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...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Language: | English |
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2018
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| Online Access: | https://hdl.handle.net/10356/89162 http://hdl.handle.net/10220/44838 |
| _version_ | 1826120961939734528 |
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| author | Hou, Junhui Chau, Lap-Pui Magnenat-Thalmann, Nadia He, Ying |
| 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 |
| author_sort | Hou, Junhui |
| collection | NTU |
| 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. |
| first_indexed | 2024-10-01T05:24:59Z |
| format | Journal Article |
| id | ntu-10356/89162 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:24:59Z |
| publishDate | 2018 |
| record_format | dspace |
| spelling | ntu-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 |
| spellingShingle | Transform Coding Motion Capture Hou, Junhui Chau, Lap-Pui Magnenat-Thalmann, Nadia He, Ying Human Motion Capture Data Tailored Transform Coding |
| title | 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_short | Human Motion Capture Data Tailored Transform Coding |
| title_sort | human motion capture data tailored transform coding |
| topic | Transform Coding Motion Capture |
| url | https://hdl.handle.net/10356/89162 http://hdl.handle.net/10220/44838 |
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