The video resolutions can be limited due to the available content or limited transmission bandwidth. To improve the viewing experiences of low-resolution videos on high-resolution displays, super-resolution algorithms can be used to upsample the video while keeping the frames sharp and clear. Unfortunately, state-of-the-art super-resolution algorithms are computationally complex and cannot deliver the throughput required to support real-time upsampling of the low-resolution videos streamed from the cloud.
In this project, we develop FAST, a framework to accelerate any image based super-resolution algorithm by leveraging embedded information in compressed videos. FAST exploits the similarity between adjacent frames in a video. Given the output of a super-resolution algorithm on one frame, the technique adaptively transfers super-resolution pixels to the adjacent frames to avoid running super-resolution on those frames. The transferring process has negligible computation cost because the required formation including motion vectors, block size, and prediction residual are embedded in the compressed video for free. We show that FAST accelerates state-of-the-art super-resolution algorithms (e.g. SR-CNN) by up to an order of magnitude with acceptable quality loss up to 0.2 dB. FAST is an important step towards enabling real-time super-resolution on streamed videos for large displays.