Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/89162
Title: Human Motion Capture Data Tailored Transform Coding
Authors: Hou, Junhui
Chau, Lap-Pui
Magnenat-Thalmann, Nadia
He, Ying
Keywords: Transform Coding
Motion Capture
Issue Date: 2015
Source: Hou, J., Chau, L.-P., Magnenat-Thalmann, N., & He, Y. (2015). Human Motion Capture Data Tailored Transform Coding. IEEE Transactions on Visualization and Computer Graphics, 21(7), 848-859.
Series/Report no.: IEEE Transactions on Visualization and Computer Graphics
Abstract: Human motion capture (mocap) is a widely used technique for digitalizing human movements. With growing usage, compressing mocap data has received increasing attention, since compact data size enables efficient storage and transmission. Our analysis shows that mocap data have some unique characteristics that distinguish themselves from images and videos. Therefore, directly borrowing image or video compression techniques, such as discrete cosine transform, does not work well. In this paper, we propose a novel mocap-tailored transform coding algorithm that takes advantage of these features. Our algorithm segments the input mocap sequences into clips, which are represented in 2D matrices. Then it computes a set of data-dependent orthogonal bases to transform the matrices to frequency domain, in which the transform coefficients have significantly less dependency. Finally, the compression is obtained by entropy coding of the quantized coefficients and the bases. Our method has low computational cost and can be easily extended to compress mocap databases. It also requires neither training nor complicated parameter setting. Experimental results demonstrate that the proposed scheme significantly outperforms state-of-the-art algorithms in terms of compression performance and speed.
URI: https://hdl.handle.net/10356/89162
http://hdl.handle.net/10220/44838
ISSN: 1077-2626
DOI: 10.1109/TVCG.2015.2403328
Rights: © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/TVCG.2015.2403328].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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