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dc.contributorDepartment of Electronic and Information Engineeringen_US
dc.creatorTsang, SHen_US
dc.creatorKwong, NWen_US
dc.creatorChan, YLen_US
dc.identifier.isbn978-1-7281-8068-7 (Electronic)en_US
dc.identifier.isbn978-1-7281-8067-0 (USB)en_US
dc.identifier.isbn978-1-7281-8069-4 (Print on Demand)en_US
dc.rights© 2020 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 S. -H. Tsang, N. -W. Kwong and Y. -L. Chan, "FastSCCNet: Fast Mode Decision in VVC Screen Content Coding via Fully Convolutional Network," 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP), 2020, pp. 177-180 is available at
dc.subjectScreen content coding (SCC)en_US
dc.subjectVersatile video coding (VVC)en_US
dc.subjectConvolutional neural network (CNN)en_US
dc.subjectFully convolutional network (FCN)en_US
dc.subjectDeep learningen_US
dc.titleFastSCCNet : fast mode decision in VVC screen content coding via fully convolutional networken_US
dc.typeConference Paperen_US
dcterms.abstractScreen content coding have been supported recently in Versatile Video Coding (VVC) to improve the coding efficiency of screen content videos by adopting new coding modes which are dedicated to screen content video compression. Two new coding modes called Intra Block Copy (IBC) and Palette (PLT) are introduced. However, the flexible quad-tree plus multi-type tree (QTMT) coding structure for coding unit (CU) partitioning in VVC makes the fast algorithm of the SCC particularly challenging. To efficiently reduce the computational complexity of SCC in VVC, we propose a deep learning based fast prediction network, namely FastSCCNet, where a fully convolutional network (FCN) is designed. CUs are classified into natural content block (NCB) and screen content block (SCB). With the use of FCN, only one shot inference is needed to classify the block types of the current CU and all corresponding sub-CUs. After block classification, different subsets of coding modes are assigned according to the block type, to accelerate the encoding process. Compared with the conventional SCC in VVC, our proposed FastSCCNet reduced the encoding time by 29.88% on average, with negligible bitrate increase under all-intra configuration. To the best of our knowledge, it is the first approach to tackle the computational complexity reduction for SCC in VVC.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIn 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP) : December 1-4, 2020, virtual conference, 2020, 9301885, p. 177-180en_US
dc.relation.ispartofbook2020 IEEE International Conference on Visual Communications and Image Processing (VCIP) : December 1-4, 2020, virtual conferenceen_US
dc.relation.conferenceIEEE International Conference on Visual Communications and Image Processing [VCIP]en_US
dc.description.validate202107 bcvcen_US
dc.description.oaAccepted Manuscripten_US
dc.description.fundingTextPolyU 152069/18Een_US
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