A Parity-based Error Control Method for Distributed Compressive Video Sensing

Shou-ning Chen, Bao-yu Zheng, Liang Zhou

2013

Abstract

A novel framework called distributed compressive video sensing (DCVS), combining distributed video coding (DVC) and compressive sensing (CS), directly capture the raw video data as measurements with low-complexity and low-cost process. It meets the requirements of distributed system very well, because of its resource consumption shifting from encoder to decoder. Nevertheless, the issue of measurements transmission in bit error channel has not been considered yet in the previous work of DCVS. This paper improved the existing DCVS codec scheme by adding the quantization and inverse quantization process, and proposed a parity-based error control (PEC) method. This method is simple enough, and has high coding efficiency. The proposed method is shown to increase video recovery quality greatly under binary symmetric channel.

References

  1. Akyildiz, I. F., Melodia, T., & Chowdhury, K. R. (2007). A survey on wireless multimedia sensor networks. Computer networks, 51(4), 921-960.
  2. Barakat, W., Saliba, R., & Evans, B. L. (2008). Compressive Sensing for Multimedia Communications in Wireless Sensor Networks.
  3. Baraniuk, R. G. (2007). Compressive sensing [lecture notes]. Signal Processing Magazine, IEEE, 24(4), 118-121.
  4. Candès, E. J., & Wakin, M. B. (2008). An introduction to compressive sampling. Signal Processing Magazine, IEEE, 25(2), 21-30.
  5. Candès, E. J. (2006). Compressive sampling. In Proceedings of the International Congress of Mathematicians: Madrid, August 22-30, 2006: invited lectures (pp. 1433-1452).
  6. Candès, E. J., & Tao, T. (2006). Near-optimal signal recovery from random projections: Universal encoding strategies?. Information Theory, IEEE Transactions on, 52(12), 5406-5425.
  7. Chen, H. W., Kang, L. W., & Lu, C. S. (2010, July). Dynamic measurement rate allocation for distributed compressive video sensing. In Visual Communications and Image Processing 2010 (pp. 77440I-77440I). International Society for Optics and Photonics.
  8. Chen, S., Zheng, B., & Li, J. (2012). A method of image quality assessment for compressive sampling video transmission. Journal of Electronics (China), 29(6), 598-603.
  9. Dai, W., Pham, H. V., & Milenkovic, O. (2009). Quantized compressive sensing. arXiv preprint arXiv:0901.0749.
  10. Do, T. T., Chen, Y., Nguyen, D. T., Nguyen, N., Gan, L., & Tran, T. D. (2009, November). Distributed compressed video sensing. In Image Processing (ICIP), 2009 16th IEEE International Conference on (pp. 1393-1396). IEEE.
  11. Do, T. T., Tran, T. D., & Gan, L. (2008, March). Fast compressive sampling with structurally random matrices. In Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on (pp. 3369-3372). IEEE.
  12. Donoho, D. L. (2006). Compressed sensing. Information Theory, IEEE Transactions on, 52(4), 1289-1306.
  13. Figueiredo, M. A., Nowak, R. D., & Wright, S. J. (2007). Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems. Selected Topics in Signal Processing, IEEE Journal of, 1(4), 586-597.
  14. Girod, B., Aaron, A. M., Rane, S., & Rebollo-Monedero, D. (2005). Distributed video coding. Proceedings of the IEEE, 93(1), 71-83.
  15. Kang, L. W., & Lu, C. S. (2009, April). Distributed compressive video sensing. In Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on (pp. 1169-1172). IEEE.
  16. Wyner, A., & Ziv, J. (1976). The rate-distortion function for source coding with side information at the decoder. Information Theory, IEEE Transactions on, 22(1), 1- 10.
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Paper Citation


in Harvard Style

Chen S., Zheng B. and Zhou L. (2013). A Parity-based Error Control Method for Distributed Compressive Video Sensing . In Proceedings of the 10th International Conference on Signal Processing and Multimedia Applications and 10th International Conference on Wireless Information Networks and Systems - Volume 1: SIGMAP, (ICETE 2013) ISBN 978-989-8565-74-7, pages 105-110. DOI: 10.5220/0004495901050110


in Bibtex Style

@conference{sigmap13,
author={Shou-ning Chen and Bao-yu Zheng and Liang Zhou},
title={A Parity-based Error Control Method for Distributed Compressive Video Sensing},
booktitle={Proceedings of the 10th International Conference on Signal Processing and Multimedia Applications and 10th International Conference on Wireless Information Networks and Systems - Volume 1: SIGMAP, (ICETE 2013)},
year={2013},
pages={105-110},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004495901050110},
isbn={978-989-8565-74-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Signal Processing and Multimedia Applications and 10th International Conference on Wireless Information Networks and Systems - Volume 1: SIGMAP, (ICETE 2013)
TI - A Parity-based Error Control Method for Distributed Compressive Video Sensing
SN - 978-989-8565-74-7
AU - Chen S.
AU - Zheng B.
AU - Zhou L.
PY - 2013
SP - 105
EP - 110
DO - 10.5220/0004495901050110