CN102999911B - Three-dimensional image quality objective evaluation method based on energy diagrams - Google Patents

Abstract

The invention discloses an objective evaluation method for the quality of stereoscopic images based on energy maps. Firstly, the energy maps of the original undistorted stereoscopic images at different scales and directions and the distorted stereoscopic images to be evaluated at different scales and directions are respectively calculated. The energy map of the distorted stereo image to be evaluated is obtained to obtain the objective evaluation metric value of each pixel in the distorted stereo image, and then according to the energy map and the binocular minimum perceptible change image, the value of each pixel of the distorted stereo image to be evaluated The objective evaluation measurement value is fused to obtain the image quality objective evaluation prediction value of the distorted stereo image to be evaluated. The advantage is that the obtained energy maps of different scales and directions can better reflect the visual perception characteristics of the human visual system, and use energy The fusion of images with minimal perceptible change can effectively improve the correlation between objective evaluation results and subjective perception.

Description

An Objective Evaluation Method of Stereo Image Quality Based on Energy Map

technical field

The invention relates to an image quality evaluation method, in particular to an energy map-based objective evaluation method for stereoscopic image quality.

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 binocular parallax principle 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 a stereoscopic image 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. Quality evaluation is of great significance. 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.

At present, the planar image quality evaluation method is usually directly applied to the evaluation of the stereoscopic image quality. However, the process of fusing the left and right viewpoint images of the stereoscopic image to produce a stereoscopic effect is not a simple process of superimposing the left and right viewpoint images, and it is difficult to use a simple method. Therefore, how to extract effective feature information from stereo images to simulate binocular stereo fusion, and how to objectively evaluate binocular stereo fusion based on the visual masking characteristics of the human eye and the response characteristics of the binocular energy intensity of the human eye Modulating the results to make the objective evaluation results more in line with the human visual system 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 for stereoscopic image quality based on an energy map, which 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: an objective evaluation method of stereoscopic image quality based on energy map, which is characterized in that its processing process is as follows: first, according to the left viewpoint image and the original undistorted stereoscopic image The even symmetric frequency response and odd symmetric frequency response of each pixel in the right viewpoint image at different scales and directions, and each of the disparity images between the left viewpoint image and the right viewpoint image of the original undistorted stereo image The pixel value of the pixel, to obtain the energy map of the original undistorted stereo image at different scales and directions, and according to each pixel in the left view point image and right view point image of the distorted stereo image to be evaluated at different scales and The even symmetric frequency response and the odd symmetric frequency response of the direction, and the pixel value of each pixel in the disparity image between the left viewpoint image and the right viewpoint image of the original undistorted stereoscopic image, obtain the distorted stereoscopic image to be evaluated The energy map of the image at different scales and directions; then obtain the objective evaluation metric value of each pixel in the distorted stereo image to be evaluated according to the two energy maps; and then according to each pixel in the distorted stereo image to be evaluated The objective evaluation metric value of the point and the binocular minimum perceptible change image of the left viewpoint image of the distorted stereo image to be evaluated are obtained, and the objective evaluation metric value for reflecting the binocular visual masking effect of the distorted stereo image to be evaluated is obtained, And according to the objective evaluation metric value of each pixel in the distorted stereo image to be evaluated and the energy maps of the distorted stereo image to be evaluated at different scales and directions, obtain the distorted stereo image to be evaluated for reflecting the dual The objective evaluation metric value of the energy intensity of the eyes; the objective evaluation metric value used to reflect the binocular visual masking effect of the distorted stereo image to be evaluated and the objective evaluation metric value used to reflect the binocular energy intensity are finally obtained to obtain the Image quality objective evaluation predictors of distorted stereoscopic images.

It specifically includes the following steps:

①Let S _org be the original undistorted stereo image, let S _dis be the distorted stereo image to be evaluated, record the left viewpoint image of S _org as {L _org (x,y)}, and let 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, 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)};

②Using the visual masking effect of human stereo vision perception on background illumination and contrast, extract the binocular minimum perceivable change image of {L _dis (x,y)}, denoted as in, Represents the binocular minimum perceptible change image of {L _dis (x,y)} The pixel value of the pixel point whose middle coordinate position is (x, y);

③ Calculate each pixel in {L _org (x,y)}, {R _org (x,y)}, {L _dis (x,y)}, {R _dis (x,y)} at different scales and odd symmetric frequency responses in the and directions; then obtain {L _org (x,y)}, {R _org (x,y)}, {L _dis (x,y)}, {R _dis (x ,y)} in the amplitude and phase of each pixel in different scales and directions; then according to each pixel in {L _org (x,y)} and {R _org (x,y)} in different scales and the amplitude and phase of the direction and the pixel value of each pixel in the disparity image between {L _org (x,y)} and {R _org (x,y)}, calculate the S _org in different scales and directions energy diagram, denoted as And according to the amplitude and phase of each pixel in {L _dis (x,y)} and {R _dis (x,y)} at different scales and directions and {L _org (x,y)} and {R _org The pixel value of each pixel in the parallax image between (x,y)}, calculate the energy map of S _dis in different scales and directions, denoted as in, express The pixel values of the pixel at the middle coordinate position (x, y) in different scales and directions, express The pixel value of the pixel at the middle coordinate position (x, y) in different scales and directions, α represents the scale factor of the filter used in filtering, 1≤α≤4, θ represents the direction factor of the filter used in filtering , 1≤θ≤4;

④ According to the energy diagram of S _org in different scales and directions And the energy diagram of S _dis at different scales and directions Calculate the objective evaluation metric value of each pixel point in S _dis , and record the objective evaluation metric value of the pixel point whose coordinate position is (x, y) in S _dis as Q(x, y), $Q(x,\mathrm{the\; y})=\underset{\mathrm{\θ}=1}{\overset{4}{\mathrm{\Σ}}}\underset{\mathrm{\α}=1}{\overset{4}{\mathrm{\Σ}}}\arccos \left(\frac{2\×{\mathrm{BE}}_{\mathrm{\α},\mathrm{\θ}}^{\mathrm{org}}(x,\mathrm{the\; y})\×{\mathrm{BE}}_{\mathrm{\α},\mathrm{\θ}}^{\mathrm{dis}}(x,\mathrm{the\; y})+T_1}{{\mathrm{BE}}_{\mathrm{\α},\mathrm{\θ}}^{\mathrm{org}}{(x,\mathrm{the\; y})}^2+{\mathrm{BE}}_{\mathrm{\α},\mathrm{\θ}}^{\mathrm{dis}}{(x,\mathrm{the\; y})}^2+T_1}\right)/16,$ Wherein, arccos() is an inverse cosine function, and T ₁ is a control parameter;

⑤According to the objective evaluation value of each pixel in S _dis and the binocular minimum perceptible change image of {L _dis (x,y)} Using the binocular visual masking effect of the human visual system, calculate the objective evaluation metric value of S _dis to reflect the binocular visual masking effect, denoted as Q _bm , $Q_{\mathrm{bm}}=\frac{\underset{(x,\mathrm{the\; y})\∈\mathrm{\Ω}}{\mathrm{\Σ}}Q(x,\mathrm{the\; y})\×(1/J_L^{\mathrm{dis}}(x,\mathrm{the\; y}))}{\underset{(x,\mathrm{the\; y})\∈\mathrm{\Ω}}{\mathrm{\Σ}}(1/J_L^{\mathrm{dis}}(x,\mathrm{the\; y}))},$ Among them, Ω represents the pixel domain range;

