Perceptual quality measurement of 3D images based on binocular vision

Three-dimensional (3D) technology has become immensely popular in recent years and widely adopted in various applications. Hence, perceptual quality measurement of symmetrically and asymmetrically distorted 3D images has become an important, fundamental, and challenging issue in 3D imaging research. In this paper, we propose a binocular-vision-based 3D image-quality measurement (IQM) metric. Consideration of the 3D perceptual properties of the primary visual cortex (V1) and the higher visual areas (V2) for 3D-IQM is the major technical contribution to this research. To be more specific, first, the metric simulates the receptive fields of complex cells (V1) using binocular energy response and binocular rivalry response and the higher visual areas (V2) using local binary patterns features. Then, three similarity scores of 3D perceptual properties between the reference and distorted 3D images are measured. Finally, by using support vector regression, three similarity scores are integrated into an overall 3D quality score. Experimental results for two public benchmark databases demonstrate that, in comparison with most current 2D and 3D metrics, the proposed metric achieves significantly higher consistency in alignment with subjective fidelity ratings.