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A Simple and Adaptive Data Rate Control Scheme for Distributed Compressive Video Sensing

Year 2019, Volume: 21 Issue: 62, 685 - 696, 21.05.2019
https://doi.org/10.21205/deufmd.2019216230

Abstract

Providing
fast video encoding, Compressive Sensing has been very suitable for the schemes
which require power-constraint devices such as in wireless sensor networks.
However, resulted data load on the network is extremely high and accomplishing
rate control on these compressive sensing codecs by a simple scheme is crucial.
In this paper we propose a simple scheme of data rate control mechanism for an
existing compressive sensing codec for distributed video coding which has been
reported as an efficient framework in terms of video reconstruction quality.
Our approach does not use a feedback channel from decoder which avoids encoder
to wait for high-complexity optimization problem. Embedding the proposed scheme
to the codec architecture, we have obtained ~43% gain in data rate with an
acceptable decrease (6%) in Video PSNR.

References

  • Richardson I.E. 2011. The H. 264 advanced video compression standard, John Wiley & Sons.
  • Akyildiz, I.F., Melodia, T., Chowdhury, K.R. 2007. A survey on wireless multimedia sensor networks, Computer networks Vol. 51, no. 4, 921–960.
  • Girod, B., Aaron, A.M., Rane, S. et al. 2005. Distributed video coding, Proceedings of the IEEE, vol 93, no. 1, 71–83.
  • Slepian D. and Wolf J. 1973. Noiseless coding of correlated information sources, IEEE Transactions on information Theory vol.19, no.4, 471–480.
  • Wyner, A. 1974. Recent results in the shannon theory, IEEE Transactions on information Theory, vol. 20, no. 1, 2–10.
  • Donoho D.L. 2006. Compressed sensing, IEEE Transactions on information theory, vol. 52, no. 4, 1289–1306.
  • Candes, E.J., Romberg, J. K. and Tao T. 2006. Stable signal recovery from incomplete and inaccurate measurements, Communications on pure and applied mathematics vol.59, no.8, 1207–1223.
  • Candes E.J. et al. 2006. Compressive sampling, International congress of mathematicians, vol.3, 1433–1452, Madrid, Spain.
  • Carron, I. 2016. Compressive Sensing: The Big Picture.
  • http://sites.google.com/site/igorcarron2/cs, (Accessed Date: 05.12.2016).
  • Elad M., Figueiredo M. A., and Ma Y. 2010. On the role of sparse and redundant representations in image processing, Proceedings of the IEEE, vol. 98, no.6, 972–982.
  • Liu Y., Vijayanagar R. K., and Kim J. 2014. Quad-tree partitioned compressed sensing for depth map coding, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
  • Liu Y., and Pados D.A. 2016. Compressed-Sensed-Domain L 1-PCA Video Surveillance, IEEE Transactions on Multimedia, vol.18, no.3, 351-363.
  • Pierantozzi M., Liu Y., Pados D.A., Colonnese S., 2016. Video background tracking and foreground extraction via L1-subspace updates, In SPIE Commercial Scientific Sensing and Imaging, pp. 985707-985707.
  • Potter, L. C., Ertin, E., Parker, J. T. et al. 2010. Sparsity and compressed sensing in radar imaging, Proceedings of the IEEE, vol. 98, no. 6, 1006–1020.
  • Starck, J.-L. and Bobin, J. 2010. Astronomical data analysis and sparsity: from wavelets to compressed sensing, Proceedings of the IEEE, vol. 98, no.6, 1021–1030.
  • Zhang X., Qian Z., Ren Y., et al. 2011. Watermarking with flexible self-recovery quality based on compressive sensing and compositive reconstruction, IEEE Transactions on Information Forensics and Security, vol. 6, no. 4, 1223–1232.
  • Mamaghanian, H., Khaled, N., Atienza D., et al. 2011. Compressed sensing for real-time energyefficient ecg compression on wireless body sensor nodes, IEEE Transactions on Biomedical Engineering, vol. 58, no. 9, 2456–2466.
  • Kang L.-W. and Lu C.-S. 2009. Distributed compressive video sensing, IEEE International Conference on Acoustics, Speech and Signal Processing, 1169–1172.
  • Do, T. T., Chen, Y., Nguyen, D. T. et al. 2009. Distributed compressed video sensing, 16th IEEE International Conference on Image Processing (ICIP), 1393–1396.
  • Prades-Nebot J., Ma Y., and Huang, T. 2009. Distributed video coding using compressive sampling, IEEE Picture Coding Symposium, 1–4.
  • Chen, H.W., Kang L.W., and Lu, C.S. 2010. Dynamic measurement rate allocation for distributed compressive video sensing, in Visual Communications and Image Processing, 77440I–77440I, International Society for Optics and Photonics.
  • Azghani, M., Aghagolzadeh, A., and Aghagolzadeh, M. 2010. Compressed video sensing using adaptive sampling rate, 5th IEEE International Symposium on Telecommunications (IST), 710–714.
  • Wang, Z. and Lee, I., 2010. A study of video coding by reusing compressive sensing measurements. In Ubiquitous Intelligence & Computing and 7th International Conference on Autonomic & Trusted Computing (UIC/ATC), 7th International Conference on (pp. 64-69).
  • Liu, Z., Zhao, H.V. and Elezzabi, A. Y. 2010. Block-based adaptive compressed sensing for video, IEEE International Conference on Image Processing, 1649–1652.
  • Unser, M. 2000. Sampling-50 years after shannon, Proceedings of the IEEE, vol. 88, no. 4, 569–587.
  • Le Gall, D. 1991. Mpeg: A video compression standard for multimedia applications, Communications of the ACM, vol. 34, no. 4, 46–58.
  • Do, T. T., Tran, T. D. and Gan L. 2008. Fast compressive sampling with structurally random matrices, IEEE International Conference on Acoustics, Speech and Signal Processing, 3369–3372. Do, T. T., Gan, L., Nguyen, N. et al. 2008. Sparsity adaptive matching pursuit algorithm for practical compressed sensing, tech. rep., DTIC Document.
  • Nowak, R. D., Wright, S. J. et al. 2007. Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems, IEEE Journal of selected topics in signal processing, vol. 1, no. 4, 586–597.
  • Kotsiantis S. and Kanellopoulos D. 2006. Discretization techniques: A recent survey, GESTS International Transactions on Computer Science and Engineering, vol. 32, no. 1, 47–58.
  • Dougherty J., Kohavi R., Sahami M., et al. 1995. Supervised and unsupervised discretization of continuous features, 12th International Conference on Machine learning, 12, 194–202.
  • Holte, R. C. 1993. Very simple classification rules perform well on most commonly used datasets, Machine learning, vol. 11, no. 1, 63–90.
  • Kerber, R. 1992. Chimerge: Discretization of numeric attributes, 10th International conference on Artificial intelligence, 123–128, Aaai Press.