⑥According to the objective evaluation measurement value of each pixel in S _dis and the energy map of S _dis in different scales and directions Using the response characteristics of the human visual system to the binocular energy intensity, calculate the objective evaluation value of S _dis for reflecting the binocular energy intensity, denoted as Q _be , $Q_{\mathrm{be}}=\frac{\underset{(x,\mathrm{the\; y})\∈\mathrm{\Ω}}{\mathrm{\Σ}}Q(x,\mathrm{the\; y})\×(1/{\mathrm{BE}}^{\mathrm{dis}}(x,\mathrm{the\; y}))}{\underset{(x,\mathrm{the\; y})\∈\mathrm{\Ω}}{\mathrm{\Σ}}(1/{\mathrm{BE}}^{\mathrm{dis}}(x,\mathrm{the\; y}))},$ in, ${\mathrm{BE}}^{\mathrm{dis}}(x,\mathrm{the\; y})=\underset{\mathrm{\θ}=1}{\overset{4}{\mathrm{\Σ}}}\underset{\mathrm{\α}=1}{\overset{4}{\mathrm{\Σ}}}{\mathrm{BE}}_{\mathrm{\α},\mathrm{\θ}}^{\mathrm{dis}}(x,\mathrm{the\; y});$

⑦Fuse the objective evaluation metric value Q _bm of S _dis to reflect the binocular visual masking effect and the objective evaluation metric value Q _be of S _dis to reflect binocular energy intensity to obtain the objective evaluation and prediction of image quality of S _dis Value, denoted as Q _fin , Q _fin =(Q _bm ) ^γ (Q _be ) ^β , where γ and β are weight parameters.

The concrete process of described step 2. is:

②-1. Calculate the visual threshold set of the brightness masking effect of {L _dis (x,y)}, denoted as {T _l (x,y)}, Among them, T _l (x, y) represents the visualization threshold of the brightness masking effect of the pixel whose coordinate position is (x, y) in {L _dis (x, y)}, and bg _l (x, y) represents {L _dis In (x, y)}, the brightness average value of all pixels in the N×N neighborhood window centered on the pixel point whose coordinate position is (x, y), N≥1;

②-2. Calculate the visual threshold set of the contrast masking effect of {L _dis (x,y)}, denoted as {T _c (x,y)}, T _c (x,y)=K(bg _l (x, y))+eh _l (x, y), where T _c (x, y) represents the visual threshold of the contrast masking effect of the pixel whose coordinate position is (x, y) in {L _dis (x, y)} , eh _l (x, y) represents the average gradient value obtained by performing edge filtering in the horizontal direction and vertical direction on the pixel at the coordinate position (x, y) in {L _dis (x, y)}, K(bg _l (x,y))=-10 ^-6 ×(0.7×bg _l (x,y) ² +32×bg _l (x,y))+0.07;

②-3. Fuse the visual threshold set {T _l (x, y)} of the brightness masking effect of {L _dis (x, y)} and the visual threshold set {T _c (x, y)} of the contrast masking effect , to get the binocular minimum perceptible change image of {L _dis (x,y)}, denoted as Will The pixel value of the pixel point whose coordinate position is (x, y) is recorded as $J_L^{\mathrm{dis}}(x,\mathrm{the\; y})=T_l(x,\mathrm{the\; y})+T_c(x,\mathrm{the\; y}).$

In the step ②-1, N=5.

The concrete process of described step 3. is:

③-1. Use the log-Garbor filter to filter {L _org (x, y)} to obtain the even symmetric frequency response of each pixel in {L _org (x, y)} at different scales and directions and odd symmetric frequency response, the even symmetric frequency response of the pixel at coordinate position (x, y) in {L _org (x, y)} in different scales and directions is recorded as The odd symmetric frequency response of the pixel at the coordinate position (x, y) in {L _org (x, y)} in different scales and directions is recorded as Among them, α represents the scale factor of the filter used in filtering, 1≤α≤4, θ represents the direction factor of the filter used in filtering, 1≤θ≤4;

③-2. Calculate the amplitude and phase of each pixel in {L _org (x, y)} in different scales and directions, and set the coordinate position in {L _org (x, y)} as (x, y) The amplitudes of pixels at different scales and directions are recorded as The phases of the pixel at the coordinate position (x, y) in {L _org (x, y)} in different scales and directions are recorded as ${\mathrm{LP}}_{\mathrm{\α},\mathrm{\θ}}^{L,\mathrm{org}}(x,\mathrm{the\; y})=\arctan \left(\frac{e_{\mathrm{\α},\mathrm{\θ}}^L(x,\mathrm{the\; y})}{o_{\mathrm{\α},\mathrm{\θ}}^L(x,\mathrm{the\; y})}\right),$ Among them, arctan() is the arc tangent function;

③-3. Follow steps ③-1 to ③-2 to obtain the amplitude and phase of each pixel in {L _org (x,y)} in different scales and directions, and obtain {R _org in the same way (x,y)}, {L _dis (x,y)}, {R _dis (x,y)} the amplitude and phase of each pixel in different scales and directions, will be {R _org (x,y )} in the coordinate position (x, y) of the pixel at different scales and directions is recorded as The phases of the pixel at the coordinate position (x, y) in {R _org (x, y)} in different scales and directions are recorded as The amplitudes of the pixels at the coordinate position (x, y) in {L _dis (x, y)} in different scales and directions are recorded as The phases of the pixel at the coordinate position (x, y) in {L _dis (x, y)} in different scales and directions are recorded as The amplitudes of the pixels at the coordinate position (x, y) in {R _dis (x, y)} in different scales and directions are recorded as The phases of the pixel at the coordinate position (x, y) in {R _dis (x, y)} in different scales and directions are recorded as

③-4. Calculate the parallax image between {L _org (x, y)} and {R _org (x, y)} by block matching method, denoted as in, express The pixel value of the pixel point whose middle coordinate position is (x, y);

③-5. According to the amplitude and phase of each pixel in {L _org (x, y)} and {R _org (x, y)} in different scales and directions, and {L _org (x, y)} and Disparity image between {R _org (x,y)} The pixel value of each pixel in , calculate the energy map of S _org in different scales and directions, denoted as Will The pixel values at different scales and directions of the pixel at the middle coordinate position (x, y) are denoted as ${\mathrm{BE}}_{\mathrm{\α},\mathrm{\θ}}^{\mathrm{org}}(x,\mathrm{the\; y})={\left({\mathrm{LE}}_{\mathrm{\α},\mathrm{\θ}}^{L,\mathrm{org}}(x,\mathrm{the\; y})\right)}^2+{\left({\mathrm{LE}}_{\mathrm{\α},\mathrm{\θ}}^{R,\mathrm{org}}(x-d_{\mathrm{org}}^L(x,\mathrm{the\; y}),\mathrm{the\; y})\right)}^2$ $+{\mathrm{LE}}_{\mathrm{\α},\mathrm{\θ}}^{L,\mathrm{org}}(x,\mathrm{the\; y})\×{\mathrm{LE}}_{\mathrm{\α},\mathrm{\θ}}^{R,\mathrm{org}}(x-d_{\mathrm{org}}^L(x,\mathrm{the\; y}),\mathrm{the\; y})\×\cos ({\mathrm{LP}}_{\mathrm{\α},\mathrm{\θ}}^{L,\mathrm{org}}(x,\mathrm{the\; y})-{\mathrm{LP}}_{\mathrm{\α},\mathrm{\θ}}^{R,\mathrm{org}}(x-d_{\mathrm{org}}^L(x,\mathrm{the\; y}),\mathrm{the\; y}))$ , where cos() is the cosine function, Indicates that the coordinate position in {R _org (x,y)} is The amplitudes of pixels in different scales and directions, Indicates that the coordinate position in {R _org (x,y)} is The phases of pixels in different scales and directions;

