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High-frequency displays are gaining immense popularity because of theirincreasing use in video games and virtual reality applications. However, theissue is that the underlying GPUs cannot continuously generate frames at thishigh rate -- this results in a less smooth and responsive experience.Furthermore, if the frame rate is not synchronized with the refresh rate, theuser may experience screen tearing and stuttering. Previous works proposeincreasing the frame rate to provide a smooth experience on modern displays bypredicting new frames based on past or future frames. Interpolation andextrapolation are two widely used algorithms that predict new frames.Interpolation requires waiting for the future frame to make a prediction, whichadds additional latency. On the other hand, extrapolation provides a betterquality of experience because it relies solely on past frames -- it does notincur any additional latency. The simplest method to extrapolate a frame is towarp the previous frame using motion vectors; however, the warped frame maycontain improperly rendered visual artifacts due to dynamic objects -- thismakes it very challenging to design such a scheme. Past work has used DNNs toget good accuracy, however, these approaches are slow. This paper proposesExwarp -- an approach based on reinforcement learning (RL) to intelligentlychoose between the slower DNN-based extrapolation and faster warping-basedmethods to increase the frame rate by 4x with an almost negligible reduction inthe perceived image quality.

ExWarp: Extrapolation and Warping-based Temporal Supersampling for High-frequency Displays