Name
Title | ||
|---|---|---|
Machine learning-based H.264/AVC to HEVC transcoding via motion information reuse and coding mode similarity analysis. |
Abstract | ||
|---|---|---|
High-efficiency video coding (HEVC), which is the latest video coding standard, is expected to have a dominant position in the market in the near future. However, most video resources are now encoded using the H.264/AVC standard. Consequently, there is a growing need for fast H.264/AVC to HEVC transcoders to facilitate the migration to the updated standard. This paper proposes a fast H.264/AVC to ... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1049/iet-ipr.2018.5703 | IET Image Processing |
Keywords | Field | DocType |
Bayes methods,code standards,computational complexity,feature selection,image classification,image motion analysis,learning (artificial intelligence),transcoding,trees (mathematics),video coding | Transcoding,Similarity analysis,Naive Bayes classifier,Pattern recognition,Feature selection,Reuse,Coding (social sciences),Artificial intelligence,Classifier (linguistics),Mathematics,Motion vector | Journal |
Volume | Issue | ISSN |
13 | 1 | 1751-9659 |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongwei Lin | 1 | 381 | 38.62 |
| Xiaohai He | 2 | 14 | 10.40 |
| Linbo Qing | 3 | 38 | 14.63 |
| Shan Su | 4 | 1 | 0.36 |
| Shuhua Xiong | 5 | 10 | 2.50 |