A novel method for VR sickness reduction based on dynamic field of view processing

Abstract In this paper, we proposed a novel method for virtual reality (VR) sickness reduction based on dynamic field of view (FOV) processing. Dynamic FOV processing is performed based on the estimated VR sickness for each video frame. The level of sickness is estimated using VR sickness model, which is obtained by defining the relationship between the motion information and the measured VR sickness. For motion information analysis, subregion-based correspondence points tracking is used to efficiently remove outliers and prevent prediction error propagation. Amount of head dispersion is used as a quantitative VR sickness measure, which can be calculated from inertial measurement unit sensor in VR devices. The optimal FOV range was determined by experimentally validating a minimum FOV that can effectively reduce VR sickness with almost negligible loss in presence. The simulation results show a significant decrease of 37% compared to full FOV viewing, when FOV is dynamically varied between full and 60°.