Accelerating fractal image compression by domain pool reduction, adaptive partitioning and structural block classification

The time consuming part of the encoding step is the search for an appropriate domain for each range. Several methods have been devised to accelerate the search and reduce the encoding complexity. In this paper we implement both quadtree and diamond partitioning. Quadtree partitioning uses horizontally and vertically oriented domains; that are twice as large as the ranges. This works extremely well for images that have a horizontal and vertical block structure. Many images also possess block structures oriented in other directions. Thus it makes sense to add domains that are of diamond or other kinds of shapes so that a large range block may find a "close" domain counterpart to avoid being further partitioned.. The diamond is well suited for our needs because it is already in a square shape and only rotational transformations of 45/spl deg/ are needed to related them to our square ranges. Adding diamond domains reduces the number of range partitions, increases the BPP (bits per pixel) of the compressed images, and increases the fidelity of encoded images. Finally, we implemented the post-processing of the decoded images to filter out the contour effects due to quantization of the shift (s) and offset (o) parameters.