Distributed Compressed Video Sensing in Camera Sensor Networks
With the booming of video devices ranging from low-power visual sensors to mobile phones, the video sequences captured by these simple devices must be compressed easily and reconstructed by relatively more powerful servers. In such scenarios, distributed compressed video sensing (DCVS), combining di...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2012-12-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2012/352167 |
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| _version_ | 1832553128855076864 |
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| author | Yu Liu Xuqi Zhu Lin Zhang Sung Ho Cho |
| author_facet | Yu Liu Xuqi Zhu Lin Zhang Sung Ho Cho |
| author_sort | Yu Liu |
| collection | DOAJ |
| description | With the booming of video devices ranging from low-power visual sensors to mobile phones, the video sequences captured by these simple devices must be compressed easily and reconstructed by relatively more powerful servers. In such scenarios, distributed compressed video sensing (DCVS), combining distributed video coding (DVC) and compressed sensing (CS), is developed as a novel and powerful signal-sensing and compression algorithm for video signals. In DCVS, video frames can be compressed to a few measurements in a separate manner, while the interframe correlation is explored by the joint recovery algorithm. In this paper, a new DCVS joint recovery scheme using side-information-based belief propagation (SI-BP) is proposed to exploit both the intraframe and interframe correlations, which is particularly efficient over error-prone channels. The DCVS scheme using SI-BP is designed over two frame signal models, the mixture Gaussian (MG) model and the wavelet hidden Markov tree (WHMT) model. Simulation results evaluated on two video sequences illustrate that the SI-BP-based DCVS scheme is error resilient when the measurements are transmitted through the noisy wireless channels. |
| format | Article |
| id | doaj-art-6da832c2a0d8415da4b3b921101724f3 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2012-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-6da832c2a0d8415da4b3b921101724f32025-02-03T05:55:23ZengWileyInternational Journal of Distributed Sensor Networks1550-14772012-12-01810.1155/2012/352167Distributed Compressed Video Sensing in Camera Sensor NetworksYu Liu0Xuqi Zhu1Lin Zhang2Sung Ho Cho3 Key Lab of Universal Wireless Communications, Ministry of Education of PRC, Beijing University of Posts and Telecommunications, Beijing 100876, China Key Lab of Universal Wireless Communications, Ministry of Education of PRC, Beijing University of Posts and Telecommunications, Beijing 100876, China Key Lab of Universal Wireless Communications, Ministry of Education of PRC, Beijing University of Posts and Telecommunications, Beijing 100876, China Department of Electronics and Computer Engineering, Hanyang University, Seoul 133791, Republic of KoreaWith the booming of video devices ranging from low-power visual sensors to mobile phones, the video sequences captured by these simple devices must be compressed easily and reconstructed by relatively more powerful servers. In such scenarios, distributed compressed video sensing (DCVS), combining distributed video coding (DVC) and compressed sensing (CS), is developed as a novel and powerful signal-sensing and compression algorithm for video signals. In DCVS, video frames can be compressed to a few measurements in a separate manner, while the interframe correlation is explored by the joint recovery algorithm. In this paper, a new DCVS joint recovery scheme using side-information-based belief propagation (SI-BP) is proposed to exploit both the intraframe and interframe correlations, which is particularly efficient over error-prone channels. The DCVS scheme using SI-BP is designed over two frame signal models, the mixture Gaussian (MG) model and the wavelet hidden Markov tree (WHMT) model. Simulation results evaluated on two video sequences illustrate that the SI-BP-based DCVS scheme is error resilient when the measurements are transmitted through the noisy wireless channels.https://doi.org/10.1155/2012/352167 |
| spellingShingle | Yu Liu Xuqi Zhu Lin Zhang Sung Ho Cho Distributed Compressed Video Sensing in Camera Sensor Networks International Journal of Distributed Sensor Networks |
| title | Distributed Compressed Video Sensing in Camera Sensor Networks |
| title_full | Distributed Compressed Video Sensing in Camera Sensor Networks |
| title_fullStr | Distributed Compressed Video Sensing in Camera Sensor Networks |
| title_full_unstemmed | Distributed Compressed Video Sensing in Camera Sensor Networks |
| title_short | Distributed Compressed Video Sensing in Camera Sensor Networks |
| title_sort | distributed compressed video sensing in camera sensor networks |
| url | https://doi.org/10.1155/2012/352167 |
| work_keys_str_mv | AT yuliu distributedcompressedvideosensingincamerasensornetworks AT xuqizhu distributedcompressedvideosensingincamerasensornetworks AT linzhang distributedcompressedvideosensingincamerasensornetworks AT sunghocho distributedcompressedvideosensingincamerasensornetworks |