Performance Evaluation of Image Quality Based on Fractal Image Compression

Fractal techniques for image compression have recently attracted a great deal of attention. Fractal image compression is a relatively recent technique based on the representation of an image by a contractive transform, on the space of images, for which the fixed point is close to the original image. This broad principle encompasses a very wide variety of coding schemes, many of which have been explored in the rapidly growing body of published research.Unfortunately, little in the way of practical algorithms or techniques has been published. Here present a technique for image compression that is based on a very simple type of iterative fractal. In our algorithm a wavelet transform (quadrature mirror filter pyramid) is used to decompose an image into bands containing information from different scales (spatial frequencies) and orientations. The conditional probabilities between these different scale bands are then determined, and used as the basis for a predictive coder. We undertake a study of the performance of fractal image compression. This paper focuses important features of compression of still images, including the extent to which the quality of image is degraded by the process of compression and decompression.The numerical experiment is done by considering various types of images and by applying fractal Image compression to compress an image. It was found that fractal yields better result as compared to other compression techniques. It provide better peak signal to noise ratio as compare to other techniques, but it take higher encoding time.The numerical results are calculated in Matlab. KeywordsImage Compression, Fractal Image Compression, Compression ratio (CR), Peak Signal to Noise Ratio (PSNR), Encode Time, Decode Time.