A Risk-Aware Pairwise Rank Learning Approach for Visual Discomfort Prediction of Stereoscopic 3D

Abstract

For visual discomfort prediction (VDP) of stereoscopic 3D images, a common two-stage framework is to first extract features that are predictive of the experienced visual discomfort level when viewing stereoscopic images and then use typical regression tools to learn the mapping from the extracted features to visual discomfort scores. Most existing approaches for stereoscopic 3D VDP focus on the former stage, i.e., feature extraction, while limited efforts have dedicated to exploiting more powerful and robust learning algorithms in this field. In this letter, inspired by the pairwise comparison-based subjective evaluation methodology, we propose a novel Risk-Aware Pairwise Rank Learning (RAPRL) approach to further improve the prediction accuracy. Unlike the traditional VDP approaches using different regression tools for feature-score mapping, our proposed RARL method addresses this problem based on a completely different pairwise rank learning framework with a risk-aware constraint. Experiments have verified the effectiveness and robustness of our proposed VDP model using RAPRL as the learning algorithm.