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The Computer Journal Advance Access originally published online on October 22, 2008
The Computer Journal 2009 52(4):413-428; doi:10.1093/comjnl/bxn035
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© The Author 2008. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Dynamic Selection of a Video Content Adaptation Strategy from a Pareto Front

Anastasis A. Sofokleous and Marios C. Angelides*

School of Information Systems, Computing and Mathematics, Brunel University, Uxbridge, Middlesex UB8 3PH, UK

* Corresponding author: marios.angelides{at}bcs.org

Received 27 March 2007; revised 17 June 2008

Genetic Algorithms may be used together with Pareto Optimality in the process of selection of a suitable video content adaptation strategy, the former to return best or fittest solutions that have evolved over many generations and the latter to evaluate and rank each generation's solutions against a set of objectives without the need to assign weights to each one. The outcome of this is a Pareto front of optimal strategies, all of which would satisfy the objectives. The distribution of optimal strategies on a Pareto front, however, suggests that there may be a ‘best-fit’ optimal strategy. This article refines the process of selection of an optimal strategy by taking into account this distribution alongside user preferences, video content characteristics and usage history. In order to make the refined process dynamic, it pursues its implementation using Self-Organising Neural Networks.

Key Words: MPEG-21 • MPEG-7 • genetic algorithms • Pareto optimality • Euclidean distances


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