Dynamic Domain Classification for Fractal Image Compression

TL;DR

This paper introduces a dynamic domain classification method for fractal image compression, significantly reducing encoding time by selecting domain pools based on local fractal dimension for each range block.

Contribution

It proposes a novel dynamic domain pool selection approach using local fractal dimension, improving encoding efficiency over static methods.

Findings

Encoding time is significantly reduced.

Dynamic domain pool selection improves matching accuracy.

Method outperforms static domain pool approaches.

Abstract

Fractal image compression is attractive except for its high encoding time requirements. The image is encoded as a set of contractive affine transformations. The image is partitioned into non-overlapping range blocks, and a best matching domain block larger than the range block is identified. There are many attempts on improving the encoding time by reducing the size of search pool for range-domain matching. But these methods are attempting to prepare a static domain pool that remains unchanged throughout the encoding process. This paper proposes dynamic preparation of separate domain pool for each range block. This will result in significant reduction in the encoding time. The domain pool for a particular range block can be selected based upon a parametric value. Here we use classification based on local fractal dimension.