,

is composed of

The first partial derivative in the vertical direction,is composed ofThe second partial derivative in the horizontal direction,

is composed ofSecond partial derivatives in the vertical direction;

② 4a, according to the current sub-block

Calculating the current sub-block according to the feature vector of each pixel point

Covariance matrix of (2), as

<math> <mrow> <msubsup> <mi>C</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>org</mi> </mrow> <mi>p</mi> </msubsup> <mo>=</mo> <mfrac> <mn>1</mn> <mrow> <mn>7</mn> <mo>&times;</mo> <mn>7</mn> <mo>-</mo> <mn>1</mn> </mrow> </mfrac> <munderover> <mi>&Sigma;</mi> <mrow> <msub> <mi>x</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>1</mn> </mrow> <mn>9</mn> </munderover> <munderover> <mi>&Sigma;</mi> <mrow> <msub> <mi>y</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>1</mn> </mrow> <mn>9</mn> </munderover> <mrow> <mo>(</mo> <msubsup> <mi>X</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>org</mi> </mrow> <mi>p</mi> </msubsup> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>y</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mo>-</mo> <msubsup> <mi>&mu;</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>org</mi> </mrow> <mi>p</mi> </msubsup> <mo>)</mo> </mrow> <msup> <mrow> <mo>(</mo> <msubsup> <mi>X</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>org</mi> </mrow> <mi>p</mi> </msubsup> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>y</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mo>-</mo> <msubsup> <mi>&mu;</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>org</mi> </mrow> <mi>p</mi> </msubsup> <mo>)</mo> </mrow> <mi>T</mi> </msup> <mo>,</mo> </mrow> </math> Wherein,

has a dimension of 7 x 7,

representing a current sub-blockThe mean vector of the feature vectors of all the pixel points in (1), <math> <msup> <mrow> <mo>(</mo> <msubsup> <mi>X</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>org</mi> </mrow> <mi>p</mi> </msubsup> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>y</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mo>-</mo> <msubsup> <mi>&mu;</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>org</mi> </mrow> <mi>p</mi> </msubsup> <mo>)</mo> </mrow> <mi>T</mi> </msup> </math> is composed of <math> <mrow> <mo>(</mo> <msubsup> <mi>X</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>org</mi> </mrow> <mi>p</mi> </msubsup> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>y</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mo>-</mo> <msubsup> <mi>&mu;</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>org</mi> </mrow> <mi>p</mi> </msubsup> <mo>)</mo> </mrow> </math> The transposed vector of (1);

② 5a, for the current sub-block

Covariance matrix ofThe Cholesky decomposition is carried out and,

obtaining the current sub-block

Sigma feature set of (D), noted

<math> <mrow> <msubsup> <mi>S</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>org</mi> </mrow> <mi>p</mi> </msubsup> <mo>=</mo> <mo>[</mo> <msqrt> <mn>10</mn> </msqrt> <mo>&times;</mo> <msup> <mi>L</mi> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </msup> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <msqrt> <mn>10</mn> </msqrt> <mo>&times;</mo> <msup> <mi>L</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>&prime;</mo> <mo>)</mo> </mrow> </msup> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <msqrt> <mn>10</mn> </msqrt> <mo>&times;</mo> <msup> <mi>L</mi> <mrow> <mo>(</mo> <mn>7</mn> <mo>)</mo> </mrow> </msup> <mo>,</mo> <mo>-</mo> <msqrt> <mn>10</mn> </msqrt> <mo>&times;</mo> <msup> <mi>L</mi> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </msup> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <mo>-</mo> <msqrt> <mn>10</mn> </msqrt> <mo>&times;</mo> <msup> <mi>L</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>&prime;</mo> <mo>)</mo> </mrow> </msup> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <mo>-</mo> <msqrt> <mn>10</mn> </msqrt> <mo>&times;</mo> <msup> <mi>L</mi> <mrow> <mo>(</mo> <mn>7</mn> <mo>)</mo> </mrow> </msup> <mo>,</mo> <msubsup> <mi>&mu;</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>org</mi> </mrow> <mi>p</mi> </msubsup> <mo>]</mo> <mo>,</mo> </mrow> </math> Wherein L is^TIs a transposed matrix of the L and,has a dimension of 7X 15, symbol "[ 2 ]]"is a vector representing a symbol where 1. ltoreq. i'. ltoreq.7, L⁽¹⁾1 st column vector representing L, L^(i')I' th column vector representing L, L⁽⁷⁾A 7 th column vector representing L;

