Name
Abstract | ||
|---|---|---|
ABSTRACTWith 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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3485983.3494856 | CONEXT |
DocType | Citations | PageRank |
Conference | 1 | 0.35 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Duin Baek | 1 | 2 | 0.71 |
| Dasari, Mallesham | 2 | 9 | 4.22 |
| Samir R. Das | 3 | 5341 | 494.55 |
| Jihoon Ryoo | 4 | 1 | 0.69 |