Matrix decomposition-based methods for compressing three-dimensional motion data

• Main Author: Hou, Junhui
• Other Authors: Chau Lap Pui
• Format: Theses and Dissertations
• Language: English
• Published: 2016
• Subjects: DRNTU::Engineering::Computer science and engineering::Data::Coding and information theory
• Online Access: http://hdl.handle.net/10356/66244
• Institution: Nanyang Technological University

Three-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.