secondly, 6a, adopting the same operation as the operation from the step II to obtain the Sigma characteristic set of the neighborhood sub-block formed by a 9 multiplied by 9 neighborhood window taking each neighborhood pixel point as the center, and leading the Sigma characteristic set to be matched with the neighborhood sub-blockSigma feature set of

Has a dimension of 7 × 15;

7a, according to the current sub-block

Sigma feature set of

And a Sigma characteristic set of a neighborhood sub-block consisting of a 9 multiplied by 9 neighborhood window with each neighborhood pixel point as a center, and acquiring the structural information of the current pixel point and recording the structural information as the structural information <math> <mrow> <msubsup> <mi>I</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>org</mi> </mrow> <mi>str</mi> </msubsup> <mrow> <mo>(</mo> <mi>p</mi> <mo>)</mo> </mrow> <mo>,</mo> <msubsup> <mi>I</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>org</mi> </mrow> <mi>str</mi> </msubsup> <mrow> <mo>(</mo> <mi>p</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <munder> <mi>&Sigma;</mi> <mrow> <mi>q</mi> <mo>&Element;</mo> <mi>N</mi> <mo>&prime;</mo> <mrow> <mo>(</mo> <mi>p</mi> <mo>)</mo> </mrow> </mrow> </munder> <mi>exp</mi> <mrow> <mo>(</mo> <mo>-</mo> <mfrac> <msup> <mrow> <mo>|</mo> <mo>|</mo> <msubsup> <mi>S</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>org</mi> </mrow> <mi>p</mi> </msubsup> <mo>-</mo> <msubsup> <mi>S</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>org</mi> </mrow> <mi>q</mi> </msubsup> <mo>|</mo> <mo>|</mo> </mrow> <mn>2</mn> </msup> <msup> <mrow> <mn>2</mn> <mi>&sigma;</mi> </mrow> <mn>2</mn> </msup> </mfrac> <mo>)</mo> </mrow> <mo>&times;</mo> <msub> <mi>L</mi> <mi>org</mi> </msub> <mrow> <mo>(</mo> <mi>q</mi> <mo>)</mo> </mrow> </mrow> <mrow> <munder> <mi>&Sigma;</mi> <mrow> <mi>q</mi> <mo>&Element;</mo> <mi>N</mi> <mo>&prime;</mo> <mrow> <mo>(</mo> <mi>p</mi> <mo>)</mo> </mrow> </mrow> </munder> <mi>exp</mi> <mrow> <mo>(</mo> <mo>-</mo> <mfrac> <msup> <mrow> <mo>|</mo> <mo>|</mo> <msubsup> <mi>S</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>org</mi> </mrow> <mi>p</mi> </msubsup> <mo>-</mo> <msubsup> <mi>S</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>org</mi> </mrow> <mi>q</mi> </msubsup> <mo>|</mo> <mo>|</mo> </mrow> <mn>2</mn> </msup> <msup> <mrow> <mn>2</mn> <mi>&sigma;</mi> </mrow> <mn>2</mn> </msup> </mfrac> <mo>)</mo> </mrow> </mrow> </mfrac> <mo>,</mo> </mrow> </math> Wherein N' (p) represents { L_orgAll neighborhood pixels in the 21 x 21 neighborhood window centered on the current pixel in (x, y) } are in { L }_orgA set of coordinate positions in (x, y) }, exp () represents an exponential function with e as the base, e =2.71828183, σ represents the standard deviation of a gaussian function, the symbol "|" is the euclidean distance calculation symbol, L_org(q) represents { L }_org(x, y) } the pixel value of a pixel point with the coordinate position of q;

