Fractals everywhere
Computer Vision, Graphics, and Image Processing
Fractal image compression: theory and application
Fractal image compression: theory and application
The algorithmic beauty of seaweeds, sponges, and corals
The algorithmic beauty of seaweeds, sponges, and corals
Fractal Geometry in Digital Imaging
Fractal Geometry in Digital Imaging
Accelerating Compression Times in Block Based Fractal Image Coding Procedures
EGUK '02 Proceedings of the 20th UK conference on Eurographics
Accelerating fractal image compression by multi-dimensional nearest neighbor search
DCC '95 Proceedings of the Conference on Data Compression
Chaos and Fractals
The use of image smoothness estimates in speeding up fractal image compression
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
A review of the fractal image coding literature
IEEE Transactions on Image Processing
Combining fractal image compression and vector quantization
IEEE Transactions on Image Processing
A fast encoding algorithm for fractal image compression using the DCT inner product
IEEE Transactions on Image Processing
Speed-up in fractal image coding: comparison of methods
IEEE Transactions on Image Processing
Fast fractal image encoding based on adaptive search
IEEE Transactions on Image Processing
Adaptive approximate nearest neighbor search for fractal image compression
IEEE Transactions on Image Processing
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Fractal image compression is an engaging and worthwhile technology that may be successfully applied to still image coding, especially at high compression ratios. Unfortunately, the large amount of computation needed for the image compression (encoding) stage is a major obstacle that needs to be overcome. In spite of numerous and many-sided attempts to accelerate fractal image compression times, the “speed problem” is far from being carried to its conclusion. In the paper, a new version (strategy) of the fractal image encoding technique, adapted to process bi-level (black and white) images, is presented. The strategy employs the necessary image similarity condition based on the use of invariant image parameters (image smoothness indices, image coloration ratios, etc.). It is shown that no images can be similar (in the mean squared error sense) if their respective parameter values differ more than somewhat. In the strategy proposed, the necessary image similarity condition plays a key role - it is applied to speed-up the search process for optimal pairings (range block-domain block), i.e., it enables to narrow the domain pool (search region) for each range block. Experimental analysis results show that implementation of the new fractal image encoding strategy accelerates bi-level image compression times considerably. Exceptionally good results (compression times and quality of restored images) are obtained for silhouette images.