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Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Wyner-Ziv Video Coding using Hadamard Transform and Deep Learning
Digital Object Identifier (DOI) : 10.14569/IJACSA.2016.070779
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 7 Issue 7, 2016.
Abstract: Predictive schemes are current standards of video coding. Unfortunately they do not apply well for lightweight devices such as mobile phones. The high encoding complexity is the bottleneck of the Quality of Experience (QoE) of a video conversation between mobile phones. A considerable amount of research has been conducted towards tackling that bottleneck. Most of the schemes use the so-called Wyner-Ziv Video Coding Paradigm, with results still not comparable to those of predictive coding. This paper shows a novel approach for Wyner-Ziv video compression. It is based on the Reinforcement Learning and Hadamard Transform. Our Scheme shows very promising results.
Keywords: Wyner-Ziv; video coding; rate distortion; Hadamard transform; Deep learning; Expectation Maximization
Jean-Paul Kouma and Ulrik Soderstrom, “Wyner-Ziv Video Coding using Hadamard Transform and Deep Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 7(7), 2016. http://dx.doi.org/10.14569/IJACSA.2016.070779
@article{Kouma2016,
title = {Wyner-Ziv Video Coding using Hadamard Transform and Deep Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2016.070779},
url = {http://dx.doi.org/10.14569/IJACSA.2016.070779},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {7},
author = {Jean-Paul Kouma and Ulrik Soderstrom}
}
