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Digital Image Compression

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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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Author information

Authors and Affiliations

  1. Informatik II, Universität Würzburg, 97074, Würzburg, Germany

    Jürgen Albert

  2. Department of Mathematics, University of Turku, 20014, Turku, Finland

    Jarkko Kari

Authors
  1. Jürgen Albert
  2. Jarkko Kari

Corresponding author

Correspondence toJürgen Albert.

Editor information

Editors and Affiliations

  1. Inst. Informatik, Universität Leipzig, Augustusplatz 10-11, Leipzig, 04109, Germany

    Manfred Droste

  2. Institut für Diskrete, TU Wien, Wiedner Hauptstr. 8-10, Wien, 1040, Austria

    Werner Kuich

  3. 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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