Skip to main content
SpringerLink
Log in

Distributed compressed video sensing based on key frame secondary reconstruction

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Distributed compressed video sensing scheme combines advantages of compressive sensing and distributed video coding to get better performance, in the meantime, adapts to the limited-resource wireless multimedia sensor network. However, in the conventional distributed compressed video sensing schemes, self-similarity and high sampling rate of the key frame have not been sufficiently utilized, and the overall computational complexity increases with the development of these schemes. To solve the aforementioned problems, we propose a novel distributed compressed video sensing scheme. A new key frame secondary reconstruction scheme is proposed, which further improves the quality of key frame and decreases computational complexity. The key frame’s initial reconstruction value is deeply exploited to assist the key frame secondary reconstruction. Then, a hypotheses set acquisition algorithm based on motion estimation is proposed to improve the quality of hypotheses set by optimizing the searching window under low complexity. Experimental results demonstrate that the overall performance of the proposed scheme outperforms that of the state-of-the-art methods.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

References

  1. Baraniuk RG (2007) A lecture on compressive sensing. IEEE Signal Process Mag 24(4):118–121

    Article  Google Scholar 

  2. Candès EJ (2006) Compressive sampling. In International Congress of Mathematicians, Madrid, Spain, vol. 3, pp 1433–1452

  3. Candès EJ, Tao T (2005) Decoding by linear programming. IEEE Trans Inf Theory 51(12):4203–4215

    Article  MathSciNet  MATH  Google Scholar 

  4. Chen C, Donoho DL, Saunders MA (2001) Atomic Decomposition by Basis Pursuit. SIAM Rev 43(1):33–61

    Article  MathSciNet  MATH  Google Scholar 

  5. Chen C, Tramel EW, Fowler JE (2011) Compressed-sensing recovery of images and video using multihypothesis predictions. Signals Systems and Computers (ASILOMAR) 2011 Conference Record of the Forty Fifth Asilomar Conference on, pp 1193–1198

  6. Chen J, Wang N, Xue F, Gao YT (2016) Distributed compressed video sensing based on the optimization of hypotheses set update technique. Multimed Tools Appl. doi:10.1007/s11042-016-3866-4

  7. Do TT, Chen Y, Nguyen DT, Nguyen N, Lu G, Tran TD (2009) Distributed compressed video sensing. Image Processing. IEEE, pp 1393–1396

  8. Donoho DL (2006) Compressed Sensing. IEEE Trans Inf Theory 52:1289–1306

    Article  MathSciNet  MATH  Google Scholar 

  9. Fowler JE, Mun S, Tramel EW (2012) Block-based compressed sensing of images and video. Found Trends in Signal Process 4(4):297–416

    Article  MATH  Google Scholar 

  10. Hosseini MS, Plataniotis KN (2014) High-accuracy total variation with application to compressed video sensing. IEEE Trans Image Process 23(9):3869–84

  11. Jiang D, Nie L, Lv Z, et al (2016) Spatio-Temporal Kronecker Compressive Sensing for Traffic Matrix Recovery. IEEE Access, 3046-3053

  12. Kang LW, Lu CS (2009) Distributed compressive video sensing. In: Proceedings of the international conference on acoustics, speech, and signal processing, pp 1169–1172

  13. Kuo Y, Wang S, Qin D, Chen J (2013) High-quality decoding method based on resampling and re-reconstruction. Electron Lett 49(16):991–992

    Article  Google Scholar 

  14. Kuo Y, Wu K, Chen J (2015) A scheme for distributed compressed video sensing based on hypotheses set optimization techniques. Multidimensional Systems & Signal Processing, pp 1–20

  15. Lu G (2007) Block compressed sensing of natural images. In: Proceedings of the International Conference on Digital Signal Processing. IEEE, pp 403–406

  16. Mallat S (2002) A wavelet tour of signal processing. Advances in Case-Based Reasoning, European Conference 2002:549–559

  17. Mun S, Fowler JE (2009) Block compressed sensing of images using directional transforms. 16th IEEE International Conference on Image Processing, Cairo, Egypt, IEEE, pp 3021–3024

  18. Mun S, Fowler JE (2011) Residual reconstruction for block-based compressed sensing of video. In: Proceedings of the IEEE Data Compression Conference, Snowbird, UT, USA, pp 183–192

  19. Nie L, Jiang D, Guo L (2015) End-to-end network traffic reconstruction via network tomography based on compressive sensing. J Netw Syst Manag 23(3):709–730

    Article  Google Scholar 

  20. Rudin LI, Osher S, Fatemi E (1992) Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenomena 60(1–4):259–268

    Article  MathSciNet  MATH  Google Scholar 

  21. Tramel EW, Fowler JE (2011) Video compressed sensing with multihypothesis. Proc Data Compress Conf, pp 193–202

  22. Wen J, Chang XW (2017) The success probability of the Babai point estimator in box-constrained integer linear models. IEEE Trans Inf Theory 63(1):631–648

    Article  MATH  Google Scholar 

  23. Wen J, Li D, Zhu F (2015) Stable recovery of sparse signals via lp-minimization. Appl Comput Harmon Anal 38(1):161–176

    Article  MathSciNet  MATH  Google Scholar 

  24. Xiao Y, Yang J (2010) A fast algorithm for total variation image reconstruction from random projections. Inverse Prob Imaging 6(3):547–563

    Google Scholar 

  25. Zhang L, Zhang L, Mou X et al (2011) FSIM: a feature similarity index for image quality assessment. IEEE Trans Image Process 20(8):2378–2386

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgments

This work was supported by National Natural Science Foundation of China (Grant No. 61540046) and the “111” project (Grant No. B08038).

Author information

Authors and Affiliations

  1. School of Telecommunications Engineering, Xidian University, Xi’an, 710071, China

    Jian Chen, Fei Xue & Yonghong Kuo

Authors
  1. Jian Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fei Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yonghong Kuo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jian Chen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, J., Xue, F. & Kuo, Y. Distributed compressed video sensing based on key frame secondary reconstruction. Multimed Tools Appl 77, 14873–14889 (2018). https://doi.org/10.1007/s11042-017-5071-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-017-5071-5

Keywords

Search

Navigation