A new method of neural network based fast fractal image compression

  • Ashok Agarwal Dr. K. N. Modi University, Rajasthan
  • Dr. J. S. Yadav MANIT, Bhopal


Fractal compression is a loss compression method for digital images, based on fractals. The method is best suited for textures and natural images, relying on the fact that parts of an image often resemble other parts of the same image. Fractal Image Compression (FIC) techniques take more time to perform processes are encoding and global search. Many different researchers and companies are trying to develop a new algorithm to reach shorter encoding time and smaller files. But there are still some problems with fractal compression. Fractal image compression is promising both theoretically and practically. The encoding speed of the traditional full search method is a key factor rendering the fractal image compression unsuitable for real-time application. The primary objective of this paper is to investigate the comprehensive coverage of the principles and techniques of fractal image compression. The experimental result shows that the application of the designed hybrid image compression method can increase the signal-to-noise ratio of an image while the high compression ratio of the image is guaranteed.


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Author Biographies

Ashok Agarwal, Dr. K. N. Modi University, Rajasthan
Ph.D Scholor
Dr. J. S. Yadav, MANIT, Bhopal
Associate Professor, ECE


[1] A. Lapp and H. G. Kranz, “The Use of the CIGRE Data Format for PD Diagnosis Applications”, IEEE Trans. Dielectr. Electr. Insul., Vol. 7, pp. 102¬112, 2000.
[2] E. Gulski, “Computer-aided Measurement of Partial Discharges in HV
Equipment”, lEEE Trans. Electr. Insul., Vol. 28, pp. 969-983, 1993.
[3] E. Gulski, “Digital Analysis of Partial Discharges”, IEEE Trans.Electr.
Insul., Vol. 2, pp. 822-837, 1995.
[4] J. Li, C. Sun, L. Du, X. Li and Q. Zhou, “Study on Fractal Dimension of PD Gray Intensity Image”, Proc. Chinese Soc. Electr. Eng., Vol. 22, pp. 123¬127, 2002 (in Chinese).
[5] E. M. Lalitha and L. Satish, “Fractal Image Compression for Classification of PD Sources”, IEEETrans. Dielectr. Electr. Insul., Vol. 5, pp.
550-557, 1998.
[6] A. Krivda, E. Gulski, L. Satish and W.S. Zaengl, “The Use of Fractal Features for Recognition of 3-D Discharge Patterns”, IEEE Trans. Dielectr.
Electr. Insul., Vol. 2, pp. 889-892, 1995.
[7] H. O. Peitgen, H. Jurgens and D. Saupe, Chaos and Fractals: New Fontiers of Science, Springer-Verlag New York, Inc. 1992.
[8] A. E. Jacquin, “Fractal Image Coding: a Review”, Proc. IEEE, Vol. 81, pp.1451-1465, 1993.
[9] J. Li, C. Sun, S. Grzybowski and C.D. Taylor, “Partial Discharge Recognition by Using a Group of New Feature”,IEEE Trans. Dielectr. Electr.
Insul., Vol. 13^ pp. 1245-1253, 2006.
[10] J. Li, Study on Methods of Recognition Feature Extraction and Fractal Compression for Partial Discharge Gray Intensity Images”, Ph.D.
Dissertation, Chongqing University, 2001 (in Chinese).
[11] M. F. Barnsley, R. L. Devaney, B. B. Mandebrot, H. O. Peitgen, D. Saupe and R. F. Voss, The Science of Fractal Images, Springer-Verlag New York, Inc. 1998.
[12] S. Chen and L. Zhang, Fractal and Image Compression, Shanghai Science-Technology and Education Publishing Company Press, Shanghai, 1st Edition, (in Chinese), 1998.
[13] R. C. Gonzalez, R. E. Woods and S. L. Eddins, Digital Image Processing Using MATLAB, Prentice Hall; 1st Edition, 2003.
How to Cite
AGARWAL, Ashok; YADAV, Dr. J. S.. A new method of neural network based fast fractal image compression. International Journal of Research and Engineering, [S.l.], v. 2, n. 10, p. 27-29, oct. 2015. ISSN 2348-7860. Available at: <https://digital.ijre.org/index.php/int_j_res_eng/article/view/106>. Date accessed: 15 sep. 2019.


Contractive transform, domain classification and feature vector, Partial discharge image, pattern recognition, fractal image compression