Wyner-Ziv Video Coding using Hadamard Transform and Deep Learning
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2016, Vol 7, Issue 7
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.
Authors and Affiliations
Jean-Paul Kouma, Ulrik Soderstrom
Keywords
Related Articles
- EP ID EP112543
- DOI 10.14569/IJACSA.2016.070779
- Views 135
- Downloads 0
Tagging Urdu Sentences from English POS Taggers
Being a global language, English has attracted a majority of researchers and academia to work on several Natural Language Processing (NLP) applications. The rest of the languages are not focused as much as English. Part-...
Value-Driven use Cases Triage for Embedded Systems: A Case Study of Cellular Phone
A well-defined and prioritized set of use cases enables the enhancement of an entire system by focusing on more important use cases identified in the previous iteration. These use cases are given more opportunities to be...
Data Mining and Intrusion Detection Systems
The rapid evolution of technology and the increased connectivity among its components, imposes new cyber-security challenges. To tackle this growing trend in computer attacks and respond threats, industry professionals a...
A General Model for Similarity Measurement between Objects
The problem to detect the similarity or the differ-ence between objects are faced regularly in several domains of applications such as e-commerce, social network, expert system, data mining, decision support system, etc....
Classifying Natural Language Text as Controlled and Uncontrolled for UML Diagrams
Natural language text fall within the category of Controlled and Uncontrolled Natural Language. In this paper, an algorithm is presented to show that a given language text is controlled or uncontrolled. The parameters an...