Advertisement

Signal, Image and Video Processing

, Volume 12, Issue 7, pp 1419–1427 | Cite as

Energy-constraint rate distortion optimization for compressive sensing-based image coding

  • Wei JiangEmail author
  • Junjie Yang
Original Paper
  • 93 Downloads

Abstract

Compressive sensing (CS) is an emerging technology which samples a sparse signal at a rate corresponding to its actual information content rather than to its bandwidth. The encoding scheme based on CS features low hardware complexity and power consumption. It is widely used in resource-limited application where the energy consumption determines the lifetime of the system. In this paper, the primary concern is how to configure the encoder to achieve the target energy consumption and target bit rate as well as the optimal picture quality. Firstly, the rate–distortion (R–D) behavior is investigated under the energy constraint. Then a source rate–distortion model and energy consuming model are proposed to quantify the relationship among coding parameters, source encoding rate, distortion, and energy consumption. The accuracy is verified through the experiments. Since the proposed models provide a theoretical basis and a practical guideline for performance optimization, the optimal configuration of coding parameters can be determined. The coding system adjusts its parameter configuration to match the available energy supply of the system while maximizing the picture quality. Simulation results have demonstrated the effectiveness of the proposed approach in achieving the optimal image quality and energy efficiency.

Keywords

Compressive sensing Rate distortion Optimization Energy constrained Model 

Notes

Acknowledgements

This paper was supported by National Natural Science Foundation of China (61401269,61572311), Shanghai Technology Innovation Shanghai Technology Innovation Project (17020500900), Foundation of Shanghai Talent Development (201501), and “Shuguang Program” sponsored by Shanghai Education Development Foundation and Shanghai Municipal Education Commission (17SG51), Local colleges and universities capacity building Program (14110500900, 15110500900).

