Human Motion Capture Data Tailored Transform Coding

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 characterist...

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Main Authors: Hou, Junhui, Chau, Lap-Pui, Magnenat-Thalmann, Nadia, He, Ying
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2018
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Online Access:https://hdl.handle.net/10356/89162
http://hdl.handle.net/10220/44838
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-891622020-03-07T14:02:36Z Human Motion Capture Data Tailored Transform Coding Hou, Junhui Chau, Lap-Pui Magnenat-Thalmann, Nadia He, Ying School of Computer Science and Engineering School of Electrical and Electronic Engineering Institute for Media Innovation Transform Coding Motion Capture 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. NRF (Natl Research Foundation, S’pore) MOE (Min. of Education, S’pore) Accepted version 2018-05-18T07:42:23Z 2019-12-06T17:19:15Z 2018-05-18T07:42:23Z 2019-12-06T17:19:15Z 2015 Journal Article 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. 1077-2626 https://hdl.handle.net/10356/89162 http://hdl.handle.net/10220/44838 10.1109/TVCG.2015.2403328 en IEEE Transactions on Visualization and Computer Graphics © 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]. 20 p. application/pdf
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Transform Coding
Motion Capture
spellingShingle Transform Coding
Motion Capture
Hou, Junhui
Chau, Lap-Pui
Magnenat-Thalmann, Nadia
He, Ying
Human Motion Capture Data Tailored Transform Coding
description 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.
author2 School of Computer Science and Engineering
author_facet School of Computer Science and Engineering
Hou, Junhui
Chau, Lap-Pui
Magnenat-Thalmann, Nadia
He, Ying
format Article
author Hou, Junhui
Chau, Lap-Pui
Magnenat-Thalmann, Nadia
He, Ying
author_sort Hou, Junhui
title Human Motion Capture Data Tailored Transform Coding
title_short Human Motion Capture Data Tailored Transform Coding
title_full Human Motion Capture Data Tailored Transform Coding
title_fullStr Human Motion Capture Data Tailored Transform Coding
title_full_unstemmed Human Motion Capture Data Tailored Transform Coding
title_sort human motion capture data tailored transform coding
publishDate 2018
url https://hdl.handle.net/10356/89162
http://hdl.handle.net/10220/44838
_version_ 1681041426535677952