Simulating binocular vision for no-reference 3D visual quality measurement.

Abstract

Perceptual quality measurement of three-dimensional (3D) visual signals has become a fundamental challenge in 3D imaging fields. This paper proposes a novel no-reference (NR) 3D visual quality measurement (VQM) metric that uses simulations of the primary visual cortex (V1) of binocular vision. As the major technical contribution of this study, perceptual properties of simple and complex cells are considered for NR 3D-VQM. More specifically, the metric simulates the receptive fields of simple cells (one class of V1 neurons) using Gaussian derivative functions, and the receptive fields of complex cells (the other class of V1 neurons) using disparity energy responses and binocular rivalry responses. Subsequently, various quality-aware features are extracted from the primary visual cortex; these will change in the presence of distortions. Finally, those features are mapped to the subjective quality score of the distorted 3D visual signal by using support vector regression (SVR). Experiments on two publicly available 3D databases confirm the effectiveness of our proposed metric, compared to the relevant full-reference (FR) and NR metrics.