Dynamic Domain Classification for Fractal Image Compression
This is an incremental improvement for researchers and practitioners in image compression, addressing a known bottleneck in fractal methods.
The paper tackles the high encoding time in fractal image compression by proposing a dynamic domain pool preparation for each range block, which results in significant reduction in encoding time.
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.