Combining tree and feature classification in fractal encoding of images

Abstract

Summary form only given. One of the main problems with fractal compression of images is the long encoding time, due to the repeated search of the domain block pool. Faster search can be achieved through block classification. This is done by grouping the domain blocks independently and online into predefined classes. Only the class of a range block is then searched for a matching domain. Bani-Eqbal (1995) presented another method for speeding up the search. It is based on an incremental evaluation of the distance between two blocks. We structure the domain pool into a tree. For a given range, we home onto a list of matching domains through a pruning algorithm based on the evaluation procedure. In this work we combine the tree method with the block classification to get an even faster search. In block classification the classes are stored without any additional structure, usually as lists, and searched linearly until a best match is found. We should expect an enhancement if we arrange the classes into trees, and use the tree algorithm for the search. Another way to view this is to regard the domain pool grown into lots of small trees rather than one big one. A significant speed-up is achievable.