A visual comfort metric for stereoscopic 3D video based on SMDE approach

Abstract

Visual comfort assessment for stereoscopic video is playing an important role for stereoscopic safety issue. In this paper, we propose a novel visual comfort assessment metric that utilizes interest regions detection approach, which is called Salient Motion Depth Extraction approach in our algorithm. In stereoscopic video shots, salient motion regions where human subjects focus on should have more weights in visual comfort assessment. To achieve better performance, our approach combines salient cues, motion cues with depth cues in order to extract salient motion regions in consideration of depth context. Our visual comfort assessment utilizes local analytical method based on attention model by analyzing disparity features in interest regions extracted by Salient Motion Depth Extraction approach. The experimental results have demonstrated that our proposed visual comfort assessment improves the correlation with the subjective assessment.