Using Independent Component Analysis and Binocular Combination for Stereoscopic Image Quality Assessment

In this paper, a full reference stereoscopic image quality assessment (FR-SIQA) method is proposed based on independent component analysis (ICA) and binocular combination. Image features that reflect the responds of simple cells in the cortex are extracted by ICA-based algorithm. Both image feature similarity (IFS) and local luminance consistency (LLC) are calculated to measure the structure and brightness distortions, respectively. To simulate the binocular fusion properties, the energy of image features and the global relative luminance information are selected as the basic of binocular combination to fuse the right-left IFS and LLC into a final index. Experimental results demonstrate that the proposed algorithm achieves high consistency with subjective assessment on two public available 3D image quality assessment databases.