Crowding Optimization Method to Improve Fractal Image Compressions Based Iterated Function

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 self-similarity 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.

@article{Al-Bundi2016,
  title     = {Crowding Optimization Method to Improve Fractal Image Compressions Based Iterated Function},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {7},
  number    = {7},
  year      = {2016},
  publisher = {The Science and Information Organization},
  author    = {Shaimaa S. Al-Bundi and Nadia M. G. Al-Saidi and Neseif J. Al-Jawari},
  doi       = {10.14569/IJACSA.2016.070755},
  url       = {https://doi.org/10.14569/IJACSA.2016.070755}
}