Robust estimation of parameter for fractal inverse problem
In this paper, some similarity measures for fractal image compression (FIC) are introduced, which are robust against noises. In the proposed methods, robust estimation technique from statistics is embedded into the encoding procedure of the fractal inverse problem to find the parameters. When the original image is corrupted by noises, we hope that the proposed scheme is insensitive to those noises presented in the corrupted image. This leads to a new concept of robust estimation of fractal inverse problem. The proposed least absolute derivation (LAD), least trimmed squares (LTS), and Wilcoxon FIC are the first attempt toward the design of robust fractal image compression which can remove the noises in the encoding process. The main disadvantage of the robust FIC is the computational cost. To overcome this drawback, particle swarm optimization (PSO) technique is utilized to reduce the searching time. Simulation results show that the proposed FIC is robust against the outliers in the image. Also, the PSO method can effectively reduce the encoding time while retaining the quality of the retrieved image.
fractal inverse problem least absolute derivation least trimmed squares particle swarm optimization wilcoxon norm robust image compression