CN107492085B - Stereo image quality evaluation method based on dual-tree complex wavelet transform - Google Patents
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Abstract
The invention relates to a full-reference stereo image quality evaluation method based on dual-tree complex wavelet transform, which comprises the following steps: 1) respectively fusing a left image and a right image for the original stereo image and the distorted stereo image; 2) performing dual-tree complex wavelet transform on the left image, the right image and the composite image of the original stereo image and the distorted stereo image; 3) extracting contrast, structure and brightness characteristics from wavelet sub-bands of the original graph and the distortion graph; 4) and performing energy calculation on each wavelet sub-band of the original stereo image, weighting the energy of each sub-band after performing wavelet decomposition on the left image, the right image and the composite image of the original stereo image by adopting a gain control method, and performing stereo image quality evaluation by combining the characteristics of brightness, contrast, structure and the like of each wavelet sub-band of the original image and the distorted image to obtain a final quality evaluation score.
Description
Technical Field
The invention belongs to the field of image processing, in particular relates to an objective image evaluation system for stereo image quality, and relates to an objective image evaluation method applying dual-tree complex wavelet transform and a stereo image synthetic image.
Background
With the advent of the age of multimedia information, digital signals typified by images and videos are affecting and changing people's life and working patterns. Due to the rapid development of imaging technology and display equipment and the popularization of stereo images, the visual quality experience of users is greatly improved. However, the quality of stereo images is degraded during the processes of acquisition, preprocessing, encoding, transmission, decoding, etc., so that the evaluation of the quality of stereo images has become an important research subject. In the existing stereo image quality evaluation methods, the most direct method is to respectively evaluate the left image and the right image by adopting the statistical characteristics of a plane image quality evaluation method, and finally predict the quality of the obtained stereo image, but the method does not consider the complex visual perception mechanism of human, so the evaluation performance is poor. The synthesis graph [1] integrates parallax information of visual perception, so that the consistency of the quality evaluation of the stereo image and the subjective evaluation is higher. Therefore, the invention provides a method for evaluating the quality of a stereo image by applying the traditional plane image quality evaluation method to extract the characteristics of the wavelets of the left image, the right image and the composite image on each subband.
[1]Chen M J,Su C C,Kwon D K,et al.Full-reference quality assessment of stereopairs accounting for rivalry[J].Signal Processing:Image Communication,2013,28(9):1143-1155.
[2]Wang Z,Bovik A C,Sheikh H R,et al.Image quality assessment:from error visibility to structural similarity[J].IEEE transactions on image processing,2004,13(4):600-612.
Disclosure of Invention
The invention aims to provide a full-reference stereo image quality evaluation method capable of obtaining a better stereo image quality evaluation effect aiming at the stereo distortion image quality evaluation problem. The invention relates to a method for evaluating the quality of a stereo image, which comprises the steps of performing wavelet decomposition on a left image, a right image and a composite image of an original stereo image and a left image, a right image and a composite image of a distorted stereo image, extracting the characteristics of contrast, structure and brightness on each wavelet subband, and performing weight fusion on the corresponding characteristics by using the energy of each wavelet subband of the left image, the right image and the composite image of the original stereo image. The technical scheme is as follows:
a full-reference stereo image quality evaluation method based on dual-tree complex wavelet transform utilizes wavelet sub-band energy of a left image, a right image and a synthetic image of a stereo image to weight wavelet sub-band characteristics to evaluate the quality of a distorted stereo image, and comprises the following steps:
1) performing left image and right image fusion on the original stereo image and the distorted stereo image respectively
For an original stereo image and a distorted stereo image, calculating left parallax by a left image and a right image, adding the left image and the left parallax to obtain a left parallax image, calculating weights by the left image and the left parallax image to synthesize a synthetic image, and obtaining image weights by normalizing Gabor filtering energy response;
2) performing dual-tree complex wavelet transform on the left image, the right image and the composite image of the original stereo image and the distorted stereo image
Each image is decomposed into 3 layers eachLayers 6 directions, namely:
they represent the wavelet coefficients of the subbands of ± 15 °, ± 45 °, ± 75 ° of the nth layer, respectively, n being 1,2, 3; obtaining 18 wavelet sub-bands;
3) extracting contrast, texture and brightness features from the wavelet sub-bands of the original and distorted images
Taking the contrast, structure and brightness of each wavelet sub-band of the original stereo image and the left image, the right image and the composite image of the distorted stereo image as features for extraction;
4) energy computation on wavelet subbands of the original stereo image, and feature fusion
And performing energy calculation on each wavelet sub-band of the original stereo image, weighting the energy of each sub-band after performing wavelet decomposition on the left image, the right image and the composite image of the original stereo image by adopting a gain control method, and performing stereo image quality evaluation by combining the characteristics of brightness, contrast, structure and the like of each wavelet sub-band of the original image and the distorted image to obtain a final quality evaluation score.
