dc.contributor.authorHou, Junhui
dc.date.accessioned2016-03-21T08:13:45Z
dc.date.available2016-03-21T08:13:45Z
dc.date.issued2016
dc.identifier.urihttp://hdl.handle.net/10356/66244
dc.description.abstractThree-Dimensional (3D) motion data, encoding geometrical variation of moving objects, is widely used in video games, movie production, 3D telepresence/3DTV, and many others. Recent advances in modern 3D scanning and acquisition techniques have led to the rapid growth in terms of the number of motion data and their complexity. Therefore, it is highly desired to compress the data for efficient storage and transmission. In this thesis, we propose several matrix decomposition-based compression frameworks for three types of commonly used 3D motion data, including 3D animated dynamic meshes (ADMs), 3D time varying mesh (TVM)-based human motions and facial expressions, and human motion capture (MoCap) data. Each of the proposed frameworks takes advantage of the specific characteristics of the input data. Extensive experiment results on a wide range of real-world datasets demonstrate that the proposed schemes outperform state-of-the-art schemes to a large extend in terms of both compression performance and computational complexity.en_US
dc.format.extent137 p.en_US
dc.language.isoenen_US
dc.subjectDRNTU::Engineering::Computer science and engineering::Data::Coding and information theoryen_US
dc.titleMatrix decomposition-based methods for compressing three-dimensional motion dataen_US
dc.typeThesis
dc.contributor.supervisorChau Lap Puien_US
dc.contributor.supervisorHe Yingen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeDOCTOR OF PHILOSOPHY (EEE)en_US
dc.contributor.supervisor2Nadia Magnenat Thalmannen_US


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