Skip to main content
SpringerLink
Log in

Lossy Image Compression Using Multiwavelet Transform for Wireless Transmission

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

The performance of the wavelets within the field of image process is standard. Multiwavelets is the next step in riffle theory and it takes the performance of wavelets to the next level. In this work the performance of the Integer Multiwavelet transform (IMWT) for lossy compression has been studied. The Proposed IMWT shows sensible performance in lossy reconstruction of the images than that of Existing lossy reconstruction. This work utilizes the performance of the Proposed IMWT for lossy compression of images with encoding techniques like Magnitude set coding and Run Length Encoding. The transform coefficients are unit coded by means of Magnitude set coding and run length coding techniques which in turn results with low bits. The transform coefficient matrix is coded on not taking under consideration of the sign values using the Magnitude Set—Variable Length Integer illustration. The sign data of the coefficients is coded as bit plane with zero thresholds. This Bit plane may be used as it is or coded to scale back the bits per pixels. The Simulation was exhausted using Matlab.

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Bandyopadhyay, S. K., Paul, T. U., & Raychoudhury, A. (2011). Image compression using approximate matching and run length. International Journal of advanced Computer science and applications, 2(6), 117–121.

  2. Calderbank, R., Daubechies, I., Sweldens, W., & Yeo, B.-L. (1997). Lossless image compression using integer to integer wavelet transforms, in Froc. In IEEE conference. on image Fmc. IEEE Press.

  3. Cheung, K. -W., & Po L. -M. (2001). Integer Multiwavelet Transform for Lossless Image Coding. In Proceedings of 2001 international symposium on intelligent multimedia, video and speech processing, 2–4 May 2001 (pp. 117–120). Hongkong.

  4. Christopoulos, C., Skodras, A., & Ebrahimi, T. (2000). The JPEG2000 still image coding system: an overview. IEEE Transactions on Consumer Electronics, 46, 1103–1127.

    Article  Google Scholar 

  5. Cotronei, M., Lazzaro, D., Montefusco, L. B., & Puccio, L. (2000). Image compression through embedded multiwavelet transform coding. IEEE Transactions on Image Processing, 9(2), 184–189.

    Article  MathSciNet  MATH  Google Scholar 

  6. Dhawan, S. (2011). A review of image compression and comparison of its algorithms. International Journal of Electronics & Communication Technology, 2(1).

  7. ISO JPEG standards committee. http://www.jpeg.org

  8. Kaushik,P., & Sharma, Y. (2012). Comparison of different image enhancement techniques based upon PSNR & MSE. International Journal of Applied Engineering Research, 7(11). http://www.ripublication.com/ijaer.htm.

  9. Lin, G., & Liu, Z.-M. (2000). The application of multiwavelet transform to image coding. IEEE Transaction on Image Processing, 9(2), 270–273.

    Article  Google Scholar 

  10. Loza, A. et al. (2006). Structural similarity-based object tracking in video sequences. In Proceedings of the 9th international conferences on information fusion.

  11. Memon, N., Wu, X., & Yeo, B.-L. (1997). Entropy coding techniques for lossless image compression with reversible integer wavelet transforms. IBM Research Report, Computer Science/Mathematics, RC 21010, Oct. 22, 1997.

  12. Raghuveer M. R., & Bopardikar A. S. (1998). Wavelet transforms—Introduction to theory and applications.

  13. Rajakumar, K., & Arivoli, T. (2013). Implementation of multiwavelet transform coding for lossless image compression. IEEE International Conference on Information Communication and Embedded systems. ISBN: 978-1-4673-5786-9.

  14. Razzaque, A., & Thakur, N. V. (2012). An approach to image compression with partial encryption without sharing the secret key. International Journal of Computer Science and Network Security, 12(7), 1–6.

  15. Strela, V., Heller, P. N., Strang, G., Topiwala, P., & Heil, C. (1999). The application of multiwavelet filter banks to image processing. IEEE Transactions on Image Processing, 8, 548–563.

    Article  Google Scholar 

  16. Telagarapu, P., Naveen, V. J., Prasanthi, A. L., & Santhi G. V. (2011). Image compression using DCT and wavelet transformations. International Journal of Signal Processing Image Processing and Pattern Recognition, 4(3).

  17. Tham, J. Y., Ranganath, S., & Kassim, A. A. (1998). Highly scalable wavelet-based video compression for very low bit-rate environment. IEEE Journal on Selected Areas in Communation-Special Issue on Very Low Bit-Rate Video Coding, 16(1), 12–27.

  18. Tham, J. Y., Shen, L., Lee, S. L., & Tan, H. H. (2000). A general approach for analysis and application of discrete multiwavelet transforms. IEEE Transaction on Signal Processing, 48(2), 457–464.

    Article  MathSciNet  MATH  Google Scholar 

  19. Triantafyllidis, G. A., & Strintzis, M. G. (1999). A context based adaptive arithmetic coding technique for lossless image compression. IEEE Signal Processing Letters, 6(7), 168–170.

    Article  Google Scholar 

  20. Veerla, R., Zhang, Z., & Rao, K. R. (2012). Advanced image coding and its comparison with various still image codecs. American Journal of Signal Processing, 2(5), 113–121. doi:10.5923/j.ajsp.20120205.04.

    Article  Google Scholar 

  21. van Bilsen, E. Introduction on Advanced Image Coding. http://www.bilsen.com/aic/software.shtml

  22. Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600–612.

    Article  Google Scholar 

  23. Wu, X., Barthel, K. U., & Ruhl, G. (1998). Adaptation to nonstationarity of embedded wavelet code stream. In Proceedings of the International Conference on Image Processing, Chicago, IL.

  24. Xia, X. G. (1998). A new prefilter design for discrete multiwavelet transforms. IEEE Transactions on Signal Processing, 46, 1558–1569.

    Article  Google Scholar 

  25. Xia, X.-G., Geronimo, J. S., Hardin, D. P., & Suter, B. W. (1996). Design of prefilters for discrete multiwavelet transforms. IEEE Transactions on Signal Processing, 44, 25–35.

    Article  Google Scholar 

Download references

Acknowledgments

The author’s heart fully likes to thank the referees for providing us with valuable comments and observant suggestions that brings more enhancements to this manuscript.

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, 626 126, TamilNadu, India

    K. Rajakumar

  2. Department of Electronics and Communication Engineering, Vickram College of Engineering, Enathi, 630561, TamilNadu, India

    T. Arivoli

Authors
  1. K. Rajakumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. T. Arivoli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K. Rajakumar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rajakumar, K., Arivoli, T. Lossy Image Compression Using Multiwavelet Transform for Wireless Transmission. Wireless Pers Commun 87, 315–333 (2016). https://doi.org/10.1007/s11277-015-2637-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-015-2637-2

Keywords

Search

Navigation