The invention carries out wavelet decomposition on the left image, the right image and the composite image of the original stereo image, takes the energy of each sub-band after the wavelet decomposition as the gain control weight, fuses the brightness, the contrast and the structural characteristics of each image, and evaluates the quality of the distorted stereo image to obtain the quality evaluation score. The method has strong consistency with subjective image quality evaluation, and the performance is superior to most of the existing three-dimensional image quality evaluation algorithms.
Drawings
FIG. 1 flow chart of the present invention
FIG. 2 three-level dual-tree complex wavelet transform
FIG. 3 uniformity
Detailed Description
The invention provides a full reference image quality evaluation method based on extracting wavelet decomposition sub-band energy on a left image, a right image and a composite image of an original stereo image as gain control weight. In order to make the technical scheme of the invention clearer, referring to fig. 1, the specific steps of the invention are as follows.
1. Left and right image fusion
When the three-dimensional images are altered in different degrees, due to the existence of parallax, the subjective quality of the images cannot be obtained only by the mean value of the quality of the left and right images, the three-dimensional image fused with parallax information is different from a plane image, and the parallax information is mainly considered in the synthesis image obtained by fusing the left and right images, so that the left and right images are processed as follows, the left and right images are subjected to Gabor filtering, and the two-dimensional complex Gabor filtering is defined as follows:
wherein R is1＝xcosθ+ysinθ，R2＝-sinθ+yconθ。σx，σyIs the standard deviation, ζx，ζyIs the spatial frequency and theta is the filter direction. The energy response values of the left image and the right image in all scales and directions are GE respectivelyL，GER。
The calculation of the left and right weights is obtained by normalizing the Gabor filter energy response assignment, and is defined as:
the present invention defines the composite map as:
C(x，y)＝wL(x，y)·IL(x，y)+wR(x+d，y)·IR(x+d，y) (4)
wherein C represents a synthetic diagram, ILAnd IRRepresenting left and right images, d is parallax, WLAnd WRFor left and right weighting, the invention uses the parallax information of left and right images to synthesize a synthetic image for quality evaluation of stereo images, and prepares for extracting features.
2. Dual tree complex wavelet transform
The present invention performs wavelet decomposition on the left image, the right image and the composite image because the peak value of the wavelet coefficient can reflect the sharpness of the image. The dual-tree complex wavelet transform is developed from the traditional wavelet transform, the traditional wavelet transform is decomposed into detail information in 3 directions of the horizontal direction, the vertical direction and the oblique direction, the dual-tree complex wavelet transform inherits the excellent characteristics of the traditional wavelet transform and can describe the directional information of an image more, the complex wavelet transform is realized through real wavelet transform, the real part and the imaginary part are separated, and the wavelet transform coefficients of the real part and the imaginary part are obtained through two parallel real filter banks. The three-layer dual-tree complex wavelet transform performed by the present invention is shown in fig. 2.
The invention decomposes each image into 3 layers, 6 directions per layer, namely:
they represent the wavelet coefficients of the subbands of ± 15 °, ± 45 °, ± 75 ° of the nth layer, respectively, with n being 1,2, 3. Energy calculation is carried out on each sub-band of the wavelet, and the energy calculation is used as the weight of the final calculated quality fraction
3. Representation of features
The three aspects of contrast, texture and brightness all directly affect the image quality, and are independent of each other. The invention takes the contrast, structure and brightness characteristics of wavelet sub-bands of the left and right images and the composite image of the stereo image as characteristic extraction. The image contrast, texture and brightness features are extracted separately as follows:
1) contrast is defined as the standard deviation σ of the signalxThe similarity of contrast is defined as:
C1is a very small constant.