Dağıtık Sıkıştırmalı Video Algılama için Basit ve Uyarlanabilir bir Veri Hızı Kontrolü Tasarımı

Year 2019, Volume: 21 Issue: 62, 685 - 696, 21.05.2019
https://doi.org/10.21205/deufmd.2019216230

Abstract

Sıkıştırmalı Algılama, düşük
karmaşıklıklı video kodlama imkanı sunduğu için, kablosuz duyarga ağları gibi
kaynak-kısıtlı cihazlar gerektiren ortamlar için oldukça uygundur. Ancak, ağ
üzerinde oluşan veri yükü geleneksel video kodlama yöntemleri ile
kıyaslandığında oldukça fazladır. Dolayısıyla, sıkıştırmalı algılamalı
kodlayıcı-kodçözücüler için düşük hesaplama karmaşıklıklı basit bir tasarım ile
veri hızı kontrolünün gerçekleştirilmesi önemlidir. Bu makalede, literatürde
dağıtık video kodlama uygulamaları için önerilmiş olan video geriçatım kalitesi
anlamında etkin bir sıkıştırmalı algılamalı kodlayıcı-kodçözücü kullanılarak
düşük karmaşıklı bir veri hızı kontrol mekanizması önerilmiştir. Önerilen
tasarım geri bildirim kanalı kullanmamakta ve dolayısıyla kodlayıcının yüksek
karmaşıklıklı eniyileme problemi çözümünü beklemesi gerekmemektedir. Önerilen
tasarım ile kabul edilebilir ölçüde (6%) Video PSNR kalite kaybına karşılık
dikkate çeker ölçüde (~43%) veri hızı kazanımı sağlandığı gösterilmiştir. 