③-6. According to the amplitude and phase of each pixel in {L _dis (x, y)} and {R _dis (x, y)} in different scales and directions and {L _org (x, y)} and Disparity image between {R _org (x,y)} The pixel value of each pixel in , calculate the energy map of S _dis in different scales and directions, denoted as Will The pixel values at different scales and directions of the pixel at the middle coordinate position (x, y) are denoted as ${\mathrm{BE}}_{\mathrm{\α},\mathrm{\θ}}^{\mathrm{dis}}(x,\mathrm{the\; y})={\left({\mathrm{LE}}_{\mathrm{\α},\mathrm{\θ}}^{L,\mathrm{dis}}(x,\mathrm{the\; y})\right)}^2+{\left({\mathrm{LE}}_{\mathrm{\α},\mathrm{\θ}}^{R,\mathrm{dis}}(x-d_{\mathrm{org}}^L(x,\mathrm{the\; y}),\mathrm{the\; y})\right)}^2$ $+{\mathrm{LE}}_{\mathrm{\α},\mathrm{\θ}}^{L,\mathrm{dis}}(x,\mathrm{the\; y})\×{\mathrm{LE}}_{\mathrm{\α},\mathrm{\θ}}^{R,\mathrm{dis}}(x-d_{\mathrm{org}}^L(x,\mathrm{the\; y}),\mathrm{the\; y})\×\cos {(L{P}_{\mathrm{\α},\mathrm{\θ}}^{L,\mathrm{dis}}(x,\mathrm{the\; y})-{\mathrm{LP}}_{\mathrm{\α},\mathrm{\θ}}^{R,\mathrm{dis}}(x-d_{\mathrm{org}}^L(x,\mathrm{the\; y}),\mathrm{the\; y}))}^{,}$ in, Indicates that the coordinate position in {R _dis (x,y)} is The amplitudes of pixels in different scales and directions, Indicates that the coordinate position in {R _dis (x,y)} is The phases of pixels in different scales and directions.

_T1 is 16 in the described step ④.

In the step ⑦, γ=0.7755 and β=0.0505 are taken.

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

1) The method of the present invention calculates the energy maps of the original undistorted stereo image at different scales and directions and the energy maps of the distorted stereo image to be evaluated at different scales and directions, and obtains the distorted stereo image to be evaluated. The objective evaluation metric value of each pixel makes the evaluation result feel more in line with the human visual system.

2) The method of the present invention obtains binocular minimum perceptible change images according to the stereoscopic vision characteristics of human eyes, and fuses the objective evaluation value of each pixel in the distorted stereoscopic image to be evaluated, so that the evaluation results can reflect binocular vision Masking effect, thus effectively improving the correlation between objective evaluation results and subjective perception.

3) According to the energy maps of different scales and directions, the method of the present invention fuses the objective evaluation measurement value of each pixel in the distorted stereo image to be evaluated, so that the evaluation result can reflect the response of the human visual system to the binocular energy intensity characteristics, thus effectively improving the correlation between objective evaluation results and subjective perception.

Description of drawings

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

Figure 2a is the left viewpoint image of the Akko (size 640×480) stereoscopic image;

Figure 2b is the right view point image of Akko (size 640×480) stereoscopic image;

Figure 3a is the left viewpoint image of Altmoabit (size 1024×768) stereoscopic image;

Figure 3b is the right viewpoint image of the Altmoabit (size 1024×768) stereo image;

Figure 4a is the left viewpoint image of the stereoscopic image of Balloons (size 1024×768);

Figure 4b is the right view point image of the stereoscopic image of Balloons (size 1024×768);

Figure 5a is the left viewpoint image of the stereoscopic image of Doorflower (size 1024×768);

Figure 5b is the right view point image of the stereoscopic image of Doorflower (size 1024×768);

Figure 6a is the left viewpoint image of the Kendo (size 1024×768) stereoscopic image;

Figure 6b is the right view point image of the Kendo (size 1024×768) stereoscopic image;

Figure 7a is the left viewpoint image of the Stereoscopic image of LeaveLaptop (size 1024×768);

Figure 7b is the right view point image of the stereoscopic image of LeaveLaptop (size 1024×768);

Figure 8a is the left viewpoint image of the stereoscopic image of Lovebierd1 (size 1024×768);

Figure 8b is the right viewpoint image of the stereoscopic image of Lovebierd1 (size 1024×768);

Figure 9a is the left viewpoint image of the stereoscopic image of Newspaper (size 1024×768);

Figure 9b is the right view point image of the stereoscopic image of Newspaper (size 1024×768);

Figure 10a is the left viewpoint image of the Puppy (size 720×480) stereoscopic image;

Figure 10b is the right view point image of the Puppy (size 720×480) stereoscopic image;

Figure 11a is the left view point image of the Soccer2 (size is 720×480) stereoscopic image;

Figure 11b is the right view point image of the Soccer2 (size is 720×480) stereoscopic image;

Figure 12a is the left viewpoint image of the stereo image of Horse (size 720×480);

Figure 12b is the right view point image of the stereoscopic image of Horse (size 720×480);

Figure 13a is the left viewpoint image of the Xmas (640×480 in size) stereoscopic image;

Fig. 13b is the right view point image of the Xmas (size is 640×480) stereoscopic image;

FIG. 14 is a scatter diagram of the difference between the predicted image quality objective evaluation value and the average subjective evaluation value of each distorted stereoscopic image.

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 energy map proposed by the present invention, its overall realization block diagram is shown in Figure 1, and its processing process is: first, according to the left viewpoint image and the right viewpoint of the original undistorted stereoscopic image The even symmetric frequency response and odd symmetric frequency response of each pixel in the image at different scales and directions, and each pixel in the disparity image between the left view point image and the right view point image of the original undistorted stereoscopic image The pixel value of the original undistorted stereo image at different scales and directions is obtained, and according to the value of each pixel in the left view point image and right view point image of the distorted stereo image to be evaluated at different scales and directions The even symmetric frequency response and the odd symmetric frequency response, and the pixel value of each pixel in the disparity image between the left view point image and the right view point image of the original undistorted stereo image, obtain the distorted stereo image to be evaluated in Energy maps of different scales and directions; then obtain the objective evaluation metric value of each pixel in the distorted stereo image to be evaluated according to the two energy maps; and then obtain the objective evaluation metric value of each pixel in the distorted stereo image to be evaluated The objective evaluation metric value and the binocular minimum perceptible change image of the left viewpoint image of the distorted stereo image to be evaluated are obtained, and the objective evaluation metric value for reflecting the binocular visual masking effect of the distorted stereo image to be evaluated is obtained, and according to The objective evaluation metric value of each pixel in the distorted stereo image to be evaluated and the energy map of the distorted stereo image to be evaluated at different scales and directions, and the binocular energy of the distorted stereo image to be evaluated is obtained The objective evaluation metric value of the intensity; finally, the objective evaluation metric value used to reflect the binocular visual masking effect of the distorted stereo image to be evaluated and the objective evaluation metric value used to reflect the binocular energy intensity are fused to obtain the distortion to be evaluated Stereoscopic image quality objective evaluation predictor value. It specifically includes the following steps:

①Let S _org be the original undistorted stereo image, let S _dis be the distorted stereo image to be evaluated, record the left viewpoint image of S _org as {L _org (x,y)}, and let 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)}.