For both signals x and y, the luminance is defined as the base mean value, i.e.:
wherein N is the length of the signal.
2) The structural similarity is defined as:
3) the similarity of luminance is defined as:
in the formula, C2Is a very small constant, ensures
The values are stable when close to 0.
The image Structure Similarity (SSIM) [2] is defined as:
SSIM(x，y)＝[L(x，y)]α[S(x，y)]β[F(x，y)]γ (9)
in the formula, α, β, γ are all numbers greater than 0, and the importance of 3 components in the mass fraction can be changed by adjusting the magnitude of these 3 parameters.
4. Feature fusion
The invention adopts a gain control method to take the energy of each sub-band after wavelet decomposition of the left image, the right image and the composite image as weight, and combines the characteristics of brightness, contrast, structural characteristics and the like of each wavelet sub-band of the image to evaluate the quality of the stereo image. The effect is not achieved by a simple linear combination mode. The formula is as follows:
wherein
EL、ERIn a similar manner to that described above,
and (3) representing the energy of the ith layer in the jth direction after wavelet decomposition of the left image:
SSIM(IL，IL′) Representing the structural similarity of the original left image and the distorted right image,
and Q is the final mass fraction.
5. Database selection
The invention selects two open test libraries, which are an asymmetric stereo image test library LIVE-3D II and a symmetric stereo image test library LIVE-3D I provided by LIVE laboratories. In the LIVE3D image quality evaluation library II, a total of 360 distorted stereo images and 8 original images are provided, the library includes 2 kinds of symmetric and asymmetric distortions, and also includes 5 kinds of distortion types, i.e., JPEG compression, JPEG2000(JP2K), Gaussian Blur (GBLUR), White Noise (WN), and Fast Fading (FF), and subjective score difference and disparity value of each group of distorted stereo images are given. In LIVE3D image quality evaluation library I, 365 distorted stereo images and 20 original images are symmetrical distortions with the same left and right distortion degree, and comprise 5 distortion types of JPEG, JP2K, GBLUR, WN and FF.
In order to prove that the predicted objective quality fraction and the subjective quality fraction of the image obtained by the method have high consistency, and the predicted objective quality fraction can accurately reflect the quality of the image, the method is tested on a symmetrical and asymmetrical stereo image test library LIVE-3D II and LIVE-3D I, 3 internationally commonly used indexes for measuring an objective image quality evaluation algorithm are used for evaluating the performance of the method, and the 3 indexes are respectively a Spearman rank-order correlation coefficient (SRCC), a Pearson Linear Correlation Coefficient (PLCC) and a Root Mean Square Error (RMSE), wherein the PLCC and RMSE indexes measure the prediction accuracy of an objective algorithm, and the SRCC indexes measure the prediction monotonicity of the objective algorithm. The closer the values of PLCC and SRCC are to 1, the smaller the value of RMSE is, the better the algorithm performance is, and the higher the correlation between the predicted objective quality score and the subjective quality score is.
6. Comparing and analyzing algorithm performance
The algorithm performance is verified on a stereo image test library LIVE-3D I and LIVE-3D II, and the method has good effect as can be seen from the following comparison. Table 1 shows the performance of the present invention on LIVE3D Phase I test library, and it can be seen from Table 1 that the present invention achieves 0.944 for symmetric Gaussian blur distortion PLCC and 0.937 for Gaussian white noise distortion SPRCC. As can be seen from Table 2, the present invention achieves PLCC of 0.971 and SPRCC of 0.947 for asymmetric Gaussian blur mixture distortion on LIVE3D Phase II test library. Tables 1 and 2 compare the method of the present invention with other methods in LIVE library, and it can be seen from the tables that the degree of correlation and accuracy of the method of the present invention with subjective evaluation are both significantly improved, and fig. 3 shows the correlation of subjective and objective scores, which indicates that the correlation of the predicted objective quality score and the subjective quality score of the method of the present invention is high.
