The complexity in search of suitable range domain and considerable loss in compression parameters like PSNR and MSE are the major constrictions of the baseline fractal image compression. Hence the existing research is focused on finding optimal solutions to pace up the search rate with marginal loss of image parameters on compression. Major existing fast search fractal algorithms attain the fractal search speed lowering the image quality. Addressing this, a Squirrel inspired fast search (SIFS)method is proposed for fractal image compression (FIC). Existing methods mainly depend on wavelet classification, the proposed SIFS uses methodical vector of range blocks based on the similarity and optimizing the search based on dynamic behavior of flying squirrels and their efficient way of gliding by the coordinate distance. The proposed SIFS method uses foraging behavior of flying squirrels to find the best range block search showing scalable improvements in search complexity to Particle Swarm Optimization and Genetic Algorithm based methods. The noteworthy reduction in MSE (Mean Square Error) calculations is observed as only six of the eight dihedral transformations are enough to compare the range blocksimilarity in the proposed SIFS. Proposed method is experimented on different kinds of images including medical MRI image and results found are encouraging.

Squirrel Search, Fractal Image, Genetic Algorithm, Particle swarm, MRI &, Medical Imaging