A Fractal Image Compression Techniques

  IJCOT-book-cover
 
International Journal of Computer & Organization Trends (IJCOT)          
 
© 2014 by IJCOT Journal
Volume - 4 Issue - 1
Year of Publication : 2014
Authors : P Subramanian, R Indumathi
DOI :  10.14445/22492593/IJCOT-V4P305

MLA

P Subramanian, R Indumathi. "Fractal Image Compression Techniques", International Journal of Computer & organization Trends  (IJCOT), V4(1):6-9 Jan - Feb 2014, ISSN:2249-2593, www.ijcotjournal.org. Published by Seventh Sense Research Group.

Abstract

Image compression is an essential technology in multimedia and digital communication fields. Fractal image compression is a potential image compression scheme due to its potential high compression ratio, fast decompression and multi resolution properties. Fractal image compression utilizes the existence of self symmetry of images. Since Bransley gave the concept of fractal image compression in 1988, fractal image compression has obtained recognition and has become one of the most popular coding methods in the recent years. However the high computational complexity of fractal image encoding greatly restricts its applications. Several techniques and improvements have been suggested to speed up the fractal image compression. This paper presents a review of the techniques published for faster fractal image compression.

References

[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] M.F. Barnsley, and L.P. Hurd, “Fractal Image Compression,” Massachusetts: A.K. Peters, Wellesley, 1993.
[3] A. Jacquin, “Image coding based on a fractal theory of iterated contractive image transformations,” IEEE Transaction on Image Processing, vol. 1, pp. 18-30, 1992.
[4] M. Barnsley, and A.D. Sloan, “A better way to compress images,” BYTE Magazine, pp. 215-223, 1998.
[5] H. Jin-shu, “Fast Fractal Image Compression Using Fuzzy Classification, ” Fifth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD `08) , vol. 3, pp. 272-276 , 2008.
[6] Y. Chakrapani, K. Soundera Rajan, “Implementation of fractal image compression employing artificial neural networks, ” World Journal of Modelling and Simulation ISSN 1 746-7233, vol. 4, no. 4, pp. 287-295, 2008.
[7] A. Gafour, M.K. Feraoun, A. Rahmoun, “On Improving Processing Speed of Image Fractal Compression Using Artificial Genetics Techniques, ” Scientific Journal of King Faisal University (Basic and Applied Sciences), vol. 7, no. 1, pp. 35-47, 2006.
[8] Y. Chakrapani, K. Soundera Rajan, “A Comparative Approach To Fractal Image Compression Using Genetic Algorithm And Simulated Annealing Technique, ” Asian Journal of Information Technology , pp. 285-289, 2008.
[9] G.M. Davis , “A wavelet-based analysis of fractal image compression, ” IEEE TRANSACTIONS ON IMAGE PROCESSING, vol. 7 , pp. 141-154, 1998.
[10] Y. Fisher, Fractal Image Compression: Theory and Application. New York: Springer-Verlag, 1994.
[11] Ruhl, H. Hartenstein, and D. Saupe, “Adaptive partitionings for fractal image compression”, IEEE Int. Conf. Image Processing, Santa Barbara, CA, vol. 2, pp. 310–313, Oct. 1997.
[12] G. Farhadi, “An enhanced fractal image compression based on Quadtree partition”, ISPA03 Proc. on Image and Signal Processing and Analysis, 2003.
[13] Y. Fisher, E. W. Jacobs, and R. D. Boss, “Fractal image compression using iterated transforms,” in Image and Text Compression, J. A. Storer, Ed. Boston, MA: Kluwer, pp. 35–61, 1992.
[14] Tanimoto, H. Ohyama and T. Kimoto, “A new fractal image coding scheme employing blocks of variable shapes”, IEEE Int. Conf. Image Processing, Lausanne Switzerland, Sept. 1996, vol. 1, pp. 137–140.
