Stereo image quality evaluation method based on Steerable Pyramid decomposition

Abstract

Abstract: In recent years, with 3D movies, 3D TV and 3D games development, stereo image has become a hot research topic. The quality assessment of stereo image is an important technology in this field. Against this problem, we utilize the steerable pyramid decomposition which has 4 scales and 12 orientations on the left image, right image and the disparity map that gain from the minimum error energy in the left and right image to get 3 high frequency sub-bands and 114 orientation sub-bands. We extract bivariate generalized Gaussian distribution of scale and shape parameters from 48 sub-bands which are got from the left image and the right image. Then we extract correlations across scales, spatial correlation statistical features from all orientation sub-bands. These features put into the support vector regression (SVR) trained to predict the stereo image quality score. The experimental results shows that the objective evaluation model used in this paper, SROCC and CC are more than 0.93 in the 3D LIVE database, and it has good consistency with the subjective evaluation of human.

Key words: Support vector regression (SVR)

CLC Number:

TP391

References

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