Prediction of motion sickness degree of stereoscopic panoramic videos based on content perception and binocular characteristics

Abstract

The immersive viewing environment of Virtual Reality (VR) system inevitably brings motion sickness to users, and restricts the development of VR system to a certain extent. In order to solve the problem of motion sickness when users view, it is necessary to predicate the motion sickness degree of the visual content. However, the methods based on subjective questionnaires or physiological signals will become cumbersome and infeasible with the growth of demand and supply of VR content. Most of the existing prediction methods for motion sickness are designed for 2D panoramic video. In this paper, a motion sickness prediction method based on content perception and binocular characteristics is proposed for stereoscopic panoramic video. The proposed method mainly consists of two modules: feature extraction based on content analysis and time pooling. The feature extraction module takes into account the attention mechanism of human visual system and the multi-channel characteristics of retina, and simulates the sensory conflict and the three-stage process of binocular stereoscopic perception in virtual environment. The time pooling module takes into account the time memory effect of human eyes. The proposed method has achieved excellent prediction performance on stereoscopic panoramic video comfort database SPVCD, its predication results have a good correlation with the subjective scores, which shows that the proposed method can effectively predict the motion sickness degree of VR content.