Quantifying and Exploiting VR Frame Correlations: An Application of a Statistical Model for Viewport Pose

In virtual reality (VR), users' head pose, that is, the location and the orientation of users' viewport, determines the view of the virtual world that is shown to the users. The importance of the viewport pose to VR experiences calls for the development of VR viewport pose models. However, no study has obtained a full pose (the position and the orientation) model applicable to modeling the viewport pose in VR experiences. In this paper, informed by our experimental measurements of viewport trajectories across 4 different types of VR interfaces, we first develop a statistical model of viewport poses in VR environments. Based on the developed model, we examine the correlations between pixels in VR frames that correspond to different viewport poses, and obtain an analytical expression for the visibility similarity (ViS) of the pixels across different VR frames. We then propose a lightweight ViS-based algorithm (ALG-ViS) that adaptively splits VR frames into the background and the foreground, reusing the background across different frames. Our implementation of ALG-ViS in two Oculus Quest 2 rendering systems demonstrates ALG-ViS running in real time, supporting the full VR frame rate, and outperforming baselines on measures of frame quality and bandwidth consumption.