②The characteristics of human vision show that the human eye is imperceptible to the attribute or noise with small changes in the image, unless the change intensity of the attribute or noise exceeds a certain threshold, which is the minimum noticeable difference (Just noticeable difference, JND). However, the visual masking effect of the human eye is a local effect, which is affected by factors such as background illumination and texture complexity. The brighter the background, the more complex the texture, the higher the threshold value. Therefore, the present invention utilizes the visual masking effect of human stereo vision perception on background illumination and contrast to extract the binocular minimum perceptible change image of {L _dis (x, y)}, denoted as in, Represents the binocular minimum perceptible change image of {L _dis (x,y)} The pixel value of the pixel whose middle coordinate position is (x, y).

In this specific embodiment, the concrete process of step 2. is:

②-1. Calculate the visual threshold set of the brightness masking effect of {L _dis (x,y)}, denoted as {T _l (x,y)}, Among them, T _l (x, y) represents the visualization threshold of the brightness masking effect of the pixel whose coordinate position is (x, y) in {L _dis (x, y)}, and bg _l (x, y) represents {L _dis In (x, y)}, the brightness average value of all pixels in the N×N neighborhood window centered on the pixel point whose coordinate position is (x, y), N≥1, in this embodiment, N =5.

②-2. Calculate the visual threshold set of the contrast masking effect of {L _dis (x,y)}, denoted as {T _c (x,y)}, T _c (x,y)=K(bg _l (x, y))+eh _l (x, y), where T _c (x, y) represents the visual threshold of the contrast masking effect of the pixel whose coordinate position is (x, y) in {L _dis (x, y)} , eh _l (x, y) represents the average gradient value obtained by performing edge filtering in the horizontal direction and vertical direction on the pixel at the coordinate position (x, y) in {L _dis (x, y)}, K(bg _l (x,y))=-10 ^-6 ×(0.7×bg _l (x,y) ² +32×bg _l (x,y))+0.07.

②-3. Fuse the visual threshold set {T _l (x, y)} of the brightness masking effect of {L _dis (x, y)} and the visual threshold set {T _c (x, y)} of the contrast masking effect , to get the binocular minimum perceptible change image of {L _dis (x,y)}, denoted as Will The pixel value of the pixel point whose coordinate position is (x, y) is recorded as $J_L^{\mathrm{dis}}(x,\mathrm{the\; y})=T_l(x,\mathrm{the\; y})+T_c(x,\mathrm{the\; y}).$

③ Calculate each pixel in {L _org (x,y)}, {R _org (x,y)}, {L _dis (x,y)}, {R _dis (x,y)} at different scales and odd symmetric frequency responses in the and directions; then obtain {L _org (x,y)}, {R _org (x,y)}, {L _dis (x,y)}, {R _dis (x ,y)} in the amplitude and phase of each pixel in different scales and directions; then according to each pixel in {L _org (x,y)} and {R _org (x,y)} in different scales and the amplitude and phase of the direction and the pixel value of each pixel in the disparity image between {L _org (x,y)} and {R _org (x,y)}, calculate the S _org in different scales and directions energy diagram, denoted as And according to the amplitude and phase of each pixel in {L _dis (x,y)} and {R _dis (x,y)} at different scales and directions and {L _org (x,y)} and {R _org The pixel value of each pixel in the parallax image between (x,y)}, calculate the energy map of S _dis in different scales and directions, denoted as in, express The pixel values of the pixel at the middle coordinate position (x, y) in different scales and directions, express The pixel value of the pixel at the middle coordinate position (x, y) in different scales and directions, α represents the scale factor of the filter used in filtering, 1≤α≤4, θ represents the direction factor of the filter used in filtering , 1≤θ≤4.

In this specific embodiment, the concrete process of step 3. is:

③-1. Use the log-Garbor filter to filter {L _org (x, y)} to obtain the even symmetric frequency response of each pixel in {L _org (x, y)} at different scales and directions and odd symmetric frequency response, the even symmetric frequency response of the pixel at coordinate position (x, y) in {L _org (x, y)} in different scales and directions is recorded as The odd symmetric frequency response of the pixel at the coordinate position (x, y) in {L _org (x, y)} in different scales and directions is recorded as Wherein, α represents the scale factor of the filter used for filtering, 1≤α≤4, and θ represents the direction factor of the filter used for filtering, 1≤θ≤4.

③-2. Calculate the amplitude and phase of each pixel in {L _org (x, y)} in different scales and directions, and set the coordinate position in {L _org (x, y)} as (x, y) The amplitudes of pixels at different scales and directions are recorded as The phases of the pixel at the coordinate position (x, y) in {L _org (x, y)} in different scales and directions are recorded as ${\mathrm{LP}}_{\mathrm{\α},\mathrm{\θ}}^{L,\mathrm{org}}(x,\mathrm{the\; y})=\arctan \left(\frac{e_{\mathrm{\α},\mathrm{\θ}}^L(x,\mathrm{the\; y})}{o_{\mathrm{\α},\mathrm{\θ}}^L(x,\mathrm{the\; y})}\right),$ Among them, arctan() is the arc tangent function.

③-3. Follow steps ③-1 to ③-2 to obtain the amplitude and phase of each pixel in {L _org (x,y)} in different scales and directions, and obtain {R _org in the same way (x,y)}, {L _dis (x,y)}, {R _dis (x,y)} the amplitude and phase of each pixel in different scales and directions, will be {R _org (x,y )} in the coordinate position (x, y) of the pixel at different scales and directions is recorded as The phases of the pixel at the coordinate position (x, y) in {R _org (x, y)} in different scales and directions are recorded as The amplitudes of the pixels at the coordinate position (x, y) in {L _dis (x, y)} in different scales and directions are recorded as The phases of the pixel at the coordinate position (x, y) in {L _dis (x, y)} in different scales and directions are recorded as The amplitudes of the pixels at the coordinate position (x, y) in {R _dis (x, y)} in different scales and directions are recorded as The phases of the pixel at the coordinate position (x, y) in {R _dis (x, y)} in different scales and directions are recorded as For example: Get the amplitude of the pixel at the coordinate position (x, y) in {L _dis (x, y)} at different scales and directions and phase The process is: a. Use the log-Garbor filter to filter {L _dis (x, y)} to obtain the even symmetry of each pixel in {L _dis (x, y)} in different scales and directions Frequency response and odd symmetric frequency response, the even symmetric frequency response of the pixel at coordinate position (x, y) in {L _dis (x, y)} in different scales and directions is recorded as The odd symmetric frequency response of the pixel at the coordinate position (x, y) in {L _dis (x, y)} in different scales and directions is recorded as Among them, α represents the scale factor of the filter used in filtering, 1≤α≤4, θ represents the direction factor of the filter used in filtering, 1≤θ≤4; calculate {L _dis (x,y)} in The amplitude and phase of each pixel point in different scales and directions, the amplitude of the pixel point whose coordinate position is (x, y) in {L _dis (x, y)} in different scales and directions is recorded as ${\mathrm{LE}}_{\mathrm{\α},\mathrm{\θ}}^{L,\mathrm{dis}}(x,\mathrm{the\; y})=\sqrt{{e_{\mathrm{\α},\mathrm{\θ}}^L}^{\′}{(x,\mathrm{the\; y})}^2+{o_{\mathrm{\α},\mathrm{\θ}}^L}^{\′}{(x,\mathrm{the\; y})}^2},$ The phases of the pixel at the coordinate position (x, y) in {L _dis (x, y)} in different scales and directions are recorded as ${\mathrm{LP}}_{\mathrm{\α},\mathrm{\θ}}^{L,\mathrm{dis}}(x,\mathrm{the\; y})=\arctan \left(\frac{{e_{\mathrm{\α},\mathrm{\θ}}^L}^{\′}(x,\mathrm{the\; y})}{{o_{\mathrm{\α},\mathrm{\θ}}^L}^{\′}(x,\mathrm{the\; y})}\right).$

③-4. Calculate the parallax image between {L _org (x, y)} and {R _org (x, y)} by block matching method, denoted as in, express The pixel value of the pixel whose middle coordinate position is (x, y).

