Deep Objective Assessment Model Based on Spatio-Temporal Perception of 360-Degree Video for VR Sickness Prediction

PubDate: August 2019

Teams: KAIST

Writers: Kihyun Kim; Sangmin Lee; Hak GU Kim; Minho Park; Yong Man Ro

PDF: Deep Objective Assessment Model Based on Spatio-Temporal Perception of 360-Degree Video for VR Sickness Prediction

Abstract

In virtual reality (VR) environment, viewing safety is one of increasing concerns because of physical symptoms induced by VR sickness. Distortion of VR video is one of main causes. In this paper, we investigate the degradation of spatial resolution as distortion causing VR sickness. We propose a novel deep learning-based VR sickness assessment framework for predicting VR sickness caused by degradation of spatial resolution. The proposed method takes into account visual perception of 360-degree videos in spatio-temporal domain for assessing VR sickness. In cooperating visual quality and the temporal flickering with deep latent feature in training stage, the proposed network could effectively learn the spatio-temporal characteristics causing VR sickness. To evaluate the performance of the proposed method, we built a new dataset consisting of 360-degree videos and ground truths (physiological signals and SSQ scores). The dataset will be open publicly. Experimental results demonstrated that the proposed VR sickness assessment had a high correlation with human subjective scores.