Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/90510
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dc.contributorDepartment of Electronic and Information Engineeringen_US
dc.creatorKuang, Wen_US
dc.creatorChan, YLen_US
dc.creatorTsang, SHen_US
dc.creatorSiu, WCen_US
dc.date.accessioned2021-07-15T02:12:02Z-
dc.date.available2021-07-15T02:12:02Z-
dc.identifier.issn1051-8215en_US
dc.identifier.urihttp://hdl.handle.net/10397/90510-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rights© 2019 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.en_US
dc.rightsThe following publication W. Kuang, Y. Chan, S. Tsang and W. Siu, "DeepSCC: Deep Learning-Based Fast Prediction Network for Screen Content Coding," in IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 7, pp. 1917-1932, July 2020 is available at https://dx.doi.org/10.1109/TCSVT.2019.2929317en_US
dc.subjectScreen content coding (SCC)en_US
dc.subjectHigh efficiency video coding (HEVC)en_US
dc.subjectfast algorithmen_US
dc.subjectConvolutional neural network (CNN)en_US
dc.subjectDeep learningen_US
dc.titleDeepSCC : deep learning-based fast prediction network for screen content codingen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1917en_US
dc.identifier.epage1932en_US
dc.identifier.volume30en_US
dc.identifier.issue7en_US
dc.identifier.doi10.1109/TCSVT.2019.2929317en_US
dcterms.abstractScreen content coding (SCC) is an extension of high efficiency video coding (HEVC), and it is developed to improve the coding efficiency of screen content videos by adopting two new coding modes: Intra Block Copy (IBC) and Palette (PLT). However, the flexible quadtree-based coding tree unit (CTU) partitioning structure and various mode candidates make the fast algorithms of the SCC extremely challenging. To efficiently reduce the computational complexity of SCC, we propose a deep learning-based fast prediction network DeepSCC that contains two parts: DeepSCC-I and DeepSCC-II. Before feeding to DeepSCC, incoming coding units (CUs) are divided into two categories: dynamic CTUs and stationary CTUs. For dynamic CTUs having different content as their collocated CTUs, DeepSCC-I takes raw sample values as the input to make fast predictions. For stationary CTUs having the same content as their collocated CTUs, DeepSCC-II additionally utilizes the optimal mode maps of the stationary CTU to further reduce the computational complexity. Compared with the HEVC-SCC reference software SCM-8.3, the proposed DeepSCC reduces the encoding time by 48.81% on average with a negligible Bjøntegaard delta bitrate increase of 1.18% under all-intra configurationen_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE transactions on circuits and systems for video technology, July 2020, v. 30, no. 7, 8764598, p. 1917-1932en_US
dcterms.isPartOfIEEE transactions on circuits and systems for video technologyen_US
dcterms.issued2020-07-
dc.identifier.scopus2-s2.0-85087892750-
dc.identifier.eissn1558-2205en_US
dc.identifier.artn8764598en_US
dc.description.validate202107 bcvcen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumbera0964-n06-
dc.identifier.SubFormID2240-
dc.description.fundingSourceRGCen_US
dc.description.fundingTextPolyU 152069/18Een_US
dc.description.pubStatusPublisheden_US
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