Energy Efficient Image Compression Techniques in WSN

  • Nishat Bano
  • Monauwer Alam
  • Shish Ahmad
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 624)


The increasing curiosity in the wireless sensor network (WSN) research measures physical phenomena, like pressure, temperature, which are transported through low bandwidth and low complexity data streams. The introduction of inexpensive CMOS cameras and microphones has promoted wireless multimedia sensor networks (WMSNs). WSN applications such as military, environmental, multimedia surveillance, health care are tailored to provide high energy efficiency. Energy efficiency is the most important parameter in WSN due to resource constraints. The aim of image compression is to reduce redundant information present in an image, thus providing energy efficiency. In this paper, we analyze image compression techniques, namely Set Partition in Hierarchical Trees (SPIHT), Set Partitioned Embedded BloCK Coder (SPECK), and JPEG2000 for energy constrained WSNs. We also compute energy consumption for transmitting a 512 × 512 Lena image from source to destination and compressed using SPIHT algorithm.


Energy efficiency Image compression Set partitioning in hierarchical trees (SPIHT) Set partitioned embedded BloCK (SPECK) JPEG2000 


  1. 1.
    Akyildiz I.F., Su W., Sankarasubramaniam Y., Cayirci E.: Wireless sensor networks: a survey, Computer Networks (Elsevier) 38 (4) pp. 393–422 (2002)Google Scholar
  2. 2.
    Akyildiz I. F., Melodia T., and Chowdhury K. R.: A Survey on Wireless Multimedia Sensor Networks. Computer Networks (Elsevier), vol. 51, no. 4, pp. 921–960, March (2007)Google Scholar
  3. 3.
    Melodia T., Akyildiz I. F.: Research challenges for wireless multimedia sensor networks: in Distributed Video Sensor Networks, London, U.K.: Springer-Verlag, 2011. pp. 233–246 (2011)Google Scholar
  4. 4.
    Downes I., Rad L. B., and Aghajan H.,: Development of a Mote for Wireless Image Sensor Networks: in Proc. Cognitive systems with Interactive Sensors (COGIS), Paris, France, (2006)Google Scholar
  5. 5.
    Pham D. M., Aziz S.M.: An Energy Efficient Image Compression Scheme for Wireless Sensor Networks: IEEE, ISSNIP (2013)Google Scholar
  6. 6.
    Huaming Wu and Abouzeid A.A.: Energy efficient distributed JPEG2000 image compression in multihop wireless networks: 4th Workshop on Applications and Services in Wireless Networks (ASWN-2004), pages 152–160. (2004)Google Scholar
  7. 7.
    Nasri M., Helali A., Sghaier H., Maaref H.,: Energy conservation for image transmission over wireless sensor networks: IEEE (2011)Google Scholar
  8. 8.
    Pham D. M. and Aziz S. M.: Object extraction scheme and protocol for energy efficient image communication over wireless sensor networks: Computer Networks, vol. 57, pp. 2949–2960. (2013)Google Scholar
  9. 9.
    Nasri M., Helali A., Sghaier H., Maaref H.,: Priority Image Transmission in Wireless Sensor Networks, 8th International multi-conference on systems, signals & devices, (2011)Google Scholar
  10. 10.
    Chefi A. and. Sicard G: SPIHT-based image compression scheme for energy conservation over Wireless Vision Sensor Networks. IEEE conference (2014)Google Scholar
  11. 11.
    Rehman Y., Tariq M., Sato T..: A Novel Energy Efficient Object Detection and Image Transmission Approach for Wireless Multimedia Sensor Networks: IEEE sensors journal (2016)Google Scholar
  12. 12.
    Wang W., Peng D., Wang H., Sharif H.: A Novel Image Component Transmission Approach to Improve Image Quality and Energy Efficiency in Wireless Sensor Networks: Journal of Computer Science. 3 (5) pp. 353–360 (2007)Google Scholar
  13. 13.
    Sweldens W.: “The lifting scheme: A custom-design construction of biorthogonal wavelets,” Appl. Comput. Harmon. Anal, vol. 3, pp. 186–200, (1996)Google Scholar
  14. 14.
    Sweldens W.: The lifting scheme: a construction of second generation wavelets: SIAM J. Math. Anal., pp. 511–546, (1997)Google Scholar
  15. 15.
    Rein S., Lehmann S., and Gühmann C.: Fractional wavelet filter for camera sensor node with external Flash and extremely little RAM: in Proc. ACM Mobile Multimedia Commun. Conf. (MobiMedia). pp. 1–7. (2008)Google Scholar
  16. 16.
    Shapiro J. M.: Embedded image coding using zerotrees of wavelet coefficients: IEEE Trans. Signal Process.. vol. 41, pp. 3445–3462 (1993)Google Scholar
  17. 17.
    Said A. and Pearlman W. A.: A new, fast, and efficient image codec based on set partitioning in hierarchical trees: IEEE Trans. Circuits Syst. Video Technol. vol. 6, no. 3, pp. 243–250 (1996)Google Scholar
  18. 18.
    Islam A., Pearlman W. A: Embedded and efficient low-complexity hierarchical image coder. IEEE Transactions on circuits and systems for video technology, vol. 14, no. 11, (2004)Google Scholar
  19. 19.
    Taubman D.: High performance scalable image compression with EBCOT: IEEE Trans. Image Processing, vol. 9, pp. 1158–1170, (2000)Google Scholar
  20. 20.
    Heinzelman W., R., Chandrakasan, A., and Balakrishnan, H.: Energy-efficient communication protocol for wireless microsensor networks: In Hawaii International Conference on System Sciences HICSS, volume 2. (2000)Google Scholar
  21. 21.
    Crossbow Technology Inc. (2007). Crossbow. Consulted in

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Electronics EngineeringIntegral UniversityLucknowIndia
  2. 2.Department of Computer ScienceIntegral UniversityLucknowIndia

Personalised recommendations