Spatiotemporal multi-feature evaluation of visually induced motion sickness in virtual reality

Visually induced motion sickness(VIMS)in immersive virtual reality experience is an important problem that impedes the development and applications of virtual reality systems. Most of the existing assessment methods based on visual content are not comprehensive enough, and the extracted features of motion information are relatively simple, and the influence of abrupt changes in time domain on motion sickness is rarely considered. To solve these problems, a spatio-temporal multi-feature assessment model for VIMS in virtual reality was proposed. The spatial and temporal information of stereoscopic panoramic video was used to design the assessment model of VIMS. The weighted motion features more in line with human perception were adopted, and the feature extraction method was designed considering the temporal mutation information of stereoscopic panoramic video. The proposed model was divided into preprocessing module, feature extraction module and time domain aggregation and regression module. The prepro-cessing module was used to extract the viewport images and estimate the optical flow map, disparity map and saliency map. The feature extraction module included foreground-background weighted motion feature extraction, disparity feature extraction based on transform domain, spatial feature extraction and time domain abrupt change feature extraction. VIMS evaluation scores were finally obtained through time domain aggregation and support vector regression. The experimental results show that the PLCC, SROCC and RMSE of the proposed model are 0.821, 0.790 and 0.489, respectively when tested on the stereoscopic panoramic video database SPVCD. The model achieves excellent prediction performance which verifies the effectiveness of the proposed feature extraction module. © 2024 Chinese Academy of Sciences. All rights reserved.