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dc.contributor.authorErabadda, Buddhiprabha
dc.contributor.authorMallikarachchi, Thanuja
dc.contributor.authorKulupana, Gosala
dc.contributor.authorFernando, Anil
dc.date.accessioned2020-08-18T15:14:43Z
dc.date.available2020-08-18T15:14:43Z
dc.date.issued2020-08-03
dc.identifier.citationErabadda, B., Mallikarachchi, T., Kulupana, G. and Fernando, A. (2020) 'iCUS: Intelligent CU Size Selection for HEVC Inter Prediction', IEEE Access, 8, pp.141143-141158. https://doi.org/10.1109/ACCESS.2020.3013804en_US
dc.identifier.issn2169-3536
dc.identifier.urihttp://hdl.handle.net/10369/11126
dc.descriptionArticle published in IEEE Access available open access at https://doi.org/10.1109/ACCESS.2020.3013804en_US
dc.description.abstractThe hierarchical quadtree partitioning of Coding Tree Units (CTU) is one of the striking features in HEVC that contributes towards its superior coding performance over its predecessors. However, the brute force evaluation of the quadtree hierarchy using the Rate-Distortion (RD) optimisation, to determine the best partitioning structure for a given content, makes it one of the most time-consuming operations in HEVC encoding. In this context, this paper proposes an intelligent fast Coding Unit (CU) size selection algorithm to expedite the encoding process of HEVC inter-prediction. The proposed algorithm introduces (i) two CU split likelihood modelling and classification approaches using Support Vector Machines (SVM) and Bayesian probabilistic models, and (ii) a fast CU selection algorithm that makes use of both offline trained SVMs and online trained Bayesian probabilistic models. Finally, (iii) a computational complexity to coding efficiency trade-off mechanism is introduced to flexibly control the algorithm to suit different encoding requirements. The experimental results of the proposed algorithm demonstrate an average encoding time reduction performance of 53.46%, 61.15%, and 58.15% for Low Delay B , Random Access , and Low Delay P configurations, respectively, with Bjøntegaard Delta-Bit Rate (BD-BR) losses of 2.35%, 2.9%, and 2.35%, respectively, when evaluated across a wide range of content types and quality levelsen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofseriesIEEE Access;
dc.titleiCUS: Intelligent CU Size Selection for HEVC Inter Predictionen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.1109/ACCESS.2020.3013804
dcterms.dateAccepted2020-08-03
rioxxterms.funderCardiff Metropolitan Universityen_US
rioxxterms.identifier.projectCardiff Metropolian (Internal)en_US
rioxxterms.versionVoRen_US
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/en_US
rioxxterms.licenseref.startdate2020-08-18
rioxxterms.funder.project37baf166-7129-4cd4-b6a1-507454d1372een_US


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