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This paper presents a novel model family that we call SUPERVEGAN, for the problem of video enhancement for low bitrate streams by simultaneous video super resolution and removal of compression artifacts from low bitrates (e.g. 250Kbps). Our strategy is fully end-to-end, but we upsample and tackle the problem in two main stages. The first stage deals with removal of streaming compression artifacts and performs a partial upsampling, and the second stage performs the final upsampling and adds detail generatively. We also use a novel progressive training strategy for video together with the use of perceptual metrics. Our experiments shown resilience to training bitrate and we show how to derive real-time models. We also introduce a novel bitrate equivalency test that enables the assessment of how much a model improves streams with respect to bitrate. We demonstrate efficacy on two publicly available HD datasets, LIVE-NFLX-II and Tears of Steel (TOS). We compare against a range of baselines and encoders and our results demonstrate our models achieve a perceptual equivalence which is up to two times over the input bitrate. In particular our 4X upsampling outperforms baseline methods on the LPIPS perceptual metric, and our 2X upsampling model also outperforms baselines on traditional metrics such as PSNR.

SUPERVEGAN: Super Resolution Video Enhancement GAN for Perceptually Improving Low Bitrate Streams