Hybrid strategies for efficient intra prediction in spatial SHVC

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dc.cclicenceCC-BY-NCen
dc.contributor.authorWang, Dayong
dc.contributor.authorSun, Yu
dc.contributor.authorLu, Xin
dc.contributor.authorLi, Weisheng
dc.contributor.authorLele, Xie
dc.contributor.authorZhu, Ce
dc.date.acceptance2022-11-28
dc.date.accessioned2023-02-22T13:06:53Z
dc.date.available2023-02-22T13:06:53Z
dc.date.issued2022-11-28
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI linken
dc.description.abstractWith multi-layer encoding and Inter-layer prediction, Spatial Scalable High Efficiency Video Coding (SSHVC) has extremely high coding complexity. It is very crucial to speed up its coding to promote widespread and cost-effective SSHVC applications. Specifically, we first reveal that the average RD cost of Inter-layer Reference (ILR) mode is different from that of Intra mode, but they both follow the Gaussian distribution. Based on this discovery, we apply the classic Gaussian Mixture Model and Expectation Maximization to determine whether ILR mode is the best mode thus skipping Intra mode. Second, when coding units (CUs) in enhancement layer use Intra mode, it indicates very simple texture is presented. We investigate their Directional Mode (DM) distribution, and divide all DMs into three classes, and then develop different methods with respect to classes to progressively predict the best DMs. Third, by jointly considering rate distortion costs, residual coefficients and neighboring CUs, we propose to employ the Conditional Random Fields model to early terminate depth selection. Experimental results demonstrate that the proposed algorithm can significantly improve coding speed with negligible coding efficiency losses.en
dc.funderOther external funder (please detail below)en
dc.funder.otherNature Science Foundation of Chinaen
dc.funder.otherNational Key Research and Development Program of Chinaen
dc.funder.otherNatural Science Foundation of Chongqing under Granten
dc.funder.otherScience and Technology Research Program of Chongqing Municipal Education Commissionen
dc.funder.otherJiangxi Provincial Natural Science Foundationen
dc.funder.otherShangrao Basic Research Programen
dc.identifier.citationWang, D., Sun, Y., Lu, X., Li, W., Li, W., Lele, X. and Zhu, C. (2022) Hybrid strategies for efficient intra prediction in spatial SHVC. IEEE Transactions on Broadcasting,en
dc.identifier.doihttps://doi.org/10.1109/TBC.2022.3222997
dc.identifier.issn1557-9611
dc.identifier.urihttps://hdl.handle.net/2086/22521
dc.language.isoenen
dc.peerreviewedYesen
dc.projectidNSFC U19A2052en
dc.projectidNSFC Grant 61972060en
dc.projectidNSFC Grant 62027827en
dc.projectidNSFC Grant 62020106011en
dc.projectid2019YFE0110800en
dc.projectidcstc2020jcyj-msxmX0766en
dc.projectidGrant cstc2020jcyj-zdxmX0025en
dc.projectidGrant cstc2019cxcyljrc-td0270en
dc.projectidKJZD-K202100604en
dc.projectid20202BABL202006en
dc.projectid2021F003en
dc.publisherIEEEen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.subjectSHVCen
dc.subjectcoding depthen
dc.subjectILR modeen
dc.subjectintra modeen
dc.subjectdirectional modeen
dc.titleHybrid strategies for efficient intra prediction in spatial SHVCen
dc.typeArticleen

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