Fractal Image Compression of Satellite Imageries

Abstract

Fractal image coding has the advantage of higher compression ratio, but is a lossy compression scheme. The encoding procedure consists of dividing the image into range blocks and domain blocks and then it takes a range block and matches it with the domain block. The image is encoded by partitioning the domain block and using Affine transformation to achieve fractal compression. The image is reconstructed using iterative functions and inverse transforms. In the present work the fractal coding techniques are applied for the compression of satellite imageries. The compression ratio and Peak Signal to Noise Ratio (PSNR) values are determined for three types of images namely standard Lena image, Satellite Rural image and Satellite Urban image. The Matlab simulation results for the reconstructed image for 4 iterations show that for a compression ratio ~3.2 and PSNR values achievable for Lena image ~12, Satellite Rural image ~17.0 and for Satellite urban image ~22.Comparison of the present results with the EZW coding indicates that, the fractal compression techniques are found more effective for compression of Satellite Urban imageries since the Satellite Urban images contains more fractals compared to that of Satellite Rural image and Lena image. General TermsStudy of Fractal image compression