② 8a, according to the structure information of current pixel point

Obtaining the texture information of the current pixel point and recording the texture information as $I_{L,\mathrm{org}}^{\mathrm{tex}}\left(p\right),I_{L,\mathrm{org}}^{\mathrm{tex}}\left(p\right)=L_{\mathrm{org}}\left(p\right)-I_{L,\mathrm{org}}^{\mathrm{str}}\left(p\right),$ Wherein L is_org(p) representing a pixel value of a current pixel point;

② 9a, will { L_orgTaking the next pixel point to be processed in (x, y) as the current pixel point, and then returning to the step 2a to continue executing until the pixel point is L_orgAll pixel points in (x, y) are processed to obtain { L }_orgThe structural information and the texture information of each pixel point in (x, y) } are expressed by { L }_orgThe structural information of all the pixel points in (x, y) } constitutes { L }_org(x, y) } structural image, noted

By

Texture information composition of all pixels in (1) { L }_org(x, y) } texture image, noted

Acquiring { L by adopting steps from 1a to 9a_org(x, y) } structural image

And texture imagesSame operation, get { R_org(x, y) } structural imageAnd texture images{L_dis(x, y) } structural image

And texture images

{R_dis(x, y) } structural image

And texture images

4. The objective evaluation method for stereo image quality based on structure texture separation according to claim 2 or 3, characterized in that the step (c) is

Objectively evaluating the predicted value of image quality

The acquisition process comprises the following steps:

fourthly-1 a, respectively

Andis divided into

Sub-blocks of size 8 × 8, which do not overlap with each other, are formed

Defining the current kth sub-block to be processed as the current first sub-blockThe current, to be processed, k sub-block is defined as the current, second sub-block, wherein,

k has an initial value of 1;

fourthly-2 a, recording the current first sub-block as

Record the current second sub-block as

Wherein (x)₄,y₄) To represent

And

x is more than or equal to 1₄≤8,1≤y₄≤8，

To represent

The middle coordinate position is (x)₄,y₄) The pixel value of the pixel point of (a),to representThe middle coordinate position is (x)₄,y₄) The pixel value of the pixel point of (1);

fourthly-3 a, calculating the current first sub-blockMean and standard deviation of (D), corresponding notation

And

<math> <mrow> <msub> <mi>&mu;</mi> <mrow> <msub> <mi>L</mi> <mi>org</mi> </msub> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mo>=</mo> <mfrac> <mrow> <munderover> <mi>&Sigma;</mi> <mrow> <msub> <mi>y</mi> <mn>4</mn> </msub> <mo>=</mo> <mn>1</mn> </mrow> <mn>8</mn> </munderover> <munderover> <mi>&Sigma;</mi> <mrow> <msub> <mi>x</mi> <mn>4</mn> </msub> <mo>=</mo> <mn>1</mn> </mrow> <mn>8</mn> </munderover> <msub> <mi>f</mi> <mrow> <msub> <mi>L</mi> <mi>org</mi> </msub> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mn>4</mn> </msub> <mo>,</mo> <msub> <mi>y</mi> <mn>4</mn> </msub> <mo>)</mo> </mrow> </mrow> <mn>64</mn> </mfrac> <mo>,</mo> <msub> <mi>&sigma;</mi> <mrow> <msub> <mi>L</mi> <mi>org</mi> </msub> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mo>=</mo> <msqrt> <mfrac> <mrow> <munderover> <mi>&Sigma;</mi> <mrow> <msub> <mi>y</mi> <mn>4</mn> </msub> <mo>=</mo> <mn>1</mn> </mrow> <mn>8</mn> </munderover> <munderover> <mi>&Sigma;</mi> <mrow> <msub> <mi>x</mi> <mn>4</mn> </msub> <mo>=</mo> <mn>1</mn> </mrow> <mn>8</mn> </munderover> <msup> <mrow> <mo>(</mo> <msub> <mi>f</mi> <mrow> <msub> <mi>L</mi> <mi>org</mi> </msub> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mn>4</mn> </msub> <mo>,</mo> <msub> <mi>y</mi> <mn>4</mn> </msub> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>&mu;</mi> <mrow> <msub> <mi>L</mi> <mi>org</mi> </msub> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> <mn>64</mn> </mfrac> </msqrt> <mo>;</mo> </mrow> </math>

