Call for Paper - January 2023 Edition
IJCA solicits original research papers for the January 2023 Edition. Last date of manuscript submission is December 20, 2022. Read More

Compression of Image using Enhanced EZW by Setting Detail Retaining Pass Number

International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 96 - Number 2
Year of Publication: 2014
Isha Tyagi
Ashish Nautiyal
Mukesh Pathela

Isha Tyagi, Ashish Nautiyal and Mukesh Pathela. Article: Compression of Image using Enhanced EZW by Setting Detail Retaining Pass Number. International Journal of Computer Applications 96(2):37-44, June 2014. Full text available. BibTeX

	author = {Isha Tyagi and Ashish Nautiyal and Mukesh Pathela},
	title = {Article: Compression of Image using Enhanced EZW by Setting Detail Retaining Pass Number},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {96},
	number = {2},
	pages = {37-44},
	month = {June},
	note = {Full text available}


For maintaining database system, the most important aspect is Compression. For which efficiency of compression system plays an important role. Now-a-days the optimization of bit stream is necessary to transfer the data in compressed manner. To achieve this objective the scheme used is embedded transmission. In embedded transmission the transmission of compressed low bit rate information is sent first then the higher bit rate information follows. The advantage of this scheme is if some information in the bit stream is not received, then using lower bit rate information corresponding to them can be decoded. Thus for these transmission scheme both the transmitting and encoding time are necessary. Thus for this objective we use Embedded zero tree wavelet (EZW) encoding technique for the compression & embedded transmission of images. The main objective of this paper is to implement & show how to improve EZW coding with reduced & improved execution time. Therefore by assuming most of the coefficients value present in the decomposed subband to be low and near to zero, present coding can be improved, as there is no need to check them again and again for significance.


  • K. P. Soman; K. I. Ramchandran; "Insight into Wavelets – From Theory to Practice", Prentice Hall of India, Second Edition, pp. 6-9, 2005 (references)
  • S. Mallat; "A theory for multiresolution signal decomposition: The wavelet representation," IEEE Trans. Patr. Anal. Machine Intell. , vol. 11, no. 7, pp. 674-693, July 1989.
  • Daubechies; Ten Lectures on Wavelets, SIAM, 1992
  • M. Antonini, et. al. : "Image Coding Using Wavelet Transforms" IEEE Trans. Image Processing, vol. 1, no. 2, pp 205-220, April 1992
  • J. M. Shapiro; "Embedded Image Coding Using Zerotrees of Wavelet Coefficients" IEEE Trans. on Signal Processing, vol. 41, issue 12, pp 3445-3462, 1993
  • Amir Averbuch, et. al. : "Image Compression Using Wavelet Transform and Multiresolution Decomposition", IEEE Trans. Image Processing; vol. 5, no. 1, pp 4-15, January 1996
  • L. Xuhong; et. al: "Improved Image Coding Algorithm Based on Embedded Zerotree"; Eighth ACIS Int. Conf. on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing , vol. 2, pp. 189-192, 2007
  • L. Kaur; et. al: "Compression of Medical Ultrasound images using Wavelet Transform and Vector Quantization", IEEE EMBS Asian-Pacific Conf. on Biomedical Engineering, 2003; pp. 170-171, 2003.