Adaptive Gray Level Difference to Speed Up Fractal Image Compression

Abstract

Fractal image compression is a lossy compression technique that has been developed in the early 1990s. It makes use of the local self similarity property existing in an image and finds a contractive mapping affine transformation (fractal transform) T, such that the fixed point of T is close to the given image in a suitable metric. It has generated much interest due to its promise of high compression ratios with good decompression quality. The other advantage is its multiresolution property, i.e. an image can be decoded at higher or lower resolutions than the original without much degradation in quality. However, the encoding time is computationally intensive. In this paper, a new method to reduce the encoding time based on computing the gray level difference of domain and range blocks, is presented. A comparison for best match is performed between the domain and range blocks only if the range block gray level difference is less than the domain block gray level difference. This reduces the number of comparisons, and thereby the encoding time considerably, while obtaining good fidelity and compression ratio for the decoded image. Experimental results on standard gray scale images (512times512, 8 bit) show that the proposed method yields superior performance over conventional fractal encoding