③-5. According to the amplitude and phase of each pixel in {L _org (x, y)} and {R _org (x, y)} in different scales and directions, and {L _org (x, y)} and Disparity image between {R _org (x,y)} The pixel value of each pixel in , calculate the energy map of S _org in different scales and directions, denoted as Will The pixel values at different scales and directions of the pixel at the middle coordinate position (x, y) are denoted as ${\mathrm{BE}}_{\mathrm{\α},\mathrm{\θ}}^{\mathrm{org}}(x,\mathrm{the\; y})={\left({\mathrm{LE}}_{\mathrm{\α},\mathrm{\θ}}^{L,\mathrm{org}}(x,\mathrm{the\; y})\right)}^2+{\left({\mathrm{LE}}_{\mathrm{\α},\mathrm{\θ}}^{R,\mathrm{org}}(x-d_{\mathrm{org}}^L(x,\mathrm{the\; y}),\mathrm{the\; y})\right)}^2$ $+{\mathrm{LE}}_{\mathrm{\α},\mathrm{\θ}}^{L,\mathrm{org}}(x,\mathrm{the\; y})\×{\mathrm{LE}}_{\mathrm{\α},\mathrm{\θ}}^{R,\mathrm{org}}(x-d_{\mathrm{org}}^L(x,\mathrm{the\; y}),\mathrm{the\; y})\×\cos ({\mathrm{LP}}_{\mathrm{\α},\mathrm{\θ}}^{L,\mathrm{org}}(x,\mathrm{the\; y})-{\mathrm{LP}}_{\mathrm{\α},\mathrm{\θ}}^{R,\mathrm{org}}(x-d_{\mathrm{org}}^L(x,\mathrm{the\; y}),\mathrm{the\; y}))$ , where cos() is the cosine function, Indicates that the coordinate position in {R _org (x,y)} is The amplitudes of pixels in different scales and directions, Indicates that the coordinate position in {R _org (x,y)} is The phases of pixels in different scales and directions.

③-6. According to the amplitude and phase of each pixel in {L _dis (x, y)} and {R _dis (x, y)} in different scales and directions and {L _org (x, y)} and Disparity image between {R _org (x,y)} The pixel value of each pixel in , calculate the energy map of S _dis in different scales and directions, denoted as Will The pixel values at different scales and directions of the pixel at the middle coordinate position (x, y) are denoted as ${\mathrm{BE}}_{\mathrm{\α},\mathrm{\θ}}^{\mathrm{dis}}(x,\mathrm{the\; y})={\left({\mathrm{LE}}_{\mathrm{\α},\mathrm{\θ}}^{L,\mathrm{dis}}(x,\mathrm{the\; y})\right)}^2+{\left({\mathrm{LE}}_{\mathrm{\α},\mathrm{\θ}}^{R,\mathrm{dis}}(x-d_{\mathrm{org}}^L(x,\mathrm{the\; y}),\mathrm{the\; y})\right)}^2$ $+{\mathrm{LE}}_{\mathrm{\α},\mathrm{\θ}}^{L,\mathrm{dis}}(x,\mathrm{the\; y})\×{\mathrm{LE}}_{\mathrm{\α},\mathrm{\θ}}^{R,\mathrm{dis}}(x-d_{\mathrm{org}}^L(x,\mathrm{the\; y}),\mathrm{the\; y})\×\cos {(L{P}_{\mathrm{\α},\mathrm{\θ}}^{L,\mathrm{dis}}(x,\mathrm{the\; y})-{\mathrm{LP}}_{\mathrm{\α},\mathrm{\θ}}^{R,\mathrm{dis}}(x-d_{\mathrm{org}}^L(x,\mathrm{the\; y}),\mathrm{the\; y}))}^{,}$ in, Indicates that the coordinate position in {R _dis (x,y)} is The amplitudes of pixels in different scales and directions, Indicates that the coordinate position in {R _dis (x,y)} is The phases of pixels in different scales and directions.

④ According to the energy diagram of S _org in different scales and directions And the energy diagram of S _dis at different scales and directions Calculate the objective evaluation metric value of each pixel in S _dis , express the objective evaluation metric value of all pixels in S _dis as {Q(x,y)}, and set the coordinate position in S _dis as (x ,y) The objective evaluation metric value of the pixel point is denoted as Q(x,y), $Q(x,\mathrm{the\; y})=\underset{\mathrm{\θ}=1}{\overset{4}{\mathrm{\Σ}}}\underset{\mathrm{\α}=1}{\overset{4}{\mathrm{\Σ}}}\arccos \left(\frac{2\×{\mathrm{BE}}_{\mathrm{\α},\mathrm{\θ}}^{\mathrm{org}}(x,\mathrm{the\; y})\×{\mathrm{BE}}_{\mathrm{\α},\mathrm{\θ}}^{\mathrm{dis}}(x,\mathrm{the\; y})+T_1}{{\mathrm{BE}}_{\mathrm{\α},\mathrm{\θ}}^{\mathrm{org}}{(x,\mathrm{the\; y})}^2+{\mathrm{BE}}_{\mathrm{\α},\mathrm{\θ}}^{\mathrm{dis}}{(x,\mathrm{the\; y})}^2+T_1}\right)/16,$ Wherein, arccos() is an inverse cosine function, and T ₁ is a control parameter. In this embodiment, T ₁ is 16.

⑤ The characteristics of the human visual system show that the human eye is more sensitive to the distortion of the area where the binocular detectable change value is small. Therefore, the present invention is based on the objective evaluation metric value of each pixel in S _dis and the binocular minimum perceptible change image of {L _dis (x, y)} Using the binocular visual masking effect of the human visual system, calculate the objective evaluation metric value of S _dis to reflect the binocular visual masking effect, denoted as Q _bm , Among them, Ω represents the pixel domain range.

