Fractal Image Compression Using Quadtree Decomposition and Huffman Coding

Abstract

Fractal image compression can be obtained by dividing the original grey level image into unoverlapped blocks depending on a threshold value and the well known techniques of Quadtree decomposition. By using threshold value of 0.2 and Huffman coding for encoding and decoding of the image these techniques have been applied for the compression of satellite imageries. The compression ratio (CR) 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 show that for the Quad tree decomposition approach shows very significant improvement in the compression ratios and PSNR values derived from the fractal compression with range block and iterations technique. The results indicate that for a Lena image C R is 2.02 and PSNR values is 29.92, Satellite Rural image 3.08 and 29.34, Satellite urban image 5.99 and 28.12 respectively The results are presented and discussed in this paper.