References

  • Richardson I.E. 2011. The H. 264 advanced video compression standard, John Wiley & Sons.
  • Akyildiz, I.F., Melodia, T., Chowdhury, K.R. 2007. A survey on wireless multimedia sensor networks, Computer networks Vol. 51, no. 4, 921–960.
  • Girod, B., Aaron, A.M., Rane, S. et al. 2005. Distributed video coding, Proceedings of the IEEE, vol 93, no. 1, 71–83.
  • Slepian D. and Wolf J. 1973. Noiseless coding of correlated information sources, IEEE Transactions on information Theory vol.19, no.4, 471–480.
  • Wyner, A. 1974. Recent results in the shannon theory, IEEE Transactions on information Theory, vol. 20, no. 1, 2–10.
  • Donoho D.L. 2006. Compressed sensing, IEEE Transactions on information theory, vol. 52, no. 4, 1289–1306.
  • Candes, E.J., Romberg, J. K. and Tao T. 2006. Stable signal recovery from incomplete and inaccurate measurements, Communications on pure and applied mathematics vol.59, no.8, 1207–1223.
  • Candes E.J. et al. 2006. Compressive sampling, International congress of mathematicians, vol.3, 1433–1452, Madrid, Spain.
  • Carron, I. 2016. Compressive Sensing: The Big Picture.
  • http://sites.google.com/site/igorcarron2/cs, (Accessed Date: 05.12.2016).
  • Elad M., Figueiredo M. A., and Ma Y. 2010. On the role of sparse and redundant representations in image processing, Proceedings of the IEEE, vol. 98, no.6, 972–982.
  • Liu Y., Vijayanagar R. K., and Kim J. 2014. Quad-tree partitioned compressed sensing for depth map coding, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
  • Liu Y., and Pados D.A. 2016. Compressed-Sensed-Domain L 1-PCA Video Surveillance, IEEE Transactions on Multimedia, vol.18, no.3, 351-363.
  • Pierantozzi M., Liu Y., Pados D.A., Colonnese S., 2016. Video background tracking and foreground extraction via L1-subspace updates, In SPIE Commercial Scientific Sensing and Imaging, pp. 985707-985707.
  • Potter, L. C., Ertin, E., Parker, J. T. et al. 2010. Sparsity and compressed sensing in radar imaging, Proceedings of the IEEE, vol. 98, no. 6, 1006–1020.
  • Starck, J.-L. and Bobin, J. 2010. Astronomical data analysis and sparsity: from wavelets to compressed sensing, Proceedings of the IEEE, vol. 98, no.6, 1021–1030.
  • Zhang X., Qian Z., Ren Y., et al. 2011. Watermarking with flexible self-recovery quality based on compressive sensing and compositive reconstruction, IEEE Transactions on Information Forensics and Security, vol. 6, no. 4, 1223–1232.
  • Mamaghanian, H., Khaled, N., Atienza D., et al. 2011. Compressed sensing for real-time energyefficient ecg compression on wireless body sensor nodes, IEEE Transactions on Biomedical Engineering, vol. 58, no. 9, 2456–2466.
  • Kang L.-W. and Lu C.-S. 2009. Distributed compressive video sensing, IEEE International Conference on Acoustics, Speech and Signal Processing, 1169–1172.
  • Do, T. T., Chen, Y., Nguyen, D. T. et al. 2009. Distributed compressed video sensing, 16th IEEE International Conference on Image Processing (ICIP), 1393–1396.
  • Prades-Nebot J., Ma Y., and Huang, T. 2009. Distributed video coding using compressive sampling, IEEE Picture Coding Symposium, 1–4.
  • Chen, H.W., Kang L.W., and Lu, C.S. 2010. Dynamic measurement rate allocation for distributed compressive video sensing, in Visual Communications and Image Processing, 77440I–77440I, International Society for Optics and Photonics.
  • Azghani, M., Aghagolzadeh, A., and Aghagolzadeh, M. 2010. Compressed video sensing using adaptive sampling rate, 5th IEEE International Symposium on Telecommunications (IST), 710–714.
  • Wang, Z. and Lee, I., 2010. A study of video coding by reusing compressive sensing measurements. In Ubiquitous Intelligence & Computing and 7th International Conference on Autonomic & Trusted Computing (UIC/ATC), 7th International Conference on (pp. 64-69).
  • Liu, Z., Zhao, H.V. and Elezzabi, A. Y. 2010. Block-based adaptive compressed sensing for video, IEEE International Conference on Image Processing, 1649–1652.
  • Unser, M. 2000. Sampling-50 years after shannon, Proceedings of the IEEE, vol. 88, no. 4, 569–587.
  • Le Gall, D. 1991. Mpeg: A video compression standard for multimedia applications, Communications of the ACM, vol. 34, no. 4, 46–58.
  • Do, T. T., Tran, T. D. and Gan L. 2008. Fast compressive sampling with structurally random matrices, IEEE International Conference on Acoustics, Speech and Signal Processing, 3369–3372. Do, T. T., Gan, L., Nguyen, N. et al. 2008. Sparsity adaptive matching pursuit algorithm for practical compressed sensing, tech. rep., DTIC Document.
  • Nowak, R. D., Wright, S. J. et al. 2007. Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems, IEEE Journal of selected topics in signal processing, vol. 1, no. 4, 586–597.
  • Kotsiantis S. and Kanellopoulos D. 2006. Discretization techniques: A recent survey, GESTS International Transactions on Computer Science and Engineering, vol. 32, no. 1, 47–58.
  • Dougherty J., Kohavi R., Sahami M., et al. 1995. Supervised and unsupervised discretization of continuous features, 12th International Conference on Machine learning, 12, 194–202.
  • Holte, R. C. 1993. Very simple classification rules perform well on most commonly used datasets, Machine learning, vol. 11, no. 1, 63–90.
  • Kerber, R. 1992. Chimerge: Discretization of numeric attributes, 10th International conference on Artificial intelligence, 123–128, Aaai Press.
There are 33 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Sinem Aslan 0000-0003-0068-6551

