High-frequency displays are gaining immense popularity because of their increasing use in video games and virtual reality applications. However, the issue is that the underlying GPUs cannot continuously generate frames at this high rate -- this results in a less smooth and responsive experience. Furthermore, if the frame rate is not synchronized with the refresh rate, the user may experience screen tearing and stuttering. Previous works propose increasing the frame rate to provide a smooth experience on modern displays by predicting new frames based on past or future frames. Interpolation and extrapolation are two widely used algorithms that predict new frames. Interpolation requires waiting for the future frame to make a prediction, which adds additional latency. On the other hand, extrapolation provides a better quality of experience because it relies solely on past frames -- it does not incur any additional latency. The simplest method to extrapolate a frame is to warp the previous frame using motion vectors; however, the warped frame may contain improperly rendered visual artifacts due to dynamic objects -- this makes it very challenging to design such a scheme. Past work has used DNNs to get good accuracy, however, these approaches are slow. This paper proposes Exwarp -- an approach based on reinforcement learning (RL) to intelligently choose between the slower DNN-based extrapolation and faster warping-based methods to increase the frame rate by 4x with an almost negligible reduction in the perceived image quality.

翻译：高频显示器因其在视频游戏和虚拟现实应用中的普及而广受欢迎。然而，问题在于底层GPU无法持续以如此高的帧率生成画面——这会导致体验不够流畅且响应性不足。此外，若帧率与刷新率未同步，用户可能遇到画面撕裂与卡顿。先前研究提出通过基于过去或未来帧预测新帧来提升帧率，从而在现代显示器上提供流畅体验。插值与外推是两种广泛使用的帧预测算法。插值需要等待未来帧才能进行预测，这会引入额外延迟。而外推仅依赖历史帧，不会产生额外延迟，因此能提供更优质的体验。最简单的帧外推方法是利用运动矢量对前一帧进行扭曲；然而，扭曲帧可能因动态物体产生渲染伪影——这使得设计此类方案极具挑战性。过往工作利用DNN获得高精度，但这些方法速度较慢。本文提出ExWarp——一种基于强化学习（RL）的方法，能智能选择较慢的DNN外推与较快的扭曲方法，从而实现4倍帧率提升，且感知图像质量几乎无损。