Call for Paper - October 2019 Edition
IJCA solicits original research papers for the October 2019 Edition. Last date of manuscript submission is September 20, 2019. Read More

Iteration less Wavelet-Fractal Image Compression Applicable in Cellular Mobile Communication System

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Sheeba K., Abdul Rahiman M.

Sheeba K. and Abdul Rahiman M.. Article: Iteration less Wavelet-Fractal Image Compression Applicable in Cellular Mobile Communication System. International Journal of Computer Applications 136(8):40-44, February 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Sheeba K. and Abdul Rahiman M.},
	title = {Article: Iteration less Wavelet-Fractal Image Compression Applicable in Cellular Mobile Communication System},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {136},
	number = {8},
	pages = {40-44},
	month = {February},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


Fractal image compression is a an active area of research with new promising technique that will work very effectively in areas where we have to deal with a huge size of data .In Fractal compression major challenge is the exhaustive comparison needed in the encoding stage. In this paper a method of iteration free- detail space fractal image compression is proposed, in which time of encoding is reduced without compromising much on the quality of image and this algorithm guarantees a high compression ratio. This is a hybrid algorithm of fractal mathematics and wavelet Transform. When comparing with the existing hybrid techniques the advantage of this proposed method is, only the approximation space undergoes exhaustive comparison and thereby it guarantees higher speed than the existing Hybrid techniques. IFS (Iterated function system) of detail space are calculated using the result of approximation space. Experimental results show that in the proposed method Computational over head is considerably reduced and maintains a good tradeoff between compression ratio and quality of image. Resultant image is resolution independent, which is the one of the properties of fractal image compression. Since this new technique guarantees, high compression ratio and low encoding time, it may work very well in mobile communication, especially in face book application.


  1. M.F. Barnsley, and S. Demko, “Iterated function systems and the global construction of fractals”, Proc. Roy. Soc. Lond., vol. A399, pp.243-275, 1985.
  2. F . M. Bayer and R. J. Cintra, Member, IEEE, “Image Compression Via a Fast DCT Approximation”, IEEE atin America Transactions , VOL. 8, NO. 6, DECEMBER 2010.
  3. Salam Benchikh, Michael Corinthios, Life ,” Efficiency Evaluation of Different Wavelets for Image Compression” The 11th International Conference on Information Sciences, Signal Processing and their Applications: Special Sessions .2012 IEEE.
  4. Hiroki Matsumoto, Student Member, IEEE, Kazuya Sasazaki, and Yukinori Suzuki, Member, IEEE “Color Image Compression with Vector Quantization” , IEEE Conference on Soft Computing in Industrial Applications (SMCia/08), June 25-27, 2008, JAPAN.
  5. M. Barnsley, and A.D. Sloan, “A better way to compress images,”BYTE Magazine, pp. 215-223, 1998 .
  6. A. E. Jacquin, “Image coding based on a fractal theory of iterated contractive image transformations" ,IEEE Trans. on Image Processing, Vol. 1, No. 1, pp. 18(30, Jan. 1992.).
  7. Chol-Hui Yun,Metzler W and Barski.M(16-19 June,2008) ‘Image Compression Predicated on Recurrent Iterated Function System’.International conference on Mathematics and Statistics.
  8. Fisher Y (1994) . Fractal Image Compression – Theory and Application New York : Springer – Verlag
  9. Nirbhay Kashyap,Dr.Shailendra Narayanan Singh,” Review of Image Compression and comparison of it’s Algorithms”, International Journal of Application or innovation in Engineering & Management.Vol.2,Issue 12,December 2013.
  10. Andreopoulos,Y.A Karayiannis,T.Stouraitis, “A Hybrid Image Compression Algorithm based on Fractal Image Coding and Wavelet transform”,ISCAS 2000-IEEE international symposium on circuits and systems,May 28-31,2000.
  11. Yuzo lano ,Fernando Silvestre da Silva,Ana Lucia Mendas Cruz,”A fast and Efficient Hybrid Fractal- Wavelet Image Coder”,IEEE Transactions on Image Processing,Vil.15,No.1,January 2006.
  12. Hitashi,Ganganpreet Kaur,Sugandha Sharma,”Fractal Image Compression a Review”, International Journal of Advanced Research in Computer Science and Software Engineering. Vol.2,Issue 2.February 2012.
  13. R.E Chaudhar,’Wavelet Transformed based Fractal Image Compression‘,International Conference on Circuits,Systems,Communication and Information Technology Application.2014,
  14. Tahamohammed Hassan and XingianWu, (2013).’An Adaptive Fractal Image Compression’, International Journal of Computer Science Issue,Vol.10,Issue2,
  15. Yin-Lon Lin and Wen-Lin Chen, (2012) ,‘Fast Search Strategies for Fractal Image Compression’,Journal of Information Science and Engineering,.


Affine transformation, fixed attractor, Fractal, Wavelet transform, Iterated function system