Comparison of image compression using GA, ACO and PSO techniques

Abstract

In fractal image compression (FIC) is based on the partitioned iterated function system (PIFS) which utilizes the self-similarity property in the image to achieve the purpose of compression., the linear regression technique from robust statistics is embedded into the encoding procedure of the fractal image compression Another drawback of FIC is the poor retrieved image qualities when compressing corrupted images, the fractal image compression scheme should be insensitive to those noises presented in the corrupted image. This leads to a new concept of robust fractal image compression. The FIC is one of our attempts toward the design of robust fractal image compression. The main disadvantage of FIC is the high computational cost. To overcome this drawback, the technique described here utilizes the optimization techniques, like GA, ACO and PSO which greatly decreases the search space for finding the self similarities in the given image. FIC is robust against outliers in the image. Also, the optimization techniques can effectively reduce the encoding time while retaining the quality of the retrieved.