DocumentCode :
248865
Title :
Mode-dependent distortion modeling for H.264/SVC coarse grain SNR scalability
Author :
Yin-An Jian ; Chun-Chi Chen ; Wen-Hsiao Peng
Author_Institution :
Dept. of Comput. Sci., Nat. Chiao Tung Univ., Hsinchu, Taiwan
fYear :
2014
fDate :
27-30 Oct. 2014
Firstpage :
3165
Lastpage :
3169
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.
Keywords :
Laplace transforms; image enhancement; video coding; H.264-SVC coarse grain SNR scalability; Laplacian distribution; base-layer distortion; enhancement-layer distortion; final distortion estimates; frame level; interlayer residual prediction; macroblock level; mode-dependent distortion modeling; parametric signal model; prediction modes; transform coefficients; transformed prediction residual variances; Discrete cosine transforms; Distortion measurement; Encoding; Predictive models; Quantization (signal); Vectors; Scalable video coding; coarse grain SNR scalability; distortion modeling;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing (ICIP), 2014 IEEE International Conference on
Conference_Location :
Paris
Type :
conf
DOI :
10.1109/ICIP.2014.7025640
Filename :
7025640
Link To Document :
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