Cyclopean Image Based Stereoscopic Image Quality Assessment by Using Sparse Representation

Abstract

3D image quality assessment confronts more difficulties than 2D image quality assessment. In this paper a 3D image quality assessment metric based on sparse representation was proposed. The contributions of the proposed method mainly include the following points: a color cyclopean image is used to better simulate the process of image processing in human brain, which is also very suitable for evaluating the quality of asymmetric distortion image. Meanwhile, for during sparse reconstruction some important information will be lost, we use the corresponding color cyclopean image to do compensation before feature extracting. And the paper creatively extracts spatial and spectral entropy feature of the distortion color cyclopean image and the corresponding reconstruction cyclopean image, respectively. Finally, we uses SVR to evaluate the quality of stereoscopic image. Experimental results show that the proposed method is very much in line with human visual perception.