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dc.contributor.authorGaliano, Vicente-
dc.contributor.authorMigallon, Hector-
dc.contributor.authorMartínez-Rach, Miguel Onofre-
dc.contributor.authorLópez Granado, Otoniel Mario-
dc.contributor.authorMalumbres, Manuel P-
dc.contributor.otherDepartamentos de la UMH::Ingeniería de Computadoreses_ES
dc.date.accessioned2024-06-04T07:19:03Z-
dc.date.available2024-06-04T07:19:03Z-
dc.date.created2022-09-03-
dc.identifier.citationThe Journal of Supercomputing (2023) 79:11641–11659es_ES
dc.identifier.issn1573-0484-
dc.identifier.issn0920-8542-
dc.identifier.urihttps://hdl.handle.net/11000/32258-
dc.description.abstractIt is well-known that each new video coding standard signifcantly increases in computational complexity with respect to previous standards, and this is particularly true for the HEVC and VVC video coding standards. The development of techniques for reducing the required complexity without afecting the rate/distortion (R/D) performance is therefore always a topic of intense research interest. In this paper, we propose a combination of two powerful techniques, deep learning and parallel computing, to signifcantly reduce the complexity of the HEVC encoding engine. Our experimental results show that a combination of deep learning to reduce the CTU partitioning complexity with parallel strategies based on frame partitioning is able to achieve speedups of up to 26× when 16 threads are used. The R/D penalty in terms of the BD-BR metric depends on the video content, the compression rate and the number of OpenMP threads, and was consistently between 0.35 and 10% for the video sequence test set used in our experimentses_ES
dc.formatapplication/pdfes_ES
dc.format.extent19es_ES
dc.language.isoenges_ES
dc.publisherSpringeres_ES
dc.rightsinfo:eu-repo/semantics/closedAccesses_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectCNNes_ES
dc.subjectDeep learninges_ES
dc.subjectHEVCes_ES
dc.subjectDeep learninges_ES
dc.subjectParallel processinges_ES
dc.subject.otherCDU::6 - Ciencias aplicadas::62 - Ingeniería. Tecnologíaes_ES
dc.titleOn the use of deep learning and parallelism techniques to significantly reduce the HEVC intra-coding timees_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttps://doi.org/10.1007/s11227-022-04764-1es_ES
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Artículos Ingeniería de computadores


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