Binocular Responses for No-Reference 3D Image Quality Assessment

Perceptual quality assessment of distorted three-dimensional (3D) images has become a fundamental yet challenging issue in the field of 3D imaging. In this paper, we propose a general-purpose blind/no-reference (NR) 3D image quality assessment (IQA) metric that utilizes the complementary local patterns (the local magnitude pattern and the proposed generalized local directional pattern) of binocular energy response (BER) and binocular rivalry response (BRR). The main technical contribution of this research is that binocular visual perception and local structural distribution are considered for NR 3D-IQA. More specifically, the metric simulates the binocular visual perception using BER and BRR. Subsequently, the local patterns of the binocular responses' encoding maps are used to form various binocular quality-predictive features, which will change in the presence of distortions. After feature extraction, we use k-nearest neighbors-based machine learning to drive the overall quality score. We tested our proposed metric against two publicly available 3D databases; these tests confirm that the proposed metric's results consistently align with human subjective judgments.