Akanksha Dixit

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.

Akanksha Dixit, Smruti R. Sarangi

High-refresh rate displays have become very popular in recent years due to the need for superior visual quality in gaming, professional displays and specialized applications like medical imaging. However, high-refresh rate displays alone do not guarantee a superior visual experience; the GPU needs to render frames at a matching rate. Otherwise, we observe disconcerting visual artifacts such as screen tearing and stuttering. Temporal supersampling is an effective technique to increase frame rates by predicting new frames from other rendered frames. There are two methods in this space: interpolation and extrapolation. Interpolation-based methods provide good image quality at the cost of a higher latency because they also require the next rendered frame. On the other hand, extrapolation methods are much faster at the cost of quality. This paper introduces PatchEX, a novel frame extrapolation method that aims to provide the quality of interpolation at the speed of extrapolation. It smartly partitions the extrapolation task into sub-tasks and executes them in parallel to improve both quality and latency. It then uses a patch-based inpainting method and a custom shadow prediction approach to fuse the generated sub-frames. This approach significantly reduces the overall latency while maintaining the quality of the output. Our results demonstrate that PatchEX achieves a 65.29% and 48.46% improvement in PSNR over the latest extrapolation methods ExtraNet and ExtraSS, respectively, while being 6x and 2x faster, respectively.