⑥The characteristics of the human visual system show that the human eye will produce a stronger response to the area with greater binocular energy intensity. Therefore the present invention is based on the objective evaluation metric value of each pixel point in S _dis and the energy figure of S _dis at different scales and directions Using the response characteristics of the human visual system to the binocular energy intensity, calculate the objective evaluation value of S _dis for reflecting the binocular energy intensity, denoted as Q _be , $Q_{\mathrm{be}}=\frac{\underset{(x,\mathrm{the\; y})\∈\mathrm{\Ω}}{\mathrm{\Σ}}Q(x,\mathrm{the\; y})\×(1/{\mathrm{BE}}^{\mathrm{dis}}(x,\mathrm{the\; y}))}{\underset{(x,\mathrm{the\; y})\∈\mathrm{\Ω}}{\mathrm{\Σ}}(1/{\mathrm{BE}}^{\mathrm{dis}}(x,\mathrm{the\; y}))},$ in, ${\mathrm{BE}}^{\mathrm{dis}}(x,\mathrm{the\; y})=\underset{\mathrm{\θ}=1}{\overset{4}{\mathrm{\Σ}}}\underset{\mathrm{\α}=1}{\overset{4}{\mathrm{\Σ}}}{\mathrm{BE}}_{\mathrm{\α},\mathrm{\θ}}^{\mathrm{dis}}(x,\mathrm{the\; y}).$

⑦Fuse the objective evaluation metric value Q _bm of S _dis to reflect the binocular visual masking effect and the objective evaluation metric value Q _be of S _dis to reflect binocular energy intensity to obtain the objective evaluation and prediction of image quality of S _dis The value is denoted as Q _fin , Q _fin =(Q _bm ) ^γ (Q _be ) ^β , where γ and β are weight parameters, and in this embodiment, γ=0.7755 and β=0.0505.

In this embodiment, using Figure 2a and Figure 2b, Figure 3a and Figure 3b, Figure 4a and Figure 4b, Figure 5a and Figure 5b, Figure 6a and Figure 6b, Figure 7a and Figure 7b, Figure 8a and Figure 8b, Figure 9a and 9b, FIG. 10a and FIG. 10b, FIG. 11a and FIG. 11b, FIG. Gaussian blur, white noise and 312 distorted stereo images under the condition of H.264 encoding distortion analyze the difference between the image quality objective evaluation prediction value and the average subjective rating difference of the distorted stereo image S _dis obtained by the method of the present invention Correlation, including 60 distorted stereoscopic images compressed by JPEG, 60 distorted stereoscopic images compressed by JPEG2000, 60 distorted stereoscopic images by Gaussian Blur, and 60 distorted stereoscopic images by White Noise There are 60 stereoscopic images, and 72 distorted stereoscopic images encoded by H.264. And use the existing subjective quality evaluation method to obtain the average subjective score difference of 312 distorted stereo images, which is recorded as DMOS, DMOS=100-MOS, where MOS represents the mean subjective score, DMOS∈[0,100].

In this embodiment, 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) under nonlinear regression conditions. , SROCC), Kendall rank-order correlation coefficient (KROCC), mean square error (root mean squared error, RMSE), PLCC and RMSE reflect the accuracy of the distorted stereo image evaluation objective model, SROCC and KROCC reflect its monotony sex. 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. Reflecting the PLCC, SROCC, KROCC and RMSE coefficients of the stereoscopic image objective evaluation model performance as shown in table 1, from the data listed in table 1, we can know that the final image quality objective evaluation prediction value of the distorted stereoscopic image obtained by the method of the present invention The correlation with the average subjective score difference is very high, which fully shows that the objective evaluation result is relatively consistent with the subjective perception result of human eyes, and is sufficient to illustrate the effectiveness of the method of the present invention.

Figure 14 shows the scatter diagram of the difference between the predicted value of the image quality objective evaluation and the average subjective evaluation of 312 distorted stereoscopic images. 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. 14 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 correlation between the image quality objective evaluation prediction value and the subjective rating of the distorted stereoscopic image obtained by the method of the present invention

Claims (7)

1. A three-dimensional image quality objective evaluation method based on an energy map is characterized in that the processing process is as follows: firstly, acquiring an energy diagram of the original undistorted stereo image in different scales and directions according to even symmetric frequency response and odd symmetric frequency response of each pixel point in the left viewpoint image and the right viewpoint image of the original undistorted stereo image in different scales and directions and a pixel value of each pixel point in a parallax image between the left viewpoint image and the right viewpoint image of the original undistorted stereo image, acquiring energy graphs of the distorted stereo image to be evaluated in different scales and directions according to even symmetric frequency response and odd symmetric frequency response of each pixel point in the left viewpoint image and the right viewpoint image of the distorted stereo image to be evaluated in different scales and directions and pixel values of each pixel point in a parallax image between the left viewpoint image and the right viewpoint image of the original undistorted stereo image; then obtaining an objective evaluation metric value of each pixel point in the distorted stereo image to be evaluated according to the two energy maps; obtaining an objective evaluation metric value of the distorted three-dimensional image to be evaluated for reflecting a binocular visual masking effect according to the objective evaluation metric value of each pixel point in the distorted three-dimensional image to be evaluated and the binocular minimum perceptible change image of the left viewpoint image of the distorted three-dimensional image to be evaluated, and obtaining an objective evaluation metric value of the distorted three-dimensional image to be evaluated for reflecting binocular energy intensity according to the objective evaluation metric value of each pixel point in the distorted three-dimensional image to be evaluated and energy maps of the distorted three-dimensional image to be evaluated in different scales and directions; finally, objective evaluation metric values used for reflecting binocular visual masking effects and objective evaluation metric values used for reflecting binocular energy intensity of the distorted three-dimensional image to be evaluated are fused to obtain an objective evaluation prediction value of the image quality of the distorted three-dimensional image to be evaluated;

here, let S_orgFor original undistorted stereo image, let S_disFor the distorted stereo image 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_org(x, y) having (x, y) as the coordinate positionPixel value, L, of a pixel point_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); then according to { L_org(x, y) } and { R }_orgAmplitude and phase of each pixel point in (x, y) in different scales and directions, and { L }_org(x, y) } and { R }_org(x, y) } calculating S from the pixel value of each pixel in the parallax image_orgEnergy diagram at different scales and directions, and is recorded asAnd according to { L_dis(x, y) } and { R }_disAmplitude and phase of each pixel point in (x, y) in different scales and directions, and { L }_org(x, y) } and { R }_org(x, y) } calculating S from the pixel value of each pixel in the parallax image_disEnergy diagram at different scales and directions, and is recorded asWherein,to representThe pixel value of the pixel point with the middle coordinate position (x, y) in different scales and directions,to representAnd the pixel values of the pixel points with the middle coordinate positions (x, y) in different scales and directions, wherein alpha represents the scale factor of the filter adopted by filtering, alpha is more than or equal to 1 and less than or equal to 4, theta represents the direction factor of the filter adopted by filtering, and theta is more than or equal to 1 and less than or equal to 4.

2. The method for objectively evaluating the quality of a stereoscopic image based on an energy map according to claim 1, characterized in that it comprises the following steps:

making S_orgFor original undistorted stereo image, let S_disFor the distorted stereo image 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);

secondly, extracting { L by using visual masking effect of human stereoscopic vision perception on background illumination and contrast_dis(x, y) } binocular minimum perceivable change image, notedWherein,represents { L_dis(x, y) } binocular minimum perceivable change imageThe middle coordinate position is the pixel value of the pixel point of (x, y);

