Fractal image compression: theory and application
Fractal image compression: theory and application
FIRE: fractal indexing with robust extensions for image databases
IEEE Transactions on Image Processing
A range/domain approximation error-based approach for fractal image compression
IEEE Transactions on Image Processing
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Many desirable properties make fractals a powerful mathematic model applied in several image processing and pattern recognition tasks: image coding, segmentation, feature extraction and indexing, just to cite some of them. Unfortunately, they are based on a strong asymmetric scheme, so suffering from very high coding times. On the other side, linear transforms are quite time balanced, allowing to be usefully integrated in real-time applications, but they do not provide comparable performances with respect to the image quality for high bit rates. Owning to their potential for preserving the original image energy in a few coefficients in the frequency domain, linear transforms also known a widespread diffusion in some side applications such as to select representative features or to define new image quality measures. In this paper, we investigate different levels of embedding linear transforms in a fractal based coding scheme. Experimental results have been organized as to point out what is the contribution of each embedding step to the objective quality of the decoded image.