[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Exploiting global redundancy in big surveillance video data for efficient coding

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

In the era of big data, the gap between the fast increasing size of the surveillance video data and the relatively stable video compression rate has become prominent. Existing data compression methods based on the known redundancies are not able to eliminate the dominant redundancy embedded in the big surveillance video data (BSVD) of multiple sources in large time span. In this study, a new approach based on global redundancy of the BSVD has been explored. We first analyze the compositions of the global redundancy based on characteristics of BSVD. A coding framework is proposed to eliminate global redundancy. Simulated experiments have been performed to examine the performance of the approach in comparison with the high efficiency video coding. The experimental result showed that the proposed approach can reach a compression rate of 1/400 for a huge dataset of surveillance videos.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. EMC. The Digital Universe in 2020: Big Data, Bigger Digital Shadows, and Biggest Growth in the Far East. 2012; Available from: http://www.emc.com/leadership/digital-universe/iview/big-data-2020.htm

  2. Sullivan, G.J., et al.: Overview of the high efficiency video coding (HEVC) standard. IEEE Trans. Circuits Syst. Video Technol. 22(12), 1649–1668 (2012)

    Article  Google Scholar 

  3. Wiegand, T.S., G.J.: Overview of the H.264/AVC video coding standard. IEEE Trans. Circuits Syst. Video Technol. 13(7):560–576 (2003)

  4. ISO/IEC., (2001 March). Overview of the MPEG-4 Standard. Consultado el

  5. Xiong, H.: Sparse spatio-temporal representation with adaptive regularized dictionary learning for low bit-rate video coding. IEEE Trans. Circuits Syst. Video Technol. 23(4), 710–728 (2013)

    Article  Google Scholar 

  6. Xu, M.: Compressibility constrained sparse representation with learnt dictionary for low bit-rate image compression. IEEE Trans. Circuits Syst. Video Technol. 24(10), 1743–1757 (2014)

    Article  Google Scholar 

  7. Liu, D.: Image compression with edge-based inpainting. IEEE Trans. Circuits Syst. Video Technol. 17(10), 1273–1287 (2007)

  8. Zhu, C.: Video coding with spatio-temporal texture synthesis and edge-based inpainting. In: 2008 IEEE International Conference on Multimedia and Expo, 2008 June. pp. 813–816

  9. Gao, W., et al.: IEEE 1857 standard empowering smart video surveillance systems. Intell. Syst. 29(5), 30–39 (2013)

    Article  Google Scholar 

  10. Differential quantization of communication signals: by Cutler. C.C, Google Patents (1952)

  11. Graphics & Media Lab Video Group, Lossless Video Codecs Comparison, 2007, Moscow State University

  12. Kang, J.-W.: Sparse/DCT (S/DCT) two-layered representation of prediction residuals for video coding. IEEE Trans. Image Process. 22(7), 2711–2722 (2013)

    Article  MathSciNet  Google Scholar 

  13. Weinzaepfel, P.: Reconstructing an image from its local descriptors. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2011 June, pp. 337–344

  14. Yue, H.: Cloud-based image coding for mobile devices-toward thousands to one compression. IEEE Trans. Multimed. 15(4), 845–857 (2013)

    Article  Google Scholar 

  15. Zhu, C.: Video coding with spatio-temporal texture synthesis. In: 2007 IEEE International Conference on Multimedia and Expo. 2007 July, pp. 112–115

  16. Zhang, X.: Background-modeling-based adaptive prediction for surveillance video coding. IEEE Trans. Image Process. 23(2), 769–784 (2014)

    Article  MathSciNet  Google Scholar 

  17. Forchheimer, R.: Low bit-rate coding through animation. In: Proceedings of the International Picture Coding Sysmposium PCS‘83: p. 113–114 (1983)

  18. Hakeem, A., Shafique, K., and Shah. M.: An object based video coding framework for video sequences obtained from static cameras. In: ACM International Conference on Multimedia. 2005, pp. 608–617

  19. ISO/IEC., Text of ISO/IEC 14496–10:200X/FDAM 1 Multi-view Video Coding, (2008)

  20. Vetro, A., Wiegand, T., Sullivan, G.J.: Overview of the stereo and multiview video coding extensions of the H.264/MPEG-4 AVC standard. Proc. IEEE. 99(4), 626–642 (2011)

