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Wyner-Ziv Video Coding using Hadamard Transform and Deep Learning
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
2016 (English)In: International Journal of Advanced Computer Sciences and Applications, ISSN 2158-107X, E-ISSN 2156-5570, Vol. 7, no 7, 582-589 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2016. Vol. 7, no 7, 582-589 p.
Keyword [en]
Wyner-Ziv, video coding, rate distortion, Hadamard transform, Deep learning, Expectation ximization
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:umu:diva-126345DOI: 10.14569/IJACSA.2016.070779ISI: 000381940300080OAI: oai:DiVA.org:umu-126345DiVA: diva2:1039668
Available from: 2016-10-25 Created: 2016-10-03 Last updated: 2016-10-26Bibliographically approved

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Kouma, Jean-PaulSöderström, Ulrik
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