Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/78195
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dc.contributor.authorFu, CHen_US
dc.contributor.authorZhao, YWen_US
dc.contributor.authorZhang, HBen_US
dc.contributor.authorChan, YLen_US
dc.contributor.authorSiu, WCen_US
dc.date.accessioned2018-09-28T01:07:57Z-
dc.date.available2018-09-28T01:07:57Z-
dc.date.issued2018-
dc.identifier.citationProceedings of the International Conference on Image Processing, ICIP, v. 2017-September, p. 4018-4022en_US
dc.identifier.isbn9781509021758-
dc.identifier.urihttp://hdl.handle.net/10397/78195-
dc.description24th IEEE International Conference on Image Processing, ICIP 2017, Beijing, China, 17-20 Sep 2017en_US
dc.description.abstractThe depth modelling modes (DMM) and 35 conventional intra modes (CHIMs) introduced in 3D-HEVC results in unacceptable huge complexity of depth intra coding. However, some redundancy between DMM and CHIMs could be avoided to accelerate the process. In this paper, a good feature-corner point (CP) is proposed to evaluate the orientation of edge in a given prediction unit (PU), by which a binary classifier is created. We further investigate the probability distribution of DMM, which is selected as the optimal intra mode in each category. According to the statistical analysis, the skipping of DMM decision is proposed to eliminate the cases which have been predicted well by CHIMs. The experimental results show that, compared with the test model HTM-13.0 of 3D-HEVC, the proposed algorithm can yield about 17% time reduction for depth intra coding with almost no degradation in coding performance.en_US
dc.description.sponsorshipDepartment of Electronic and Information Engineeringen_US
dc.language.isoenen_US
dc.publisherIEEE Computer Societyen_US
dc.subject3D-HEVCen_US
dc.subjectCorner pointen_US
dc.subjectDepth mapen_US
dc.subjectIntra predictionen_US
dc.subjectMulti-view video plus depthen_US
dc.titleDepth modelling mode decision for depth intra coding via good featureen_US
dc.typeConference Paperen_US
dc.identifier.spage4018-
dc.identifier.epage4022-
dc.identifier.volume2017-September-
dc.identifier.doi10.1109/ICIP.2017.8297037-
dc.identifier.scopus2-s2.0-85045322543-
dc.relation.conferenceIEEE International Conference on Image Processing [ICIP]-
dc.identifier.rosgroupid2017006092-
dc.description.ros2017-2018 > Academic research: refereed > Refereed conference paper-
dc.description.validate201809 bcma-
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