Multiple blocs classification for fast encoding in fractal-based images compression

Increasing the search speed for matching range and domain blocs is the main challenge facing fractal-based images compression. One way to remedy at this problem is to classify image blocs into categories and only search among domain blocs which are in the same category as the target range bloc. Since image blocs with a simple edge are a very important portions of the perceptual information content in image, we propose a method to both identify and classify this kind of blocs according to their edge presentation. We refer to this method as forced classification. This method is combined with other suitable methods of blocs classification available in the literature to allow a fast and encoding of grey-scale images. The result obtained is good, the encoding time for a 512/spl times/512 image is reduced by a factor of 37.52% than using the Fisher classification only, while the loss of image quality is low.