Toward Real-Time and Efficient Compression of Human Time-Varying Meshes.

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Title: Toward Real-Time and Efficient Compression of Human Time-Varying Meshes.
Authors: Doumanoglou, Alexandros1, Alexiadis, Dimitrios S.1, Zarpalas, Dimitrios1, Daras, Petros1
Source: IEEE Transactions on Circuits & Systems for Video Technology. Dec2014, Vol. 24 Issue 12, p2099-2116. 18p.
Subjects: Time perception, Time management, Time series analysis, Time study, Skeleton
Abstract: In this paper, a novel skeleton-based approach to human time-varying mesh (H-TVM) compression is presented. The topic of TVM compression is new and has many challenges, such as handling the lack of obvious mapping of vertices across frames and handling the variable connectivity across frames, while maintaining efficiency, which are the most important ones. Very few works exist in the literature, while not all of the challenges have been addressed yet. In addition, developing an efficient and real-time solution, handling the above, obviously is a difficult task. We attempt to address the H-TVM compression problem inspired from video coding using different types of frames and trying to efficiently remove inter-frame geometric redundancy utilizing the recent advances in human skeleton tracking. The overall approach focuses on compression efficiency, low distortion, and low computation time enabling for real-time transmission of H-TVMs. It efficiently compresses geometry and vertex attributes of TVMs. In addition, this paper is the first to provide an efficient method for connectivity coding of TVMs, by introducing a modification to the state-of-the-art MPEG-4 TFAN algorithm. Experiments are conducted in the MPEG-3DGC TVM database. The method outperforms the state-of-the-art standardized static mesh coder MPEG-4 TFAN at low bit-rates, while remaining competent at high bit-rates. It gives a practical proof of concept that in the combined problem of geometry, connectivity, and vertex attribute coding of TVMs, efficient inter-frame redundancy removal is possible, establishing ground for further improvements. Finally, this paper proposes a method for motion-based coding of H-TVMs that can further enhance the overall experience when H-TVM compression is used in a tele-immersion scenario. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Circuits & Systems for Video Technology is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Toward Real-Time and Efficient Compression of Human Time-Varying Meshes.
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  Data: <searchLink fieldCode="AR" term="%22Doumanoglou%2C+Alexandros%22">Doumanoglou, Alexandros</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Alexiadis%2C+Dimitrios+S%2E%22">Alexiadis, Dimitrios S.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Zarpalas%2C+Dimitrios%22">Zarpalas, Dimitrios</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Daras%2C+Petros%22">Daras, Petros</searchLink><relatesTo>1</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Circuits+%26+Systems+for+Video+Technology%22">IEEE Transactions on Circuits & Systems for Video Technology</searchLink>. Dec2014, Vol. 24 Issue 12, p2099-2116. 18p.
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  Data: <searchLink fieldCode="DE" term="%22Time+perception%22">Time perception</searchLink><br /><searchLink fieldCode="DE" term="%22Time+management%22">Time management</searchLink><br /><searchLink fieldCode="DE" term="%22Time+series+analysis%22">Time series analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Time+study%22">Time study</searchLink><br /><searchLink fieldCode="DE" term="%22Skeleton%22">Skeleton</searchLink>
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  Data: In this paper, a novel skeleton-based approach to human time-varying mesh (H-TVM) compression is presented. The topic of TVM compression is new and has many challenges, such as handling the lack of obvious mapping of vertices across frames and handling the variable connectivity across frames, while maintaining efficiency, which are the most important ones. Very few works exist in the literature, while not all of the challenges have been addressed yet. In addition, developing an efficient and real-time solution, handling the above, obviously is a difficult task. We attempt to address the H-TVM compression problem inspired from video coding using different types of frames and trying to efficiently remove inter-frame geometric redundancy utilizing the recent advances in human skeleton tracking. The overall approach focuses on compression efficiency, low distortion, and low computation time enabling for real-time transmission of H-TVMs. It efficiently compresses geometry and vertex attributes of TVMs. In addition, this paper is the first to provide an efficient method for connectivity coding of TVMs, by introducing a modification to the state-of-the-art MPEG-4 TFAN algorithm. Experiments are conducted in the MPEG-3DGC TVM database. The method outperforms the state-of-the-art standardized static mesh coder MPEG-4 TFAN at low bit-rates, while remaining competent at high bit-rates. It gives a practical proof of concept that in the combined problem of geometry, connectivity, and vertex attribute coding of TVMs, efficient inter-frame redundancy removal is possible, establishing ground for further improvements. Finally, this paper proposes a method for motion-based coding of H-TVMs that can further enhance the overall experience when H-TVM compression is used in a tele-immersion scenario. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of IEEE Transactions on Circuits & Systems for Video Technology is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1109/TCSVT.2014.2319631
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      – SubjectFull: Time series analysis
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              Text: Dec2014
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