Name
Title | ||
|---|---|---|
Fast Partitioning Decision Making For Prediction Units On H.264-To-Hevc Transcoding Using Machine Learning |
Abstract | ||
|---|---|---|
With the high computational complexity of transcoding video from the H.264/AVC standard to the state-of-the-art High Efficiency Video Coding (HEVC) standard, new approaches must be explored to fasten up the adaptation of all legacy content. This work proposes a fast decision algorithm to reduce the transcoding complexity between the H.264/AVC and HEVC video standards using partitioning information from the H.264/AVC macroblocks to fasten up the partitioning decisions of Prediciton Units (PUs) on the HEVC reencoding process. This strategy allowed a 25% reduction on transcoding time with a compression efficiency loss of just 0.745% in comparison with the original transcoder. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3323503.3349553 | WEBMEDIA 2019: PROCEEDINGS OF THE 25TH BRAZILLIAN SYMPOSIUM ON MULTIMEDIA AND THE WEB |
Keywords | Field | DocType |
H.264/AVC, HEVC, Video Coding, Machine Learning, Computational Complexity | Transcoding,Computer science,Artificial intelligence,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yan Soares | 1 | 0 | 0.34 |
| Guilherme Corrêa | 2 | 181 | 23.16 |
| luciano agostini | 3 | 60 | 9.52 |