Abstract

Quality assessment of 3D images presents many challenges when attempting to gain better understanding of the human visual system. In this paper, we propose a new 3D image quality prediction approach by simulating receptive fields (RFs) of human visual cortex. To be more specific, we extract the RFs from a complete visual pathway, and calculate their similarity indices between the reference and distorted 3D images. The final quality score is obtained by determining their connections via support vector regression. Experimental results on three 3D image quality assessment databases demonstrate that in comparison with the most relevant existing methods, the devised algorithm achieves high consistency alignment with subjective assessment, especially for asymmetrically distorted stereoscopic images.

© 2016 Optical Society of America

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