TABLE 1 results of the consistency calculation for 5 distortions in LIVE3D Phase I
TABLE 2 results of the consistency calculation for 5 blending distortions in LIVE3D Phase II
The method of the invention has the following advantages:
(1) the method of the invention has high consistency with subjective stereo image quality evaluation.
(2) Different from plane image quality evaluation, the invention considers the special parallax information of the stereo image and provides the characteristics of image brightness, structure and contrast ratio extracted from each sub-band of the wavelet of the synthetic image, and the experimental result shows that the performance of the method of the invention is superior to that of most stereo image quality evaluation algorithms existing at present.
Claims (1)
1. A full-reference stereo image quality evaluation method based on dual-tree complex wavelet transform weights all wavelet sub-band characteristics by taking the energy of each wavelet sub-band of a left image, a right image and a composite image of a stereo image as a weight, and further performs quality evaluation on a distorted stereo image, and comprises the following steps:
1) and respectively fusing a left image and a right image for the original stereo image and the distorted stereo image, wherein the method comprises the following steps:
for an original stereo image, calculating a left parallax by an original left image and an original right image, adding the left image and the left parallax to obtain a left parallax image, obtaining image weights by normalizing Gabor filtering energy response, and weighting the left image and the left parallax image by the obtained image weights to obtain a synthetic image of the original stereo image, which is called as an original synthetic image; for the distorted left image and the distorted right image of the distorted stereo image, obtaining a composite image of the distorted stereo image according to the same processing mode as the processing mode, and the composite image is called as a distorted composite image;
2) the method for performing dual-tree complex wavelet transform on the left image, the right image and the composite image of the original stereo image and the distorted stereo image comprises the following steps:
combining the original stereo image withThe left image, right image and composite map of the distorted stereo image are decomposed into 3 layers, 6 directions per layer, namely:
they represent the wavelet coefficients of the subbands of ± 15 °, ± 45 °, ± 75 ° of the nth layer, respectively, n being 1,2, 3; obtaining 18 wavelet sub-bands;
3) the method for extracting contrast, structure and brightness features from wavelet sub-bands of a left image, a right image and a composite image of an original stereo image and a distorted stereo image respectively comprises the following steps:
the contrast, the structure and the brightness of each wavelet sub-band of the left image, the right image and the composite image of the original stereo image and the distorted stereo image are taken as characteristic extraction, and the image structure similarity between each wavelet sub-band of the original left image and each wavelet sub-band of the distorted left image can be obtained by multiplying the contrast, the structure and the brightness characteristics
Image structure similarity between wavelet sub-bands of original right image and wavelet sub-bands of distorted right image
Image structure similarity between wavelet sub-bands of original composite image and wavelet sub-bands of distorted composite image
Used for calculating the final quality evaluation score in the step 4); wherein
Wavelet sub-bands respectively representing the ith direction of the original left image and the ith direction of the distorted left image;
wavelet sub-bands respectively representing ith directions of the original right image and the distorted right image;
wavelet sub-bands of the ith direction of the original composite map and the distortion composite map are respectively represented;
4) respectively carrying out energy calculation and feature fusion on the left image and the right image of the original stereo image and 18 wavelet sub-bands of the synthetic image, wherein the method comprises the following steps:
respectively carrying out energy calculation on 18 wavelet sub-bands of a left image, a right image and a composite image of an original stereo image, taking the energy of each sub-band after wavelet decomposition of the left image, the right image and the composite image of the original stereo image as a weight, and carrying out stereo image quality evaluation by combining the brightness, the contrast and the structural characteristics of each wavelet sub-band of the original image and the distorted image to obtain a final quality evaluation score:
wherein EL、ER、ECWavelet coefficient energies of the original left image, the original right image and the original composite image, respectively, wherein
Representing the energy of the ith layer in the jth direction after wavelet decomposition of the original left image:
ηL,
wavelet sub-bands in jth direction of ith layer of original left image
A real part and an imaginary part;
ηR,
wavelet sub-bands in jth direction of ith layer of original right image
The real part and the imaginary part are,
ηC,
wavelet sub-bands in jth direction of ith layer of original synthesis image
The real part and the imaginary part are,
and Q is the final quality evaluation score.
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