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dc.contributor.authorErabadda, B.
dc.contributor.authorMallikarachchi, Thanuja
dc.contributor.authorKulupana, G.
dc.contributor.authorFernando, A.
dc.date.accessioned2019-03-08T13:06:11Z
dc.date.available2019-03-08T13:06:11Z
dc.date.issued2018-12-13
dc.identifier.citationErabadda, B., Mallikarachchi, T. , Kulupana, G. and Fernando, A. (2018) 'Machine Learning Approaches for Intra-Prediction in HEVC', 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE), Nara, 2018, pp. 206-209. doi: 10.1109/GCCE.2018.8574648en_US
dc.identifier.isbn978-1-5386-6309-7
dc.identifier.issn2378-8143
dc.identifier.urihttp://hdl.handle.net/10369/10346
dc.descriptionConference paper published in 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE), available at https://doi.org/10.1109/GCCE.2018.8574648en_US
dc.description.abstractThe use of machine learning techniques for encoding complexity reduction in recent video coding standards such as High Efficiency Video Coding (HEVC) has received prominent attention in the recent past. Yet, the dynamically changing nature of the video contents makes it evermore challenging to use rigid traditional inference models for predicting the encoding decisions for a given content. In this context, this paper investigates the resulting implications on the coding efficiency and the encoding complexity, when using offline trained and online trained machine-learning models for coding unit size selection in the HEVC intra-prediction. The experimental results demonstrate that the ground truth encoding statistics of the content being encoded, is crucial to the efficient encoding decision prediction when using machine learning based prediction models.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofseries2018 IEEE 7th Global Conference on Consumer Electronics (GCCE);
dc.rights© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
dc.subjectlearning (artificial intelligence)en_US
dc.subjectstatistical analysisen_US
dc.subjectvideo codingen_US
dc.subjectmachine learning approachesen_US
dc.subjectcomplexity reductionen_US
dc.subjectHigh Efficiency Video Codingen_US
dc.subjectvideo contentsen_US
dc.subjectcoding efficiencyen_US
dc.subjectencoding complexityen_US
dc.subjectunit size selectionen_US
dc.subjectprediction modelsen_US
dc.subjectinference modelsen_US
dc.subjectHEVC intrapredictionen_US
dc.subjectencoding decision predictionen_US
dc.subjectground truth encoding statisticsen_US
dc.subjectSupport vector machinesen_US
dc.titleMachine Learning Approaches for Intra-Prediction in HEVCen_US
dc.typeConference paperen_US
dc.typeacceptedVersion
dc.identifier.doihttps://doi.org/10.1109/GCCE.2018.8574648
dcterms.dateAccepted2018-10-09
rioxxterms.funderCardiff Metropolitan Universityen_US
rioxxterms.identifier.projectCardiff Metropolian (Internal)en_US
rioxxterms.versionAMen_US
rioxxterms.funder.project37baf166-7129-4cd4-b6a1-507454d1372een_US


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