MIJ2K Optimization using evolutionary multiobjective optimization algorithms

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dc.contributor.author Luis Bustamante, Álvaro
dc.contributor.author Molina, José M.
dc.contributor.author Patricio Guisado, Miguel Ángel
dc.date.accessioned 2014-05-30T10:03:22Z
dc.date.available 2014-05-30T10:03:22Z
dc.date.issued 2011-09
dc.identifier.bibliographicCitation Expert Systems with Applications, (2011), 38 (9), 10999–11010.
dc.identifier.issn 0957-4174
dc.identifier.uri http://hdl.handle.net/10016/18922
dc.description.abstract This paper deals with the multiobjective definition of video compression and its optimization. The optimization will be done using NSGA-II, a well-tested and highly accurate algorithm with a high convergence speed developed for solving multiobjective problems. Video compression is defined as a problem including two competing objectives. We try to find a set of optimal, so-called Pareto-optimal solutions, instead of a single optimal solution. The two competing objectives are quality and compression ratio maximization. The optimization will be achieved using a new patent pending codec, called MIJ2K, also outlined in this paper. Video will be compressed with the MIJ2K codec applied to some classical videos used for performance measurement, selected from the Xiph.org Foundation repository. The result of the optimization will be a set of near-optimal encoder parameters. We also present the convergence of NSGA-II with different encoder parameters and discuss the suitability of MOEAs as opposed to classical search-based techniques in this field.
dc.description.sponsorship This work was supported in part by Projects CICYT TIN2008- 06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, SINPROB, CAM MADRINET S-0505/TIC/0255 and DPS2008-07029-C02-02.
dc.format.extent 12
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Elsevier
dc.rights © 2011 Elsevier Ltd.
dc.subject.other Multi-objective
dc.subject.other Optimization
dc.subject.other Video
dc.subject.other Encoder
dc.title MIJ2K Optimization using evolutionary multiobjective optimization algorithms
dc.type article
dc.description.status publicado
dc.relation.publisherversion http://dx.doi.org/10.1016/j.eswa.2011.02.143
dc.subject.eciencia Informática
dc.identifier.doi 10.1016/j.eswa.2011.02.143
dc.rights.accessRights openAccess
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 10999
dc.identifier.publicationissue 9
dc.identifier.publicationlastpage 11010
dc.identifier.publicationtitle Expert systems with applications
dc.identifier.publicationvolume 38
dc.identifier.uxxi AR/0000009188
dc.affiliation.dpto UC3M. Departamento de Informática
dc.affiliation.grupoinv UC3M. Grupo de Investigación: Inteligencia Artificial Aplicada (GIAA)
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