No-reference stereoscopic image quality assessment based on hue summation-difference mapping image and binocular joint mutual filtering.

Abstract

The no-reference (NR) quality assessment for stereoscopic images plays a significant role in 3D technology, but it also faces great challenges. In this paper, a novel NR stereo image quality assessment (SIQA) method is proposed. Based on the human visual system, this method mimics the summation and difference channels, which consider the binocular interactive perception property, to process the visual information. Especially, the summation and difference images are calculated by the contrast of hue and luminance in color patches. Meanwhile, considering the interactive filtering between the left and right viewpoints, this method uses the filtered information as the weighting factor to integrate the visual information of the summation and difference channels to form the summation-difference mapping image (SDMI). Then, energy entropy, bivariate generalized Gaussian distribution for the joint distribution of SDMI and the depth map subband coefficients, and the local log-Euclidean multivariate Gaussian descriptor are detected as the feature descriptors. Support vector regression, trained by the features, is utilized to predict the quality of stereoscopic images. Experimental results demonstrate that the proposed algorithm achieves high consistency with subjective assessment on four SIQA databases.