E. Turhan Tunalı This is me

Publication Date May 21, 2019
Published in Issue Year 2019 Volume: 21 Issue: 62

Cite

APA Aslan, S., & Tunalı, E. T. (2019). A Simple and Adaptive Data Rate Control Scheme for Distributed Compressive Video Sensing. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen Ve Mühendislik Dergisi, 21(62), 685-696. https://doi.org/10.21205/deufmd.2019216230
AMA Aslan S, Tunalı ET. A Simple and Adaptive Data Rate Control Scheme for Distributed Compressive Video Sensing. DEUFMD. May 2019;21(62):685-696. doi:10.21205/deufmd.2019216230
Chicago Aslan, Sinem, and E. Turhan Tunalı. “A Simple and Adaptive Data Rate Control Scheme for Distributed Compressive Video Sensing”. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen Ve Mühendislik Dergisi 21, no. 62 (May 2019): 685-96. https://doi.org/10.21205/deufmd.2019216230.
EndNote Aslan S, Tunalı ET (May 1, 2019) A Simple and Adaptive Data Rate Control Scheme for Distributed Compressive Video Sensing. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi 21 62 685–696.
IEEE S. Aslan and E. T. Tunalı, “A Simple and Adaptive Data Rate Control Scheme for Distributed Compressive Video Sensing”, DEUFMD, vol. 21, no. 62, pp. 685–696, 2019, doi: 10.21205/deufmd.2019216230.
ISNAD Aslan, Sinem - Tunalı, E. Turhan. “A Simple and Adaptive Data Rate Control Scheme for Distributed Compressive Video Sensing”. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi 21/62 (May 2019), 685-696. https://doi.org/10.21205/deufmd.2019216230.
JAMA Aslan S, Tunalı ET. A Simple and Adaptive Data Rate Control Scheme for Distributed Compressive Video Sensing. DEUFMD. 2019;21:685–696.
MLA Aslan, Sinem and E. Turhan Tunalı. “A Simple and Adaptive Data Rate Control Scheme for Distributed Compressive Video Sensing”. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen Ve Mühendislik Dergisi, vol. 21, no. 62, 2019, pp. 685-96, doi:10.21205/deufmd.2019216230.
Vancouver Aslan S, Tunalı ET. A Simple and Adaptive Data Rate Control Scheme for Distributed Compressive Video Sensing. DEUFMD. 2019;21(62):685-96.

Dokuz Eylül Üniversitesi, Mühendislik Fakültesi Dekanlığı Tınaztepe Yerleşkesi, Adatepe Mah. Doğuş Cad. No: 207-I / 35390 Buca-İZMİR.