likewise, the current second sub-block is calculated

Mean and standard deviation of (D), corresponding notation

And

<math> <mrow> <msub> <mi>&mu;</mi> <mrow> <msub> <mi>L</mi> <mi>dis</mi> </msub> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mo>=</mo> <mfrac> <mrow> <munderover> <mi>&Sigma;</mi> <mrow> <msub> <mi>y</mi> <mn>4</mn> </msub> <mo>=</mo> <mn>1</mn> </mrow> <mn>8</mn> </munderover> <munderover> <mi>&Sigma;</mi> <mrow> <msub> <mi>x</mi> <mn>4</mn> </msub> <mo>=</mo> <mn>1</mn> </mrow> <mn>8</mn> </munderover> <msub> <mi>f</mi> <mrow> <msub> <mi>L</mi> <mi>dis</mi> </msub> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mn>4</mn> </msub> <mo>,</mo> <msub> <mi>y</mi> <mn>4</mn> </msub> <mo>)</mo> </mrow> </mrow> <mn>64</mn> </mfrac> <mo>,</mo> <msub> <mi>&sigma;</mi> <mrow> <msub> <mi>L</mi> <mi>dis</mi> </msub> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mo>=</mo> <msqrt> <mfrac> <mrow> <munderover> <mi>&Sigma;</mi> <mrow> <msub> <mi>y</mi> <mn>4</mn> </msub> <mo>=</mo> <mn>1</mn> </mrow> <mn>8</mn> </munderover> <munderover> <mi>&Sigma;</mi> <mrow> <msub> <mi>x</mi> <mn>4</mn> </msub> <mo>=</mo> <mn>1</mn> </mrow> <mn>8</mn> </munderover> <msup> <mrow> <mo>(</mo> <msub> <mi>f</mi> <mrow> <msub> <mi>L</mi> <mi>dis</mi> </msub> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mn>4</mn> </msub> <mo>,</mo> <msub> <mi>y</mi> <mn>4</mn> </msub> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>&mu;</mi> <mrow> <msub> <mi>L</mi> <mi>dis</mi> </msub> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> <mn>64</mn> </mfrac> </msqrt> <mo>;</mo> </mrow> </math>

fourthly-4 a, calculating the current first sub-block

With the current second sub-block

Structural similarity between them, is recorded as

<math> <mrow> <msubsup> <mi>Q</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>k</mi> </mrow> <mi>tex</mi> </msubsup> <mo>=</mo> <mfrac> <mrow> <mn>4</mn> <mo>&times;</mo> <mrow> <mo>(</mo> <msub> <mi>&sigma;</mi> <mrow> <msub> <mi>L</mi> <mi>org</mi> </msub> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mo>&times;</mo> <msub> <mi>&sigma;</mi> <mrow> <msub> <mi>L</mi> <mi>dis</mi> </msub> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mo>)</mo> </mrow> <mo>&times;</mo> <mrow> <mo>(</mo> <msub> <mi>&mu;</mi> <mrow> <msub> <mi>L</mi> <mi>org</mi> </msub> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mo>&times;</mo> <msub> <mi>&mu;</mi> <mrow> <msub> <mi>L</mi> <mi>dis</mi> </msub> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>C</mi> <mn>2</mn> </msub> </mrow> <mrow> <mrow> <mo>(</mo> <msup> <mrow> <mo>(</mo> <msub> <mi>&sigma;</mi> <mrow> <msub> <mi>L</mi> <mi>org</mi> </msub> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <msub> <mi>&sigma;</mi> <mrow> <msub> <mi>L</mi> <mi>dis</mi> </msub> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>)</mo> </mrow> <mo>+</mo> <mrow> <mo>(</mo> <msup> <mrow> <mo>(</mo> <msub> <mi>&mu;</mi> <mrow> <msub> <mi>L</mi> <mi>org</mi> </msub> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <msub> <mi>&mu;</mi> <mrow> <msub> <mi>L</mi> <mi>dis</mi> </msub> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>C</mi> <mn>2</mn> </msub> </mrow> </mfrac> <mo>,</mo> </mrow> </math> Wherein, C₂Is a control parameter;

