 L'uso dell'ottimizzazione non lineare nella procedura di compressione di immagini con IFSDepartmental Working Papers from Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano Abstract: Using nonlinear optimization techniques in fractal image compression. Fractal image compression generally seeks to express an image as a union of spatially contracted and greyscale modified copies of subsets of itself. Most practical as well as theoretical works in image processing and mathematical imaging consider images as real-valued functions u: X->R where X denotes the base or pixel space over which the images are defined. After all, at a particular point x in a (for simplicity) greyscale image I, we observe a pixel with a well defined intensity value, which we denote as u(x). Various function spaces F(X) may be considered. It has been found that an image u may be very well approximated by a union of shrunken and distorted copies of subsets of itself. This has been the basis of the so called "inverse problem" for fractal image compression which, after some transformations, can be reduced to a nonlinear optimization problem. Keywords: Iterated function systems; inverse problem; nonlinear (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:mil:wpdepa:2001-04 Access Statistics for this paper More papers in Departmental Working Papers from Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano Via Conservatorio 7, I-20122 Milan - Italy. Contact information at EDIRC. |
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