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dc.contributor.authorErabadda, Buddhiprabha
dc.contributor.authorMallikarachchi, Thanuja
dc.contributor.authorKulupana, Gosala
dc.contributor.authorFernando, Anil
dc.date.accessioned2020-11-17T09:58:51Z
dc.date.available2020-11-17T09:58:51Z
dc.date.issued2020-10-13
dc.identifier.citationErabadda, B., Mallikarachchi, T., Kulupana, G., Fernando, A. (2020) 'Improving HEVC Coding Efficiency Using Virtual Long-Term Reference Pictures', 2020 IEEE 9th Global Conference on Consumer Electronics (GCCE 2020) Kobe: 13- 16th October 2020en_US
dc.identifier.urihttp://hdl.handle.net/10369/11220
dc.descriptionConference paper presented at 2020 IEEE 9th Global Conference on Consumer Electronics (GCCE 2020)en_US
dc.description.abstractInter-frame prediction in HEVC uses two types of reference pictures: short-term and long-term. Out of these long-term reference (LTR) pictures enable exploiting correlation among frames with extended temporal distances. In addition, LTR pictures improve the inter-frame prediction where video scenes are repeated such as in TV-series episodes, news broad-casts and movies. In this context, this paper proposes an algorithm to calculate LTR pictures using artificially generated virtual reference frames for static-camera scenes. The experimental results demonstrate an average coding improvement of2.34%in terms of Bjøntegaard Delta Bit Rate(BDBR), when compared with the HEVC reference encoder HM16.8.en_US
dc.description.sponsorshipThis work was supported by the CONTENT4ALL project, which is funded under European Commission’s H2020 Framework Program (Grant number:762021).
dc.language.isoenen_US
dc.relation.ispartofseries2020 IEEE 9th Global Conference on Consumer Electronics (GCCE 2020);
dc.subjectinter-prediction,en_US
dc.subjectlong-term referenceen_US
dc.subjectvirtual reference framesen_US
dc.titleImproving HEVC Coding Efficiency Using Virtual Long-Term Reference Picturesen_US
dc.typeConference paperen_US
dcterms.dateAccepted2020-10-13
rioxxterms.funderCardiff Metropolitan Universityen_US
rioxxterms.identifier.projectCardiff Metropolian (Internal)en_US
rioxxterms.versionAMen_US
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden_US
rioxxterms.funder.project37baf166-7129-4cd4-b6a1-507454d1372een_US


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