(iv) 5a, let k = k +1, will

Taking the next sub-block to be processed as the current first sub-block

Taking the next sub-block to be processed as the current second sub-block, and then returning to the step (2 a) to continue execution until the next sub-block to be processed is reached

Andall the sub-blocks in the Chinese herbal medicine are processed to obtain the Chinese herbal medicine

Each sub-block of (1) and

wherein "=" in k = k +1 is an assignment symbol;

fourthly-6 a, according to

Each sub-block of (1) andstructural similarity between corresponding sub-blocks in the sequence, calculating

Objective evaluation of image qualityPredicted value, recorded as

In the step (iv)

Objectively evaluating the predicted value of image quality

The acquisition process comprises the following steps:

fourthly-1 b, respectively mixing

Andis divided into

Sub-blocks of size 8 × 8, which do not overlap with each other, are formed

Defining the current kth sub-block to be processed as the current first sub-block

The current, to be processed, k sub-block is defined as the current, second sub-block, wherein,

k has an initial value of 1;

fourthly-2 b, recording the current first sub-block as

Record the current second sub-block as

Wherein (x)₄,y₄) To representAnd

x is more than or equal to 1₄≤8,1≤y₄≤8，

To representThe middle coordinate position is (x)₄,y₄) The pixel value of the pixel point of (a),

to represent

The middle coordinate position is (x)₄,y₄) The pixel value of the pixel point of (1);

fourthly-3 b, calculating the current first sub-block

Mean and standard deviation of (D), corresponding notation

And <math> <mrow> <msub> <mi>&mu;</mi> <mrow> <msub> <mi>R</mi> <mi>org</mi> </msub> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mo>=</mo> <mfrac> <mrow> <munderover> <mi>&Sigma;</mi> <mrow> <msub> <mi>y</mi> <mn>4</mn> </msub> <mo>=</mo> <mn>1</mn> </mrow> <mn>8</mn> </munderover> <munderover> <mi>&Sigma;</mi> <mrow> <msub> <mi>x</mi> <mn>4</mn> </msub> <mo>=</mo> <mn>1</mn> </mrow> <mn>8</mn> </munderover> <msub> <mi>f</mi> <mrow> <msub> <mi>R</mi> <mi>org</mi> </msub> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mn>4</mn> </msub> <mo>,</mo> <msub> <mi>y</mi> <mn>4</mn> </msub> <mo>)</mo> </mrow> </mrow> <mn>64</mn> </mfrac> <mo>,</mo> <msub> <mi>&sigma;</mi> <mrow> <msub> <mi>R</mi> <mi>org</mi> </msub> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mo>=</mo> <msqrt> <mfrac> <mrow> <munderover> <mi>&Sigma;</mi> <mrow> <msub> <mi>y</mi> <mn>4</mn> </msub> <mo>=</mo> <mn>1</mn> </mrow> <mn>8</mn> </munderover> <munderover> <mi>&Sigma;</mi> <mrow> <msub> <mi>x</mi> <mn>4</mn> </msub> <mo>=</mo> <mn>1</mn> </mrow> <mn>8</mn> </munderover> <msup> <mrow> <mo>(</mo> <msub> <mi>f</mi> <mrow> <msub> <mi>R</mi> <mi>org</mi> </msub> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mn>4</mn> </msub> <mo>,</mo> <msub> <mi>y</mi> <mn>4</mn> </msub> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>&mu;</mi> <mrow> <msub> <mi>R</mi> <mi>org</mi> </msub> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> <mn>64</mn> </mfrac> </msqrt> <mo>;</mo> </mrow> </math>

likewise, the current second sub-block is calculatedMean and standard deviation of (D), corresponding notation

