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Proceedings Paper

Fast multilayered prediction algorithm for group of pictures in H.264/SVC
Author(s): Abdelrahman Abdelazim; Stephen James Mein; Martin Roy Varley; Christos Grecos; Djamel Ait-Boudaoud
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Paper Abstract

The objective of scalable video coding is to enable the generation of a unique bitstream that can adapt to various bitrates, transmission channels and display capabilities. The scalability is categorised in terms of temporal, spatial, and quality. To improve encoding efficiency, the SVC scheme incorporates inter-layer prediction mechanisms which increases complexity of overall encoding. In this paper several conditional probabilities are established relating motion estimation characteristics and the mode distribution at different layers of the H.264/SVC. An evaluation of these probabilities is used to structure a low-complexity prediction algorithm for Group of Pictures (GOP) in H.264/SVC, reducing computational complexity whilst maintaining similar performance. When compared to the JSVM software, this algorithm achieves a significant reduction of encoding time, with a negligible average PSNR loss and bit-rate increase in temporal, spatial and SNR scalability. Experiments are conducted to provide a comparison between our method and a recently developed fast mode selection algorithm. These demonstrate our method achieves appreciable time savings for scalable spatial and scalable quality video coding, while maintaining similar PSNR and bit rate.

Paper Details

Date Published: 2 February 2011
PDF: 6 pages
Proc. SPIE 7871, Real-Time Image and Video Processing 2011, 78710P (2 February 2011); doi: 10.1117/12.873079
Show Author Affiliations
Abdelrahman Abdelazim, Univ. of Central Lancashire (United Kingdom)
Stephen James Mein, Univ. of Central Lancashire (United Kingdom)
Martin Roy Varley, Univ. of Central Lancashire (United Kingdom)
Christos Grecos, Univ. of the West of Scotland (United Kingdom)
Djamel Ait-Boudaoud, Univ. of Portsmouth (United Kingdom)

Published in SPIE Proceedings Vol. 7871:
Real-Time Image and Video Processing 2011
Nasser Kehtarnavaz; Matthias F. Carlsohn, Editor(s)

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