Fractals are geometric patterns generated by
Iterated Function System theory. A popular technique known as
fractal image compression is based on this theory, which assumes
that redundancy in an image can be exploited by block-wise selfsimilarity and that the original image can be approximated by a
finite iteration of fractal codes. This technique offers high
compression ratio among other image compression techniques.
However, it presents several drawbacks, such as the inverse
proportionality between image quality and computational cost.
Numerous approaches have been proposed to find a compromise
between quality and cost. As an efficient optimization approach,
genetic algorithm is used for this purpose. In this paper, a
crowding method, an improved genetic algorithm, is used to
optimize the search space in the target image by good
approximation to the global optimum in a single run. The
experimental results for the proposed method show good
efficiency by decreasing the encoding time while retaining a high
quality image compared with the classical method of fractal
image compression.
Keywords
Fractal; Iterated Function System (IFS); Genetic
algorithm (GA); Crowding method; Fractal Image Compression
(FIC)
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