    Article  Google Scholar 

  21. Barnich, O., Van Droogenbroeck, M.: ViBe: a universal background substraction algorithm for video sequences. IEEE Trans. Image Process. 20(6), 1709–1724 (2011)

    Article  MathSciNet  Google Scholar 

  22. Tan, T.N., Sullivan, G.D., Baker, K.D.: Model-based localisation and recognition of road vehicles. Int. J, Comput. Vis. 27(1), 5–25 (1998)

    Article  Google Scholar 

  23. Wang, Lizhe, Kunze, Marcel, Tao, Jie, von Laszewski, Gregor: Towards building a cloud for scientific applications. Adv. Eng. Softw. 42(9), 714–722 (2011)

    Article  Google Scholar 

  24. Wang, Lizhe, von Laszewski, Gregor, Younge, Andrew J., He, Xi, Kunze, Marcel, Tao, Jie: Cloud computing: a perspective study. New Gener. Comput. 28(2), 137–146 (2010)

    Article  MATH  Google Scholar 

  25. Ma, Yan, Wang, Lizhe, Zomaya, Albert Y., Chen, Dan, Ranjan, Rajiv: Task-tree based large-scale mosaicking for massive remote sensed imageries with dynamic DAG scheduling. IEEE Trans. Parallel Distrib. Syst. 25(8), 2126–2137 (2014)

    Article  Google Scholar 

  26. Wang, L., Ma, Y., Ranjan, R., Zomaya, A.Y., Chen, D.: A parallel file system with application aware data layout policies in digital earth. IEEE Trans. Parallel Distrib. Syst. 99(PrePrints):1 (2014). doi:10.1109/TPDS.2014.2322362

  27. Wang, L., Tao, J., Ranjan, R., Marten, H., Streit, A., Chen, J., Chen, D.: G-Hadoop: MapReduce across distributed data centers for data-intensive computing. Future Gener. Comp. Syst. 29(3), 739–750 (2013)

  28. Wang, L., Khan, S.U., Chen, D., Kolodziej, J., Ranjan, R., Xu, C., Zomaya, A.: Energy-aware parallel task scheduling in a cluster. Future Gener. Comp. Syst. 29(7),1661–1670 (2013)

  29. Chen, D., Li, X., Wang, L., Khan, S., Wang, J., Zeng, K., Cai, C..: Fast and scalable multi-way analysis of massive neural data. IEEE Trans. Comput. PP(99) (2014). doi:10.1109/TC.2013.2295806

  30. Chen, D., Wang, L., Zomaya, A., Dou, M., Chen, J., Deng, Z., Hariri, S.: Parallel simulation of complex evacuation scenarios with adaptive agent models. IEEE Trans. Parallel Distrib. Syst. PP(99) (2014). doi:10.1109/TPDS.2014.2311805

  31. Chen, D., Li, X., Cui, D., Wang, L., Lu, D.: Global synchronization measurement of multivariate neural signals with massively parallel nonlinear interdependence analysis. IEEE Trans. Neural Syst. Rehabil. Eng. 22(1), 33–43 (2014)

    Article  Google Scholar 

  32. Chen, D., Li, D., Xiong, M., Bao, H., Li, X.: GPGPU-aided ensemble empirical-mode decomposition for EEG analysis during anesthesia. IEEE Trans. Inf. Technol. Biomed. 14(6), 1417–1427 (2010)

    Article  Google Scholar 

Download references

Acknowledgments

This work was partly supported by the EU FP7 QUICK project under Grant Agreement (PIRSES-GA-2013-612652), National Nature Science Foundation of China (No. 61271256), China Postdoctoral Science Foundation (2014M562058), Fundamental Research Funds for the Central Universities (2042014kf0025, 2042014kf0286, 2042014kf0212).

Author information

Authors and Affiliations

  1. National Engineering Center for Multimedia Software, School of Computer, Wuhan University, Wuhan, 430079, Hubei, People’s Republic of China

    Jing Xiao, Liang Liao, Jinhui Hu, Yu Chen & Ruimin Hu

Authors
  1. Jing Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liang Liao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jinhui Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ruimin Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jing Xiao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xiao, J., Liao, L., Hu, J. et al. Exploiting global redundancy in big surveillance video data for efficient coding. Cluster Comput 18, 531–540 (2015). https://doi.org/10.1007/s10586-015-0434-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-015-0434-z

Keywords

Search

Navigation