May-chen Kuo (University of Southern California), “Mocap Data Compression: Algorithms and Performance Evaluation” (2010)

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May-chen Kuo (University of Southern California), “Mocap Data Compression: Algorithms and Performance Evaluation” (2010)

May-chen Kuo (University of Southern California), “Mocap Data Compression: Algorithms and Performance Evaluation” Advisor: C.-C. Jay Kuo (2010)

The richness of a motion capture (mocap) database is essential to motion synthesis applications, especially in the entertainment industry. The constraints on the size of the mocap collection to be used and the need for efficient database management drive the need for an effective compression scheme.

In this thesis, the author explores the characteristics of the mocap data and proposes two real-time compression schemes with flexible rate-distortion trade-off. These two compression schemes are designed to optimize different objective functions (precision-based and perception-based) for different application purposes. The first one aims at preserving nearly lossless content. This scheme can achieve a coding gain of about 50:1, which is 2.5 times better than the state-of-the-art techniques.  The second one aims at further compression with visually pleasant quality. This scheme can achieve a coding gain of at least 100:1, and can be used to provide a quick preview of the content of the database.

For details, click here or contact the author.

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