ExWarp: Extrapolation and Warping-based Temporal Supersampling for
  High-frequency Displays
Akanksha Dixit Yashashwee Chakrabarty Smruti R. Sarangi
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.