Fixed Range Block Segmentation and Classification for Fractal Image Compression of Satellite Imageries

Abstract

The fixed range block segmentation and classification for fractal image compression has been carried out for Standard Lena and Satellite imageries. The encoding procedure consists of dividing the image into range blocks and domain blocks. Classification of sub image into three major classes and 24 different subclasses for every major classes. There is total domain and range blocks are represented in 72 classes. Then, each range block is to search the corresponding domain block to find the best match. Affine transformation and entropy coding are applied to achieve fractal compression. The image is reconstructed using an iterative function and inverse transforms. The Mat lab simulation for the standard Lena and Satellite imageries have been carried out for the fixed range block sizes of 4x4, 8x8 and 16x16. The results show that for the fixed range block size 4x4 achieve higher Peak Signal to Noise Ratio (PSNR) ~ 28dB and Compression Ratio (CR) of ~ 3.7 for the Lena and Satellite imageries. The results for the fixed range block size 16x16 achieve higher CR of ~69 and PSNR of ~20dB for the Lena and Satellite imageries. This method is found to indicate higher PSNR, good CR and reduced encoding -- decoding time. The results are presented and discussed in the paper.