Hybrid Genetic-Simulated Annealing Approach for Fractal Image Compression
In this paper a hybrid technique of Genetic Algorithm
and Simulated Annealing (HGASA) is applied for Fractal Image
Compression (FIC). With the help of this hybrid evolutionary
algorithm effort is made to reduce the search complexity of matching
between range block and domain block. The concept of Simulated
Annealing (SA) is incorporated into Genetic Algorithm (GA) in order
to avoid pre-mature convergence of the strings. One of the image
compression techniques in the spatial domain is Fractal Image
Compression but the main drawback of FIC is that it involves more
computational time due to global search. In order to improve the
computational time along with acceptable quality of the decoded
image, HGASA technique has been proposed. Experimental results
show that the proposed HGASA is a better method than GA in terms
of PSNR for Fractal image Compression.
[1] A.E.Jacquin, ÔÇÿImage coding Based on a Fractal theory of Iterated
contractive Image Transformations,- IEEE Transactions on Image
Processing. vol 1, Jan 92, pp 18-30
[2] A.E.Jacquin, "Fractal Image coding: A Review," Proc. IEEE, vol 81, pp.
1451-1465, 1993.
[3] M.F.Barnsley, Fractals Everywhere. New York: Academic 1988.
[4] M.F Barnsley and A.E Jacquin, "Application of recurrent iterative
function systems to images", Proc SPIE, 1001(3),122-131 (1998)
[5] Mingshui Li, Shanhu Ou and Heng Zhang, "The new progress in
research approach of fractal image compression" journal of engineering
graphics, 4(3), 2004, pp 143-152 .
[6] Xiaoping Wang, Liming Cao, "Genetic Algorithms-theory, application
and software reliability Xi" An Jiao Tong University Press (2002).
[7] David Goldberg, "Genetic algorithms in search, optimization and
machine learning" Addison-Wesley Publishing Company: MA (1989).
[8] Kalyanmoy Deb, "Optimization for engineering design" Prentice Hall
of India,2000.
[9] Y.Chakrapani, K.Soundera Rajan, "A comparative approach to fractal
image compression using genetic algorithm and simulated annealing
technique" Asian Journal of Information technology 7(7), pp 285-289.
[10] M.Hassaballah, M.M.Makky, Youssuf B. Mahdy, "A fast fractal image
compression method based entropy" Electronic letters on computer
vision and image analysis, 5(I) 2005, pp 30-40.
[11] Zhang Chao, Zhou Yiming, Zhang Zengke, "Fast fractal image encoding
based on special image features" Journal of Tsinghua science and
technology, Vol12, No 1, 2007,pp 58-62
[1] A.E.Jacquin, ÔÇÿImage coding Based on a Fractal theory of Iterated
contractive Image Transformations,- IEEE Transactions on Image
Processing. vol 1, Jan 92, pp 18-30
[2] A.E.Jacquin, "Fractal Image coding: A Review," Proc. IEEE, vol 81, pp.
1451-1465, 1993.
[3] M.F.Barnsley, Fractals Everywhere. New York: Academic 1988.
[4] M.F Barnsley and A.E Jacquin, "Application of recurrent iterative
function systems to images", Proc SPIE, 1001(3),122-131 (1998)
[5] Mingshui Li, Shanhu Ou and Heng Zhang, "The new progress in
research approach of fractal image compression" journal of engineering
graphics, 4(3), 2004, pp 143-152 .
[6] Xiaoping Wang, Liming Cao, "Genetic Algorithms-theory, application
and software reliability Xi" An Jiao Tong University Press (2002).
[7] David Goldberg, "Genetic algorithms in search, optimization and
machine learning" Addison-Wesley Publishing Company: MA (1989).
[8] Kalyanmoy Deb, "Optimization for engineering design" Prentice Hall
of India,2000.
[9] Y.Chakrapani, K.Soundera Rajan, "A comparative approach to fractal
image compression using genetic algorithm and simulated annealing
technique" Asian Journal of Information technology 7(7), pp 285-289.
[10] M.Hassaballah, M.M.Makky, Youssuf B. Mahdy, "A fast fractal image
compression method based entropy" Electronic letters on computer
vision and image analysis, 5(I) 2005, pp 30-40.
[11] Zhang Chao, Zhou Yiming, Zhang Zengke, "Fast fractal image encoding
based on special image features" Journal of Tsinghua science and
technology, Vol12, No 1, 2007,pp 58-62
@article{"International Journal of Information, Control and Computer Sciences:55892", author = "Y.Chakrapani and K.Soundera Rajan", title = "Hybrid Genetic-Simulated Annealing Approach for Fractal Image Compression", abstract = "In this paper a hybrid technique of Genetic Algorithm
and Simulated Annealing (HGASA) is applied for Fractal Image
Compression (FIC). With the help of this hybrid evolutionary
algorithm effort is made to reduce the search complexity of matching
between range block and domain block. The concept of Simulated
Annealing (SA) is incorporated into Genetic Algorithm (GA) in order
to avoid pre-mature convergence of the strings. One of the image
compression techniques in the spatial domain is Fractal Image
Compression but the main drawback of FIC is that it involves more
computational time due to global search. In order to improve the
computational time along with acceptable quality of the decoded
image, HGASA technique has been proposed. Experimental results
show that the proposed HGASA is a better method than GA in terms
of PSNR for Fractal image Compression.", keywords = "Fractal Image Compression, Genetic Algorithm,
HGASA, Simulated Annealing.", volume = "2", number = "11", pages = "3789-6", }