Objective Stereo Image Quality Assessment Model based on Matrix Decomposition

Abstract

Stereo image quality assessment (SIQA) is a key issue of stereo image processing. Image pixels have strong correlation and highly structured features, according to that an image quality mainly depends on the structure information distortion of the image, an objective stereo image quality assessment (OSIQA) model based on matrix decomposition is proposed. Firstly, the concavity and convexity maps of image are extracted through Hessian matrix decomposition, which reflects complexity of image, and the left-right image quality assessment (LR-IQA) value is gained by judging loss severity of concavity and convexity map, which is adopting singular value decomposition in the left and right images. Secondly, eigenvalues and eigenvectors of the absolute difference map that is the absolute differential value between the left image and right image in stereo image are extracted. Eigenvalues can reflect image energy of some directions, and eigenvectors can reflect the directionality of image. Depth perception quality assessment (DP-QA) value is gained by calculating the degree of the structure distortion under the edge and non-edge regions. Finally, OSIQA value is obtained through nonlinearly fitting of LR-IQA value and DP-QA value. Experimental results show that the proposed OSIQA model have a good consistency with subjective perception. The correlation coefficient and spearman rank order correlation coefficient between OSIQA model and subjective perception are more than 0.92, and rooted mean squared error is lower than 6.5. Index Terms—Stereo image quality assessment, left-right image quality assessment, depth perception quality assessment, Hessian matrix