Learning natural statistics of binocular contrast for no reference quality assessment of stereoscopic images

Abstract

Algorithms for no-reference (NR) stereoscopic image quality assessment (SIQA) aim to evaluate the perceptual quality of a stereoscopic/3D image without the assistance of its reference. Current NR SIQA models often require training on 3D distorted images and their associated human opinion scores, which ultimately restrict their further application. In this paper, we present a simple yet effective NR SIQA model that does not require training on existing 3D image databases. Instead, we train our model on a large dataset of natural stereoscopic images based on learning the local statistics of the Cyclopean contrast maps, and then use the existing 2D NR IQA model to help guide the NR SIQA task. Experimental results demonstrate the efficacy of our proposed method.