Please use this identifier to cite or link to this item: https://repository.cihe.edu.hk/jspui/handle/cihe/2288
DC FieldValueLanguage
dc.contributor.authorSiu, Wan Chien_US
dc.contributor.otherFu, C.-H.-
dc.contributor.otherZhao, Y.-W.-
dc.contributor.otherZhang, H.-B.-
dc.contributor.otherChan, Y.-L.-
dc.date.accessioned2022-02-16T01:44:40Z-
dc.date.available2022-02-16T01:44:40Z-
dc.date.issued2017-
dc.identifier.urihttps://repository.cihe.edu.hk/jspui/handle/cihe/2288-
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.language.isoenen_US
dc.publisherIEEEen_US
dc.titleDepth modelling mode decision for depth intra coding via good featureen_US
dc.typeconference proceedingsen_US
dc.relation.publicationProceedings of 2017 IEEE International Conference on Image Processing (ICIP)en_US
dc.identifier.doi10.1109/ICIP.2017.8297037-
dc.contributor.affiliationSchool of Computing and Information Sciencesen_US
dc.relation.isbn9781509021765en_US
dc.description.startpage4018en_US
dc.description.endpage4022en_US
dc.cihe.affiliatedNo-
item.openairetypeconference proceedings-
item.languageiso639-1en-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_5794-
item.grantfulltextnone-
item.cerifentitytypePublications-
crisitem.author.deptSchool of Computing and Information Sciences-
crisitem.author.orcid0000-0001-8280-0367-
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