Generalized square isometries — an improvement for fractal image coding
Most recent advances in fractal image coding have been concentrating on better adaptive coding algorithms, on extending the variety of the blocks and on search strategies to reduce the encoding time. Very little has been done to challenge the linear model of the fractal transformations used so far in practical applications. In this paper we explain why effective non-linear transformations are not easy to find and propose a model based on conformai mappings in the geometric domain that are a natural extension of the affine model. Our compression results show improvements over the linear model and support the hope that a deeper understanding of the notion of self-similarity would further advance fractal image coding.
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