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dc.contributor.authorLiu, Qien
dc.contributor.authorYuan, Huien
dc.contributor.authorHou, Junhuien
dc.contributor.authorLiu, Haoen
dc.contributor.authorHamzaoui, Raoufen
dc.date.accessioned2018-08-30T12:46:49Z
dc.date.available2018-08-30T12:46:49Z
dc.date.issued2018-11
dc.identifier.citationLiu, Q., Yuan, H., Hou, J., Liu, H. and Hamzaoui, R. (2018) Model-based encoding parameter optimization for 3D point cloud compression. In: Proc. APSIPA ASC 2018, 10th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, Honolulu, Nov. 2018en
dc.identifier.urihttp://hdl.handle.net/2086/16518
dc.description.abstractRate-distortion optimal 3D point cloud compression is very challenging due to the irregular structure of 3D point clouds. For a popular 3D point cloud codec that uses octrees for geometry compression and JPEG for color compression, we first find analytical models that describe the relationship between the encoding parameters and the bitrate and distortion, respectively. We then use our models to formulate the rate-distortion optimization problem as a constrained convex optimization problem and apply an interior point method to solve it. Experimental results for six 3D point clouds show that our technique gives similar results to exhaustive search at only about 1.57% of its computational cost.en
dc.language.isoenen
dc.publisherIEEEen
dc.subjectPoint cloud compressionen
dc.subjectrate-distortion optimizationen
dc.subjectrate and distortion modelsen
dc.titleModel-based encoding parameter optimization for 3D point cloud compressionen
dc.typeConferenceen
dc.researchgroupInstitute of Engineering Sciences (IES)en
dc.peerreviewedYesen
dc.funderNational Natural Science Foundation of Chinaen
dc.funderShandong Natural Science Funds for Distinguished Young Scholaren
dc.funderShandong Provincial Key Research and Development Planen
dc.funderShenzhen Science and Technology Research and Development Fundsen
dc.funderYoung Scholars Program of Shandong Universityen
dc.projectid61571274, 61871342en
dc.projectidJQ201614en
dc.projectid2017CXGC1504en
dc.projectidJCYJ20170818103244664en
dc.projectid2015WLJH39en
dc.cclicenceCC-BY-NCen
dc.date.acceptance2018-08-07en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.researchinstituteInstitute of Engineering Sciences (IES)en


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