dcSR
Duin Baek, Mallesham Dasari, Samir R. Das, Jihoon Ryoo
Abstract
With the next generation immersive video applications, network capacity is becoming a growing bottleneck to deliver a high quality video to end-users. Recent advances to tackle this challenge introduced super-resolution (SR) for video quality enhancement through neural computations by leveraging client-side compute capacity. However, the existing SR models are bulky, compute-, and memory-expensive, which makes it difficult to deploy them in practice. In this work, we present dcSR, a lightweight data-centric SR approach that enables a practical neural quality enhancement for videos. On the server-side, dcSR constructs micro SR models trained on a few selected frames from each video through a data-centric paradigm by employing a long term video scene understanding mechanism. On the client-side, dcSR integrates the micro SR models into the regular video decoder and enhances the video quality in real-time without compromising on quality enhancement. We evaluate dcSR and show its benefits by comparing it with previous methods.