Name
Title | ||
|---|---|---|
A fast feature-assisted adaptive early termination approach for multiple reference frames motion estimation in H.264 |
Abstract | ||
|---|---|---|
The multiple reference frames motion estimation approach used in H.264 is computationally intensive. This paper presents a
fast or computationally efficient feature-assisted adaptive early termination approach in order to reduce the computational
complexity while maintaining more or less the same video quality. The introduced feature-assisted approach consists of three
parts: (1) reduction of the number of available reference frames using predicted motion activity, extracted texture information,
and skip mode from neighboring macroblocks, (2) the most probable reference frame prediction based on neighboring macroblocks,
and (3) an adaptive early termination threshold derived from a theoretical analysis of all zero block detection. Extensive
experimental results are performed to demonstrate the computational gain of the introduced approach over the standard approach
for the multiple reference frames motion estimation. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/s11554-007-0067-4 | J. Real-Time Image Processing |
Keywords | Field | DocType |
multiple reference frames motion estimation � feature-assisted early termination � all-zero block detection,motion estimation,reference frame,video quality,computational complexity | Reference frame,Skip mode,Computer vision,Block-matching algorithm,Computer science,Motion compensation,Real-time computing,Inter frame,Artificial intelligence,Motion estimation,Video quality,Computational complexity theory | Journal |
Volume | Issue | ISSN |
3 | 1-2 | 1861-8219 |
Citations | PageRank | References |
5 | 0.50 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianfeng Ren | 1 | 291 | 16.97 |
| Nasser D. Kehtarnavaz | 2 | 198 | 20.74 |
| Madhukar Budagavi | 3 | 267 | 23.26 |