SQUIRREL INSPIRED IMPROVED SEARCH METHOD FOR FRACTAL IMAGE COMPRESSION ON STANDARD AND MAGNETIC RESONANCE IMAGES

Abstract

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 oncompression. Major existing fast search fractal algorithms attain the fractal search speed lowering the image quality.Addressingthis,aSquirrelinspiredfastsearch(SIFS)method is proposed for fractal image compression (FIC). Existing methods mainly depend onwaveletclassification,theproposedSIFSusesmethodicalvectorofrangeblocksbased on thesimilarity 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 enoughtocompare the range blocksimilarityintheproposedSIFS. Proposed method is experimented on different kinds of images including medical MRI image and results found are encouraging.