A generalized fractal transform for measure-valued images
AbstractFractal image coding generally seeks to express an image as a union of spatially contracted and greyscale modified copies of subsets of itself. Generally,images are represented as functions u(x) and the fractal coding method is conducted in the framework of L^2 or L^1. Here we formulate a method of fractal image coding on measure-valued images: At each point \mu(x) is a probability measure overthe range of allowed greyscale values. We construct a complete metric space (Y,d_Y )of measure-valued images, \mu : X -> M(Rg), where X is the base or pixel space and M(Rg) is the set of probability measures supported on the greyscale range Rg. A method of fractal transforms is formulated over the metric space (Y,d_Y ). Under suitable conditions, a transform M : Y -> Y is contractive, implying the existence of a unique fixed point measure-valued function \mu^*= M\mu^*. We also show that the pointwise moments of this measure satisfy a set of recursion relations that are generalizations of those satisfied by moments of invariant measures of Iterated Function Systems with Probabilities.
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Bibliographic InfoPaper provided by Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano in its series Departmental Working Papers with number 2008-38.
Date of creation: 09 Dec 2008
Date of revision:
Measure-valued images; multifunctions; self-similarity; fractal;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-06-17 (All new papers)
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