Domain classification using B+trees in fractal image compression

The computational complexity of fractal image compression is mainly because of the huge number of comparisons required to find a matching domain block corresponding to the range blocks within the image. Various schemes have been presented by researchers for domain classification which can lead to significant reduction in the time spent for range-domain matching. All the schemes propose to first separate domains into different classes and then select the appropriate class for matching with selected range block. Here, we propose a dynamic classification scheme based on local fractal dimensions. The method can be experimented with other features of image blocks measured locally. In this work we have investigated the computational efficiency of multi-way search trees for storing domain information. The domains can be listed in a B+ tree ordered on one or more selected local features of each domain.