Name
Title | ||
|---|---|---|
Efficient Motion Vector Re-Estimation Based On A Novel Cost Model For A H.264/Avc Transcoder |
Abstract | ||
|---|---|---|
In transcoding, it is well known that refinement of the motion vectors is critical to enhance the quality of transcoded video while significantly reducing transcoding complexity. This paper proposes a novel cost model to estimate the rate-distortion cost of motion vector composition in order to develop a reliable motion vector re-estimation method that has reasonable computation cost. Based on a statistical analysis of motion compensated prediction errors, we design a basic form of the proposed cost model as a function of distance from the optimal motion vector. Simulations with a transcoder employing the proposed cost model demonstrate a significant quality gain over representative video transcoding schemes with no complexity increase. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1587/transinf.2015EDL8181 | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS |
Keywords | Field | DocType |
H.264/AVC, transcoding, motion vector re-estimation, motion vector cost estimator | Computer vision,Transcoding,Quarter-pixel motion,Computer science,Artificial intelligence,Motion vector | Journal |
Volume | Issue | ISSN |
E99D | 3 | 1745-1361 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Soongi Hong | 1 | 3 | 2.09 |
| Yoonsik Choe | 2 | 91 | 28.68 |
| Yong Kim | 3 | 24 | 5.72 |