Part of the book series:Monographs in Theoretical Computer Science. An EATCS Series ((EATCS))
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Abstract
Nondeterministic finite automata with states and transitions labeled by real-valued weights have turned out to be powerful tools for the representation and compression of digital grayscale and color images. The addressing of pixels by input-sequences is extended to cover multi-resolution images. Encoding algorithms for such weighted finite automata (WFA) exploit self-similarities for efficient image compression, outperforming the well-known JPEG baseline standard most of the time. WFA-concepts are embedded easily into weighted finite transducers (WFT) which can execute several natural operations on images in their compressed form and also into so-called parametric WFA, which are closely related to generalized Iterated Function Systems.
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Authors and Affiliations
Informatik II, Universität Würzburg, 97074, Würzburg, Germany
Jürgen Albert
Department of Mathematics, University of Turku, 20014, Turku, Finland
Jarkko Kari
- Jürgen Albert
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- Jarkko Kari
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Correspondence toJürgen Albert.
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Editors and Affiliations
Inst. Informatik, Universität Leipzig, Augustusplatz 10-11, Leipzig, 04109, Germany
Manfred Droste
Institut für Diskrete, TU Wien, Wiedner Hauptstr. 8-10, Wien, 1040, Austria
Werner Kuich
Fak. Informatik, TU Dresden, Nöthnitzer Str. 46, Dresden, 01187, Germany
Heiko Vogler
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Albert, J., Kari, J. (2009). Digital Image Compression. In: Droste, M., Kuich, W., Vogler, H. (eds) Handbook of Weighted Automata. Monographs in Theoretical Computer Science. An EATCS Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01492-5_11
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