Optimization of Fractal Image Compression

Fractal encoding is a promising method of image compression. It is built on the basis of the forms found in the image and the generation of repetitive blocks based on mathematical translations. The technique seems to be moved theoretically and practically, but it requires enormous programming time due to the excessive resources required when compressing large volumes of data. On the other hand, metaheuristics represent all of the methods used to solve difficult optimization problems with less consumption of resources. They are marked by their rapid convergence and their lessening in research difficulties. In this chapter, we have tried to apply a new experience around the performance of organic metaheuristics inspired by nature, which are, respectively, the wolf pack algorithm (WPA) and the bat-inspired algorithm (BIA), as bioinspired techniques to optimize the fractal image compression (FIC). Experiments show the enhancement of diverse characteristics (coding time, compression rate (CR), peak signal-to-noise ratio (PSNR), and mean square error (MSE)). In addition, an assessment of the proposed approaches via many other approaches highlights this improvement