Name
Abstract | ||
|---|---|---|
This paper provides a technical overview of a deep-learning-based encoder method aiming at optimizing next generation hybrid video encoders for driving the block partitioning in intra slices. An encoding approach based on Convolutional Neural Networks is explored to partly substitute classical heuristics-based encoder speed-ups by a systematic and automatic process. The solution allows controlling the trade-off between complexity and coding gains, in intra slices, with one single parameter. This algorithm was proposed at the Call for Proposals of the Joint Video Exploration Team (JVET) on video compression with capability beyond HEVC. In All Intra configuration, for a given allowed topology of splits, a speed-up of ×2 is obtained without BD-rate loss, or a speed-up above ×4 with a loss below 1% in BD-rate. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/DCC.2019.00024 | 2019 Data Compression Conference (DCC) |
Keywords | Field | DocType |
video coding,deep learning,encoding method | Computer vision,Computer science,Artificial intelligence,Encoding (memory) | Conference |
ISSN | ISBN | Citations |
1068-0314 | 978-1-7281-0658-8 | 0 |
PageRank | References | Authors |
0.34 | 1 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Franck Galpin | 1 | 0 | 1.69 |
| Fabien Racapé | 2 | 6 | 1.85 |
| Sunil Prasad Jaiswal | 3 | 42 | 7.94 |
| Philippe Bordes | 4 | 8 | 4.71 |
| Fabrice Le Léannec | 5 | 0 | 1.35 |
| Edouard François | 6 | 155 | 27.66 |