Name
Abstract | ||
|---|---|---|
This paper presents a mode-dependent distortion model for H.264/SVC coarse grain SNR scalability. It estimates the base-layer and enhancement-layer's distortions with particular consideration of their prediction modes and inter-layer residual prediction. Based on a parametric signal model, the variances of the transformed prediction residual at both layers are first formulated analytically and approximated empirically. The results are then incorporated into the assumption that the transform coefficients are distributed according to the Laplacian distribution to obtain the final distortion estimates. Experimental results confirm its fairly good ability to predict the actual distortions in both the frame and macroblock levels. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICIP.2014.7025640 | ICIP |
Keywords | Field | DocType |
parametric signal model,frame level,transform coefficients,h.264-svc coarse grain snr scalability,coarse grain snr scalability,final distortion estimates,laplace transforms,laplacian distribution,scalable video coding,macroblock level,video coding,base-layer distortion,transformed prediction residual variances,prediction modes,image enhancement,distortion modeling,enhancement-layer distortion,mode-dependent distortion modeling,interlayer residual prediction | Computer science,Artificial intelligence,Distortion,Scalable Video Coding,Macroblock,Residual,Prediction residual,Mathematical optimization,Laplace distribution,Pattern recognition,Algorithm,Parametric statistics,Scalability | Conference |
ISSN | Citations | PageRank |
1522-4880 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yin-An Jian | 1 | 0 | 0.34 |
| Chun-Chi Chen | 2 | 199 | 25.04 |
| Wen-Hsiao Peng | 3 | 209 | 33.15 |