And

<math> <mrow> <msub> <mi>&mu;</mi> <mrow> <msub> <mi>R</mi> <mi>dis</mi> </msub> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mo>=</mo> <mfrac> <mrow> <munderover> <mi>&Sigma;</mi> <mrow> <msub> <mi>y</mi> <mn>4</mn> </msub> <mo>=</mo> <mn>1</mn> </mrow> <mn>8</mn> </munderover> <munderover> <mi>&Sigma;</mi> <mrow> <msub> <mi>x</mi> <mn>4</mn> </msub> <mo>=</mo> <mn>1</mn> </mrow> <mn>8</mn> </munderover> <msub> <mi>f</mi> <mrow> <msub> <mi>R</mi> <mi>dis</mi> </msub> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mn>4</mn> </msub> <mo>,</mo> <msub> <mi>y</mi> <mn>4</mn> </msub> <mo>)</mo> </mrow> </mrow> <mn>64</mn> </mfrac> <mo>,</mo> <msub> <mi>&sigma;</mi> <mrow> <msub> <mi>R</mi> <mi>dis</mi> </msub> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mo>=</mo> <msqrt> <mfrac> <mrow> <munderover> <mi>&Sigma;</mi> <mrow> <msub> <mi>y</mi> <mn>4</mn> </msub> <mo>=</mo> <mn>1</mn> </mrow> <mn>8</mn> </munderover> <munderover> <mi>&Sigma;</mi> <mrow> <msub> <mi>x</mi> <mn>4</mn> </msub> <mo>=</mo> <mn>1</mn> </mrow> <mn>8</mn> </munderover> <msup> <mrow> <mo>(</mo> <msub> <mi>f</mi> <mrow> <msub> <mi>R</mi> <mi>dis</mi> </msub> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mn>4</mn> </msub> <mo>,</mo> <msub> <mi>y</mi> <mn>4</mn> </msub> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>&mu;</mi> <mrow> <msub> <mi>R</mi> <mi>dis</mi> </msub> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> <mn>64</mn> </mfrac> </msqrt> <mo>;</mo> </mrow> </math>

fourthly-4 b, calculating the current first sub-block

With the current second sub-blockStructural similarity between them, is recorded as

<math> <mrow> <msubsup> <mi>Q</mi> <mrow> <mi>R</mi> <mo>,</mo> <mi>k</mi> </mrow> <mi>tex</mi> </msubsup> <mo>=</mo> <mfrac> <mrow> <mn>4</mn> <mo>&times;</mo> <mrow> <mo>(</mo> <msub> <mi>&sigma;</mi> <mrow> <msub> <mi>R</mi> <mi>org</mi> </msub> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mo>&times;</mo> <msub> <mi>&sigma;</mi> <mrow> <mi>R</mi> <msub> <mi>L</mi> <mi>dis</mi> </msub> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mo>)</mo> </mrow> <mo>&times;</mo> <mrow> <mo>(</mo> <msub> <mi>&mu;</mi> <mrow> <msub> <mi>R</mi> <mi>dis</mi> </msub> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mo>&times;</mo> <msub> <mi>&mu;</mi> <mrow> <msub> <mi>R</mi> <mi>dis</mi> </msub> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>C</mi> <mn>2</mn> </msub> </mrow> <mrow> <mrow> <mo>(</mo> <msup> <mrow> <mo>(</mo> <msub> <mi>&sigma;</mi> <mrow> <msub> <mi>R</mi> <mi>org</mi> </msub> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <msub> <mi>&sigma;</mi> <mrow> <msub> <mi>R</mi> <mi>dis</mi> </msub> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>)</mo> </mrow> <mo>+</mo> <mrow> <mo>(</mo> <msup> <mrow> <mo>(</mo> <msub> <mi>&mu;</mi> <mrow> <msub> <mi>R</mi> <mi>dis</mi> </msub> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <msub> <mi>&mu;</mi> <mrow> <msub> <mi>R</mi> <mi>dis</mi> </msub> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>C</mi> <mn>2</mn> </msub> </mrow> </mfrac> <mo>,</mo> </mrow> </math> Wherein, C₂Is a control parameter;

(iv) 5b, let k = k +1, will

Taking the next sub-block to be processed as the current first sub-blockTaking the next sub-block to be processed as the current second sub-block, and then returning to the step (2 b) to continue execution until the next sub-block to be processed is reached

And

all the sub-blocks in the Chinese herbal medicine are processed to obtain the Chinese herbal medicine

Each sub-block of (1) andwherein "=" in k = k +1 is an assignment symbol;

fourthly-6 b, according to

Each sub-block of (1) andstructural similarity between corresponding sub-blocks in the sequence, calculating

The predicted value of objective evaluation of image quality is recorded as

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