[15] F. Davoine, M. Antonini, J.M. Chassery and M. Barlaud, “Fractal image compression based on Delaunay triangulation and vector quantization”, IEEE Trans. Image Processing, vol. 5, no. 2, pp. 338– 346, Feb. 1996.
[16] E.W Jacobs, Y Fisher, and R. D. Boss, “Image compression: A study of iterated transform method”, Signal Processing, Vol. 29, No 3, pp. 251-263, December 1992.
[17] J. M. Beaumont, “Advances in block based fractal coding of still pictures”, in Proc. IEEE Colloq.: The Application of Fractal Techniques in Image Processing, pp. 3.1–3.6, Dec. 1990.
[18]B. Wohlberg and G.D. Jager, “A review of fractal image coding literature”, ”, IEEE Trans. on Image Processing, vol. 8, no. 12, pp. 1716-1729, Dec. 1999.
[19] B. Hurtgen and C. Stiller, “Fast hierarchical codebook search for fractal coding of still images”, in Proc. EOS/SPIE Visual Communications PACS Medical Applications ’93, Berlin, Germany, 1993
[20] R. Hamzaoui, M. Muller and D. Saupe, “VQ-enhanced fractal image compression”, IEEE Int. Conf. Image Processing, Lausanne, Switzerland, vol. 1, pp. 153–156, Sept. 1996.
[21] T.K. Truong, J.H. Jeng, I.S. Reed, P.C. Lee and A.Q. Li, “A fast encodind algorithm for fractal image compression using the DCT inner product”, IEEE Trans. Image processing, vol. 9, no. 4, pp. 529- 535, April 2000.
[22] A.E. Jacquin, “ A novel fractal block-coding technique for digital Images”, ICASSP International Conference on Acoustics, Speech, and Signal Processing, 1990.
[23] Y. Fisher, E. W. Jacobs, and R. D. Boss, “Fractal image compression using iterated transforms,” in Image and Text Compression, J. A. Storer, Ed. Boston, MA: Kluwer, pp. 35–61, 1992.
[24] B. Hurtgen and C. Stiller, “Fast hierarchical codebook search for fractal coding of still images”, in Proc. EOS/SPIE Visual Communications PACS Medical Applications ’93, Berlin, Germany, 1993.
[25] B. Rejeb and W. Anheier, “A new approach for speed-up of fractal image coding”, IEEE 13th Int. National Conf. DSP proceedings, vol. 2, pp. 853-856, July 1997.
[26] C.J. Wein and I.F. Blake, “On the performance of fractal compression with clustering”, IEEE Trans. Image Processing, vol. 5, no. 3, pp. 522-526, March 1996.
[27] D. Saupe and U. Freiburg, “Accelerating Fractal Image Compression by Multi-Dimensional Nearest Neighbor Search”, Proceedings DCC’95 Data Compression Conference, J. A. Storer and M. Colin (eds.) IEEE Comp. Soc. Press, March 1995.
[28] S.K. Mitra, C.A. Murthy, M.K. Kundu, “Technique for fractal image compression using genetic algorithm”, IEEE Trans Image Processing. Vol. 7 No. 4, pp. 586-593, April 1998.
[29] T.K. Truong, J.H. Jeng, I.S. Reed, P.C. Lee and A.Q. Li, “A fast encodind algorithm for fractal image compression using the DCT inner product”, IEEE Trans. Image processing, vol. 9, no. 4, pp. 529- 535, April 2000.
[30] Jinjiang Li, Da Yun, Qingsong Xie and Caiming Zhang, “Fractal Image Compression by ant colony algorithm”, Ninth International Conference for young computer scientists, 2008.
[31] H Lin and A N Venetsanopoulos, “A pyramid algorithm for fast fractal image compression”, Proceedings of the International Conference on Image Processing, 1995.
[32] Cangju Xing, “An adaptive domain pool scheme for fractal image compression”, International Workshop on Geoscience and remote sensing, 2008.
 [33] Jinshu Han, “Speeding up Fractal Image Compression based on local extreme points”, 8th International Conference on Software engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, 2007.

Keywords

Image Compression, Fractal image compression, Image partitioning