Name
Title | ||
|---|---|---|
Lagrangian Multiplier Optimization Using Markov Chain Based Rate and Piecewise Approximated Distortion Models |
Abstract | ||
|---|---|---|
The traditional Lagrange RDO algorithm assumes the transformed residues as memory less random variables, and then doesn't perform well when the prediction residues posses strong temporal correlations. We extend the RDO by modeling the residues as the first order Markov source and calibrating the distortion model with the piecewise approximation function. Comprehensive experimental results testify that our optimizations achieve up to 1.875dB coding gain as compared with the H.264/AVC reference software, and exhibit the robust performance. Moreover, the short processing latency makes our algorithm cooperate well with the rate control operation. Last but not least, the proposed approach is also useful for other emerging standards, such as HEVC. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/DCC.2012.59 | DCC |
Keywords | Field | DocType |
order markov source,comprehensive experimental result,piecewise approximated distortion models,distortion model,coding gain,traditional lagrange rdo algorithm,lagrangian multiplier optimization,avc reference software,markov chain,prediction residue,piecewise approximation function,posses strong temporal correlation,approximation theory,markov processes,encoding,random variable,optimization,first order,approximation algorithms,heuristic algorithm | Approximation algorithm,Coding gain,Mathematical optimization,Markov process,Lagrange multiplier,Computer science,Markov chain,Approximation theory,Distortion,Piecewise | Conference |
ISSN | Citations | PageRank |
1068-0314 | 0 | 0.34 |
References | Authors | |
1 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhenyu Liu | 1 | 172 | 23.22 |
| Dongsheng Wang | 2 | 373 | 64.93 |
| Junwei Zhou | 3 | 118 | 16.64 |
| Takeshi Ikenaga | 4 | 618 | 125.50 |