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Fast and Innovative Suitable Domain Search for Fractal Image Compression

Vijayshri Chaurasia, Ajay Somkuwar

Abstract


Fractal Image compression is a very advantageous technique in the field of image compression. It shows the merits like very high compression ratio, high decompression speed, high bit rate and resolution independence. This technique is based on existence of self-symmetry in image. The coding phase of this technique is very time consuming because of computational expenses of suitable domain search. Despite the advances made, the huge encoding time remains the main holdup of this technique. In this paper we have proposed an approximation error based speed-up technique with the use of feature extraction and investigate the effect of efficient data structure on its performance. Proposed scheme reduces the number of range-domain comparisons with significant amount and gives improved time performance.

Keywords


Domain Block, Feature Extraction, Feature Vector, Range Block, Suitable Domain Search.

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References


Y. Fisher, Fractal Image Compression: Theory and Application. New York: Springer-Verlag, 1994.

M. Barnsley, Fractals Everywhere. New York: Academic, 1988.

A.E. Jacquin, “A novel fractal block-coding technique for digital Images”, ICASSP International Conference on Acoustics, Speech, and Signal Processing, 1990.

A.E Jaquin, “Image coding based on a fractal theory of iterated contractive image transformation”, IEEE Trans. On Image Processing, vol. 1, No 1, January 1992.

A.E Jaquin, “Fractal image coding: A review”, proceeding of tile IEEE, vol. 81, No.10, October 1993.

R. Distasi, M. Nappi and D. Riccio, “A range/domain approximation error- based approach for fractal image compression”, IEEE Trans. Image processing, vol. 15, no. 1, pp. 89-97, Jan. 2006.

E. Horowitz; S. Sahni, “Fundamentals of Data structure”, Galgotia Booksource, New Delhi, India, 1981.

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.

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.

S. Weistead, Fractal and Wavelet Image Compression Technique: PHI, India, 2005.

Polvere, M.; Nappi, M, “Speed-up in fractal image coding: comparison of methods”, IEEE Trans Image Processing. Vol. 9 No. 6, pp. 1002-1009, June 2000.

Mark S. Nixon, Alberto S. Aguado, “Feature extraction and image processing”, second edition, Academic Press, Oxford, 2002.

B. Ramarurthi and A. Gersho, “Classified vector quantization of images”, IEEE Trans. on Communications, vol. COM-34, No 11, Nov 1986.

D.N. Elhance; Veena Elhance: B.M. Aggrawal, “Fundamentals of Statistics”, Kitab Mahal, India, 2000.

Jon Dattorro, “Convex Optimization & Euclidian Distance Geometry”, Meboo Publishing USA, 2005.

E. Horowitz; S. Sahni, “Fundamentals of Data structure”, Galgotia Booksource, New Delhi, India, 1981.


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