(iii) calculating { L_org(x,y)}、{R_org(x,y)}、{L_dis(x,y)}、{R_disEven symmetric frequency response and odd symmetric frequency response of each pixel point in (x, y) } in different scales and directions; then obtain { L_org(x,y)}、{R_org(x,y)}、{L_dis(x,y)}、{R_disThe amplitude and the phase of each pixel point in (x, y) in different scales and directions; then according to { L_org(x, y) } and { R }_orgAmplitude and phase of each pixel point in (x, y) in different scales and directions, and { L }_org(x, y) } and { R }_org(x, y) } calculating S from the pixel value of each pixel in the parallax image_orgEnergy diagram at different scales and directions, and is recorded asAnd according to { L_dis(x, y) } and { R }_disAmplitude and phase of each pixel point in (x, y) in different scales and directions, and { L }_org(x, y) } and { R }_org(x, y) } calculating S from the pixel value of each pixel in the parallax image_disEnergy diagram at different scales and directions, and is recorded asWherein,to representThe pixel value of the pixel point with the middle coordinate position (x, y) in different scales and directions,to representThe pixel values of the pixel points with the (x, y) coordinate positions in different scales and directions are determined, alpha represents the scale factor of the filter adopted by filtering, alpha is more than or equal to 1 and less than or equal to 4, theta represents the direction factor of the filter adopted by filtering, and theta is more than or equal to 1 and less than or equal to 4θ≤4；

Fourthly, according to S_orgEnergy diagram at different scales and directionsAnd S_disEnergy diagram at different scales and directionsCalculating S_disThe objective evaluation metric value of each pixel point in S_disThe objective evaluation metric value of the pixel point with the middle coordinate position (x, y) is marked as Q (x, y), <math> <mrow> <mi>Q</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>&theta;</mi> <mo>=</mo> <mn>1</mn> </mrow> <mn>4</mn> </munderover> <munderover> <mi>&Sigma;</mi> <mrow> <mi>&alpha;</mi> <mo>=</mo> <mn>1</mn> </mrow> <mn>4</mn> </munderover> <mi>arccos</mi> <mrow> <mo>(</mo> <mfrac> <mrow> <mn>2</mn> <mo>&times;</mo> <msubsup> <mi>BE</mi> <mrow> <mi>&alpha;</mi> <mo>,</mo> <mi>&theta;</mi> </mrow> <mi>org</mi> </msubsup> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>&times;</mo> <msubsup> <mi>BE</mi> <mrow> <mi>&alpha;</mi> <mo>,</mo> <mi>&theta;</mi> </mrow> <mi>dis</mi> </msubsup> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>T</mi> <mn>1</mn> </msub> </mrow> <mrow> <msubsup> <mi>BE</mi> <mrow> <mi>&alpha;</mi> <mo>,</mo> <mi>&theta;</mi> </mrow> <mi>org</mi> </msubsup> <msup> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msubsup> <mi>BE</mi> <mrow> <mi>&alpha;</mi> <mo>,</mo> <mi>&theta;</mi> </mrow> <mi>dis</mi> </msubsup> <msup> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msub> <mi>T</mi> <mn>1</mn> </msub> </mrow> </mfrac> <mo>)</mo> </mrow> <mo>/</mo> <mn>16</mn> <mo>,</mo> </mrow> </math> wherein arccos () is an inverted cosine function, T₁Is a control parameter;

according to S_disAnd the sum of objective evaluation metrics of each pixel point { L_dis(x, y) } binocular minimum perceivable change imageCalculating S using binocular visual masking effect of human visual system_disThe objective evaluation metric value for reflecting the binocular visual masking effect is marked as Q_bm，Wherein Ω represents a pixel domain range;

according to S_disAnd the objective evaluation metric value of each pixel point in the image data and S_disEnergy diagram at different scales and directionsCalculating S by using the response characteristics of the human visual system to the binocular energy intensity_disThe objective evaluation metric value for reflecting the binocular energy intensity is marked as Q_be，Wherein, <math> <mrow> <msup> <mi>BE</mi> <mi>dis</mi> </msup> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>&theta;</mi> <mo>=</mo> <mn>1</mn> </mrow> <mn>4</mn> </munderover> <munderover> <mi>&Sigma;</mi> <mrow> <mi>&alpha;</mi> <mo>=</mo> <mn>1</mn> </mrow> <mn>4</mn> </munderover> <msubsup> <mi>BE</mi> <mrow> <mi>&alpha;</mi> <mo>,</mo> <mi>&theta;</mi> </mrow> <mi>dis</mi> </msubsup> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>;</mo> </mrow> </math>

to S_disObjective evaluation metric Q for reflecting binocular visual masking effect_bmAnd S_disObjective evaluation metric Q for reflecting binocular energy intensity_beCarrying out fusion to obtain S_disThe predicted value of the objective evaluation of image quality is marked as Q_fin，Q_fin＝(Q_bm)^γ(Q_be)^βWherein γ and β are weight parameters.

3. The objective evaluation method for the quality of the stereo image based on the energy map as claimed in claim 2, characterized in that the concrete process of the step (II) is as follows:

② 1, calculating { L_disVisualization threshold set of luminance masking effect of (x, y) }, denoted as { T }_l(x,y)}，Wherein, T_l(x, y) represents { L }_disThe coordinate position in (x, y) is the visual threshold value of the brightness masking effect of the pixel point of (x, y), bg_l(x, y) represents { L }_disThe average value of the brightness of all pixel points in an NxN neighborhood window taking a pixel point with a coordinate position (x, y) as the center in (x, y) }, wherein N is more than or equal to 1;

2, calculating { L_disVisualization threshold set of contrast masking effect of (x, y) }, denoted as { T }_c(x,y)}，T_c(x,y)＝K(bg_l(x,y))+eh_l(x, y) wherein T_c(x, y) represents { L }_disThe coordinate position in (x, y) is the visual threshold value of the contrast masking effect of the pixel point of (x, y), eh_l(x, y) represents the pair { L }_disThe pixel points with the coordinate positions (x, y) in (x, y) are respectively subjected to edge filtering in the horizontal direction and the vertical direction to obtain an average gradient value K (bg)_l(x,y))＝-10^-6×(0.7×bg_l(x,y)²+32×bg_l(x,y))+0.07；

2- (3) pairs of { L_disVisualization threshold set of luminance masking effects of (x, y) } { T_l(x, y) } and a visual threshold set of contrast masking effects { T }_c(x, y) } to obtain { L_dis(x, y) } binocular minimum perceivable change image, notedWill be provided withThe pixel value of the pixel point with the middle coordinate position (x, y) is recorded as $J_L^{\mathrm{dis}}(x,y)=T_l(x,y)+T_c(x,y).$

4. The method according to claim 3, wherein in step (2), N is 5.

5. The objective evaluation method for stereo image quality based on energy map according to any one of claims 2 to 4, characterized in that the concrete process of the third step is:

③ 1, adopting log-Garbor filter pair { L_org(x, y) is filtered to obtain { L }_orgEven symmetric frequency response and odd symmetric frequency response of each pixel point in (x, y) } in different scales and directions are converted into { L }_orgEven symmetric frequency responses of pixel points with coordinate positions (x, y) in different scales and directions in (x, y) are recorded asWill { L_orgThe odd symmetric frequency response of the pixel point with the coordinate position (x, y) in different scales and directions is recorded asWhere α represents the scale of the filter employed for filteringThe factor alpha is more than or equal to 1 and less than or equal to 4, theta represents the direction factor of the filter used for filtering, and theta is more than or equal to 1 and less than or equal to 4;

③ 2, calculate { L_orgThe amplitude and phase of each pixel point in (x, y) in different scales and directions will be { L }_orgThe amplitudes of the pixel points with the coordinate positions (x, y) in the (x, y) at different scales and directions are recorded as Will { L_orgThe phase positions of the pixel points with the coordinate positions (x, y) in (x, y) at different scales and directions are recorded as Wherein arctan () is an inverse tangent function;