References

  1. 1.
    Donoho, D.: Compressive sensing. IEEE Trans. Inf. Theory 52, 1289–1306 (2006)CrossRefzbMATHGoogle Scholar
  2. 2.
    Candès, E., Romberg, J., Tao, T.: Exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inf. Theory 52, 489–509 (2006)CrossRefzbMATHGoogle Scholar
  3. 3.
    Wu, X.L., Zhang, X.J.: Model-guided adaptive recovery of compressive sensing. In: Proceedings on Data Compression Conference, pp. 123–132 (2009)Google Scholar
  4. 4.
    Goyal, V.K., Fletcher, A.K., Rangan, S.: Compressive sensing and lossy compression. IEEE Signal Process. Mag. 25, 48–52 (2008)CrossRefGoogle Scholar
  5. 5.
    He, Z.H., Liang, Y.F., Chen, L.L., Ahmad, I., Wu, D.P.: Power–rate–distortion analysis for wireless video communication under energy constraints. IEEE Trans. Circuits Syst. Video Technol. 15, 645–658 (2005)CrossRefGoogle Scholar
  6. 6.
    Kang, L.W., Lu, C.S., Lin, C.Y.: Low-complexity video coding via power–rate–distortion optimization. J. Vis. Commun. Image Represent. 23, 569–585 (2012)CrossRefGoogle Scholar
  7. 7.
    Schulz, A., Velho, L., Silva, E.D.: On the empirical rate–distortion performance of compressive sensing. In: IEEE International Conference on Image Processing, pp. 3049–3052 (2009)Google Scholar
  8. 8.
    Li, C.L., Wu, D.P.: Delay–power–rate–distortion model for wireless video communication under delay and energy constraints. IEEE Trans. Circuits Syst. Video Technol. 24, 1170–1183 (2014)CrossRefGoogle Scholar
  9. 9.
    He, Z., Cheng, W., Chen, X.: Energy minimization of portable video communication devices based on power–rate–distortion optimization. IEEE Trans. Circuits Syst. Video Technol. 18, 596–608 (2008)CrossRefGoogle Scholar
  10. 10.
    He, Z., Liang, Y., Chen, L., Ahmad, I., Wu, D.: Power–rate–distortion analysis for wireless video communication under energy constraints. IEEE Trans. Circuits Syst. Video Technol. 15, 645–658 (2005)CrossRefGoogle Scholar
  11. 11.
    He, Z., Wu, D.: Resource allocation and performance analysis of wireless video sensors. IEEE Trans. Circuits Syst. Video Technol. 16, 590–599 (2006)CrossRefGoogle Scholar
  12. 12.
    Lian, C.J., Chien, S.Y., Lin, C.P., Tseng, P.C., Chen, L.G.: Power-aware multimedia: concepts and design perspectives. IEEE Circuits Syst. Mag. 7, 26–34 (2007)CrossRefGoogle Scholar
  13. 13.
    Hemalatha, R., Radha, S., Sudharsan, S.: Energy-efficient image transmission in wireless multimedia sensor networks using block-based compressive sensing. Comput. Electr. Eng. 44, 67–79 (2015)CrossRefGoogle Scholar
  14. 14.
    Pudlewski, S., Melodia, T.: A rate–energy–distortion analysis for compressed-sensing-enabled wireless video streaming on multimedia sensors. In: IEEE Globecom, pp. 1–6 (2011)Google Scholar
  15. 15.
    pudlewski, S., Melodia, T.: compressive video compression: design and rate–energy–distortion analysis. IEEE Trans. Multimed. 15, 2072–2086 (2013)CrossRefGoogle Scholar
  16. 16.
    Bellasi, D.E., Benini, L.: Energy-efficiency analysis of analog and digital compressive sensing in wireless sensors. IEEE Circuits Syst. Mag. 62, 2718–2729 (2015)MathSciNetGoogle Scholar
  17. 17.
    Ramchandran, K., Vetterli, M.: Best wavelet packet bases in a rate–distortion sense. IEEE Trans. Image Process. 2, 160–175 (1993)CrossRefGoogle Scholar
  18. 18.
    Jiang, W.: A novel algorithm of solving the optimal slope on rate–distortion curve for the given rate budget. J. Donghua Univ. 26, 259–263 (2009)Google Scholar
  19. 19.
    Liu, H.X., Song, B., Tian, F., Qin, H.: Joint sampling rate and bit-depth optimization in compressive video sampling. IEEE Trans. Multimed. 16, 1549–1562 (2014)Google Scholar
  20. 20.
    Noise, E.: The restricted isometry property and its implications for compressed sensing. C. R. Math. 346, 589–592 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Candes, E., Wakin, M.: An introduction to compressive sampling. IEEE Signal Process. Mag. 25, 21–30 (2008)CrossRefGoogle Scholar
  22. 22.
    Do, T.T., Gan, L., Nguyen, N., Tran, T.D.: Sparsity adaptive matching pursuit algorithm for practical compressed sensing. In: Asilomar Conference on Signals, Systems and Computers, pp. 581–587 (2008)Google Scholar
  23. 23.
    Donoho, D.L., Tsaig, Y., Drori, I., luc Starck, J.: Sparse solution of underdetermined linear equations by stage wise orthogonal matching pursuit. IEEE Trans. Inf. Theory 58, 1094–1121 (2012)CrossRefzbMATHGoogle Scholar
  24. 24.
    Needell, D., Tropp, A.: Cosamp: iterative signal recovery from incomplete and inaccurate samples. Appl. Comput. Harmon. Anal. 26, 301–321 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Gunturk, C.S., Lammers, M., Powell, A., Saab, R., Yilmaz, O.: Sigma delta quantization for compressed sensing. In: Proceedings of the 44th Annual Conference on Information Sciences and Systems, pp. 1–6 (2010)Google Scholar
  26. 26.
    Candes, E., Romberg, J.: Encoding the lp Ball from limited measurements. In: Proceedings of the Data Compression Conference (DCC), pp. 1–10 (2006)Google Scholar
  27. 27.
    Dunkels, A., Eriksson, J., Finne, N., Tsiftes, N.: Powertrace: network-level power profiling for low-power wireless networks. Swed. Inst. Comput. Sci. 5, 1–14 (2011)Google Scholar
  28. 28.
    Lee, D.U., Kim, H., Tu, S., Rahimi, M., Estrin, D., Villasenor, J.: Energy-optimized image communication on resource constrained sensor platforms. http://escholarship.org/uc/item/5vg6h5n0#page-3. Accessed 11 Sept 2017
  29. 29.
    Wang, H., Peng, D., Wang, W., Sharif, H., Chen, H.H.: Cross-layer routing optimization in multirate wireless sensor networks for distributed source coding based applications. IEEE Trans. Wirel. Commun. 7, 3999–4009 (2008)CrossRefGoogle Scholar
  30. 30.
    Shoham, Y., Gersho, A.: Efficient bit allocation for an arbitrary set of quantizers (speech coding). IEEE Trans. Acoust. Speech Signal Process. 36, 1445–1453 (1988)CrossRefzbMATHGoogle Scholar
  31. 31.
    Wu, X., Dong, W., Zhang, X., Shi, G.: Model-assisted adaptive recovery of compressed sensing with imaging applications. IEEE Trans. Image Process. 21, 451–458 (2012)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Electronics and Information EngineeringShanghai University of Electric PowerShanghaiChina

Personalised recommendations