Improve Image Quality Using Hybrid Wavelet Fractal Image Coder

In this paper, the transformation function used in fractal image compression is chosen in such a way that its unique fix point is a close approximation of the input image. Compression occurs because storing the details of the image transform also known as encoding takes up much less space than the original image. Decompression or decoding involves applying the transform repeatedly to an arbitrary starting image, to arrive at a (fractal) image that is either the original, or one very similar to it.image coding can provide a highly reconstructed image quality with a high compression ratio, is independent of resolution, and has fast decoding process [4].Based on the standard fractal transformation in spatial domain, simple relations may be found relating coefficients in detail sub bands in the wavelet domain. In this paper we evaluate a hybrid wavelet-fractal image coder, and we test its ability to compress the different images and decompress the same [2]. A comparative study between the hybrid wavelet fractal coder and standard fractal coding compression technique have been made in order to investigate the compression ratio with high quality of the image using peak signal to noise ratio and also computation (encoding and decoding) time. We find that the superior performance of the hybrid wavelet-fractal coder (WFC). In The WFC uses the fractal contractive mapping to predict the wavelet coefficients of the higher resolution from those of the lower resolution and then encode the prediction residue with a bit plane wavelet coder. The fractal prediction is adaptively applied only to regions where the rate saving offered by fractal prediction justifies its overhead. A rate-distortion criterion is derived to evaluate the fractal rate saving and used to select the optimal fractal parameter set for WFC.