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Research Article | Open Access |

Wyner-Ziv Video Coding using Hadamard Transform and Deep Learning

Author 1: Jean-Paul Kouma Author 2: Ulrik Soderstrom
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 7, No. 7 · Published 2016

DOI: https://doi.org/10.14569/IJACSA.2016.070779

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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

How to Cite this Article

Kouma, J., & Soderstrom, U. (2016). Wyner-Ziv Video Coding using Hadamard Transform and Deep Learning. International Journal of Advanced Computer Science and Applications, 7(7). https://doi.org/10.14569/IJACSA.2016.070779

Kouma, Jean-Paul, and Ulrik Soderstrom. "Wyner-Ziv Video Coding using Hadamard Transform and Deep Learning." International Journal of Advanced Computer Science and Applications, vol. 7, no. 7, 2016, https://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},
  volume    = {7},
  number    = {7},
  year      = {2016},
  publisher = {The Science and Information Organization},
  author    = {Jean-Paul Kouma and Ulrik Soderstrom},
  doi       = {10.14569/IJACSA.2016.070779},
  url       = {https://doi.org/10.14569/IJACSA.2016.070779}
}

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