Abstract
The main issue about an image is its size. Fractal Image Compression is a developing procedure which may represent an image by a contractive change on an image space for which the settled point is close to the primary image. This wide standard conceals a wide variety of coding designs, a robust segment of which have been explored in the rapidly creating collection of appropriated look into. While certain theoretical parts of this depiction are settled in, for the most part little thought has been given to the advancement of an understandable fundamental picture exhibit that would legitimize its use. Most basically fractal based plans are not forceful with the present best in class, yet these designs combining fractal compression and alternative techniques have gained widely more important ground. This review addresses an investigation of the most basic advances, both useful and theoretical in one of a kind fractal coding design. In this paper, we survey the fundamental models of the advancement of fractal objects with iterated function system (IFS) utilizing ICA and DBSCAN algorithms.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Hitashi, kaur G., Sungandha S.: Fractal image compression—a review. Int. J. Adv. Res. Comput. Sci. Softw. Eng. (2012)
Sun, Y., Xu, R., Chen, L., Hu, X.: Image compression and encryption scheme using fractal dictionary and Julia set. IET Image Process. 9(3), 173–183 (2015)
Sarabjeet, K., Anand Kumar, M.: Improved fractal based image compression for grayscale using combined shear and skew transformations. GJRA Global J. Res. Anal. 156–160 (2015)
Nadira Banu Kamal, A.R.: Iteration free fractal image compression for color images using vector quantization, genetic algorithm and simulated annealing. TOJSAT Online J. Sci. Technol. 5(1), 39–48 (2015)
Nodehi, A., Sulong, G., Al rodhaan, M., Abdullah-Al-dheelan, Rehmaan, A., Saba, T.: Intelligent fuzzy approach for fractal image compression; EURASIP-J. Adv. Sig. Process. 1–9 (Springer) (2014)
Jaseela, C.C., James, A.: A new approach to fractal image compression using DBSCAN. Int. J. Electr. Energy 2(1), 18–22 (2014)
Nadira Banu Kamal, A.R., Priyanga, P.: ICTACT J. Image Video Process. 4(3), 785–790 (2014)
Michael Vanitha, S., Kuppusamy, K.: Survey on fractal image compression. Int. J. Comput. Trends Technol. (IJCTT), 4(5), 1462–1464 (2013)
Sophin Seeli, D., Jeyakumar, M.K.: A study on fractal image compression using soft computing techniques. IJCSI Int. J. Comput. Sci. Iss. 9(6), 420–430, no 2 (2012). ISSN 1694-0814
Negi. A., Chauhan. Y.S, Rana, R.: Complex dynamics of ishikawa iterates for non integer values. Int. J. Comput. Appl. 9(2), 0975–8887 (2010)
Negi, A., Rani, M., Mahanti, P.K.: Computer simulation of the behavior of Julia sets using switching processes. Chaos Solitons Fractals 37, 1187–1192 (2008)
Negi, A.: Fractal generations and applications. Ph.D. thesis, Department of Mathematics, Gurukula Kangri Vishwavidyalaya, Hardwar (2006)
Berinde, V.: Iterative Approximation of Fixed Points. Editura Efemeride, Baia Mare
Kigami, J.: Analysis on Fractals. Cambridge University Press, Cambridge (2001)
Barnsley, M.: Fractals Everywhere. Academic Press, Inc., Boston, MA, pp. xii+396 (1988). ISBN 0-12-079062-9 MR0977274 (90e:58080)
Barnsley, M.F., Devaney, R.L., Mandelbrot, B.B., Peitgen, H.-O., Saupe, D., Voss, R.F.: The science of fractal images. With contributions by Yuval Fisher and Michael McGuire, pp. xiv+312. Springer, New York (1988). ISBN: 0-387-96608-0 MR0952853(92a:68145)
Peitgen, H.-O., Richter, P.H.: The Beauty of Fractals. Springer, Berlin (1986)
Huang, Z.: Mann and Ishikawa iterations with errors for asymptotically non expansive mappings. Comput. Math. Appl. 37(3), 1–7 (1999). MR1674407(2000a:47118)
Hutchinson, J.E.: Fractals and self-similarity. Indiana Univ. J. Math. 30, 713–743 (1981)
Falconer, K.J.: Fractal Geometry: Mathematical Foumiations and Applications. Wiley, Chichester (1990)
Bamsley, M.F., Hurd, L.P.: Fractal Image Compression. AK Peters Ltd., Wellesley, MA (1993)
Mandelbrot, B.B., Cannon, J.W.: Reviews: the fractal geometry of nature. Am. Math. Monthly 91(9), 594–598 (1984). MR1540536
Gonzalez, R., Eugene, R.: Digital Image Processing, p. 466 (2008)
Thyagarajan, S.K.: Still Image and Video Compression with MATLAB, p. 97100 (2007)
Jitendra, R., Tarun Kumar, S., Ajith, A., Vaishali: Glossary of metaheuristic algorithms. Comput. Inf. Syst. Ind. Manag. Appl. 9, 181–205 (2017)
Manish, J., Ambuj, A.K.: Analysis of different fractal image compression techniques. In: Proceedings of SMART-2017
Agarwal, A.K., Sharma, T., Saxena, A., Ather, D.: Search based software engineering in requisite phase of SDLC: a survey. Tech. J. LBSIMDS, 95–101. ISSN -09752374
Agarwal, T., Agarwal, A.K., Singh, S.K.: Cloud computing security: issues and challenges. In: Proceedings of SMART-2014, pp.10–14
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Joshi, M., Agarwal, A.K., Gupta, B. (2019). Fractal Image Compression and Its Techniques: A Review. In: Ray, K., Sharma, T., Rawat, S., Saini, R., Bandyopadhyay, A. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 742. Springer, Singapore. https://doi.org/10.1007/978-981-13-0589-4_22
Download citation
DOI: https://doi.org/10.1007/978-981-13-0589-4_22
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-0588-7
Online ISBN: 978-981-13-0589-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)