③ 3, obtaining { L ] according to the steps from the third step to the third step_orgThe operation process of the amplitude and the phase of each pixel point in (x, y) } in different scales and directions is carried out to obtain { R } in the same way_org(x,y)}、{L_dis(x,y)}、{R_disThe amplitude and phase of each pixel point in (x, y) in different scales and directions will be { R }_orgThe amplitudes of the pixel points with the coordinate positions (x, y) in the (x, y) at different scales and directions are recorded asWill { R_orgThe phase positions of the pixel points with the coordinate positions (x, y) in (x, y) at different scales and directions are recorded asWill { L_disThe amplitudes of the pixel points with the coordinate positions (x, y) in the (x, y) at different scales and directions are recorded asWill { L_disThe phase positions of the pixel points with the coordinate positions (x, y) in (x, y) at different scales and directions are recorded asWill { R_disThe amplitudes of the pixel points with the coordinate positions (x, y) in the (x, y) at different scales and directions are recorded asWill { R_disThe phase positions of the pixel points with the coordinate positions (x, y) in (x, y) at different scales and directions are recorded as

Thirdly-4, calculating L by adopting a block matching method_org(x, y) } and { R }_org(x, y) } parallax images, noted asWherein,to representThe middle coordinate position is the pixel value of the pixel point of (x, y);

③ 5 according to { L_org(x, y) } and { R }_orgAmplitude and phase of each pixel point in (x, y) in different scales and directions, and { L }_org(x, y) } and { R }_orgParallax image between (x, y) } sCalculating the pixel value of each pixel point in S_orgEnergy diagram at different scales and directions, and is recorded asWill be provided withThe pixel values of the pixel points with the middle coordinate position (x, y) in different scales and directions are recorded as

<math> <mfenced open='' close=''> <mtable> <mtr> <mtd> <msubsup> <mi>BE</mi> <mrow> <mi>&alpha;</mi> <mo>,</mo> <mi>&theta;</mi> </mrow> <mi>org</mi> </msubsup> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>=</mo> <msup> <mrow> <mo>(</mo> <msubsup> <mi>LE</mi> <mrow> <mi>&alpha;</mi> <mo>,</mo> <mi>&theta;</mi> </mrow> <mrow> <mi>L</mi> <mo>,</mo> <mi>org</mi> </mrow> </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>LE</mi> <mrow> <mi>&alpha;</mi> <mo>,</mo> <mi>&theta;</mi> </mrow> <mrow> <mi>R</mi> <mo>,</mo> <mi>org</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>x</mi> <mo>-</mo> <msubsup> <mi>d</mi> <mi>org</mi> <mi>L</mi> </msubsup> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mtd> </mtr> <mtr> <mtd> <mo>+</mo> <msubsup> <mi>LE</mi> <mrow> <mi>&alpha;</mi> <mo>,</mo> <mi>&theta;</mi> </mrow> <mrow> <mi>L</mi> <mo>,</mo> <mi>org</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>&times;</mo> <msubsup> <mi>LE</mi> <mrow> <mi>&alpha;</mi> <mo>,</mo> <mi>&theta;</mi> </mrow> <mrow> <mi>R</mi> <mo>,</mo> <mi>org</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>x</mi> <mo>-</mo> <msubsup> <mi>d</mi> <mi>org</mi> <mi>L</mi> </msubsup> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>&times;</mo> <mi>cos</mi> <mrow> <mo>(</mo> <msubsup> <mi>LP</mi> <mrow> <mi>&alpha;</mi> <mo>,</mo> <mi>&theta;</mi> </mrow> <mrow> <mi>L</mi> <mo>,</mo> <mi>org</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>-</mo> <msubsup> <mi>LP</mi> <mrow> <mi>&alpha;</mi> <mo>,</mo> <mi>&theta;</mi> </mrow> <mrow> <mi>R</mi> <mo>,</mo> <mi>org</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>x</mi> <mo>-</mo> <msubsup> <mi>d</mi> <mi>org</mi> <mi>L</mi> </msubsup> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>)</mo> </mrow> </mtd> </mtr> </mtable> </mfenced> </math> Wherein cos () is a cosine-taking function,represents { R_org(x, y) } coordinate position ofThe amplitudes of the pixel points of (a) at different scales and directions,represents { R_org(x, y) } coordinate position ofThe phases of the pixel points in different scales and directions;

③ 6 according to { L_dis(x, y) } and { R }_disAmplitude and phase of each pixel point in (x, y) in different scales and directions, and { L }_org(x, y) } and { R }_orgParallax image between (x, y) } sCalculating the pixel value of each pixel point in S_disEnergy diagram at different scales and directions, and is recorded asWill be provided withThe pixel values of the pixel points with the middle coordinate position (x, y) in different scales and directions are recorded as <math> <mrow> <mfenced open='' close=''> <mtable> <mtr> <mtd> <msubsup> <mi>BE</mi> <mrow> <mi>&alpha;</mi> <mo>,</mo> <mi>&theta;</mi> </mrow> <mi>dis</mi> </msubsup> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>=</mo> <msup> <mrow> <mo>(</mo> <msubsup> <mi>LE</mi> <mrow> <mi>&alpha;</mi> <mo>,</mo> <mi>&theta;</mi> </mrow> <mrow> <mi>L</mi> <mo>,</mo> <mi>dis</mi> </mrow> </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>LE</mi> <mrow> <mi>&alpha;</mi> <mo>,</mo> <mi>&theta;</mi> </mrow> <mrow> <mi>R</mi> <mo>,</mo> <mi>dis</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>x</mi> <mo>-</mo> <msubsup> <mi>d</mi> <mi>org</mi> <mi>L</mi> </msubsup> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mtd> </mtr> <mtr> <mtd> <mo>+</mo> <msubsup> <mi>LE</mi> <mrow> <mi>&alpha;</mi> <mo>,</mo> <mi>&theta;</mi> </mrow> <mrow> <mi>L</mi> <mo>,</mo> <mi>dis</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>&times;</mo> <msubsup> <mi>LE</mi> <mrow> <mi>&alpha;</mi> <mo>,</mo> <mi>&theta;</mi> </mrow> <mrow> <mi>R</mi> <mo>,</mo> <mi>dis</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>x</mi> <mo>-</mo> <msubsup> <mi>d</mi> <mi>org</mi> <mi>L</mi> </msubsup> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>,</mo> <mi>Y</mi> <mo>)</mo> </mrow> <mo>&times;</mo> <mi>cos</mi> <mrow> <mo>(</mo> <msubsup> <mi>LP</mi> <mrow> <mi>&alpha;</mi> <mo>,</mo> <mi>&theta;</mi> </mrow> <mrow> <mi>L</mi> <mo>,</mo> <mi>dis</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>-</mo> <msubsup> <mi>LP</mi> <mrow> <mi>&alpha;</mi> <mo>,</mo> <mi>&theta;</mi> </mrow> <mrow> <mi>R</mi> <mo>,</mo> <mi>dis</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>x</mi> <mo>-</mo> <msubsup> <mi>d</mi> <mi>org</mi> <mi>L</mi> </msubsup> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>)</mo> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>,</mo> </mrow> </math> Wherein,represents { R_dis(x, y) } coordinate position ofThe amplitudes of the pixel points of (a) at different scales and directions,represents { R_dis(x, y) } coordinate position ofThe phase of the pixel point in different scales and directions.

6. The objective evaluation method for stereo image quality based on energy map as claimed in claim 5, wherein T is taken in the step (iv)₁Is 16.

7. The method as claimed in claim 6, wherein in step (c) γ 0.7755 and β 0.0505 are included.