Advertisement

Computational Algorithmic Generation of High-Quality Colour Patterns

  • Alfonsas Misevičius
  • Evaldas Guogis
  • Evelina Stanevičienė
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 403)

Abstract

The purpose of this paper is to describe the computational algorithmic generation of high-quality colour patterns (digital halftones). At the beginning, the formal model for generation of the digital halftones, the so-called grey pattern problem (GPP) is introduced. Then, the heuristic algorithm for the solution of the grey pattern problem is discussed. Although the algorithm employed does not guarantee the optimality of the solutions found, still perfect quality, near-optimal (and in some cases probably optimal) solutions can be achieved within reasonable computation time. Further, we provide the preliminary results of the extensive computational experiments with the extra-large instance (data set) of the GPP. As a confirmation of the quality of the analytical solutions produced, we also give the visual representations of fine-looking graphic halftone patterns.

Keywords

combinatorial optimization heuristic algorithms grey pattern problem digital (colour) halftoning creative multimedia 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Burkard, R.E., Karisch, S., Rendl, F.: QAPLIB – a quadratic assignment problem library. J. Glob. Optim. 10, 391–403 (1997), http://www.seas.upenn.edu/qaplib (cited June 29, 2013)Google Scholar
  2. 2.
    Çela, E.: The Quadratic Assignment Problem: Theory and Algorithms. Kluwer, Dordrecht (1998)CrossRefzbMATHGoogle Scholar
  3. 3.
    Drezner, Z.: Finding a cluster of points and the grey pattern quadratic assignment problem. OR Spectrum 28, 417–436 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Kang, H.R.: Digital Color Halftoning. In: Dougherty, E.R. (ed.). SPIE/IEEE Series on Imaging Science & Engineering. The SPIE Optical Engineering Press/IEEE Press, Bellingham/Piscataway (1999) Google Scholar
  5. 5.
    Lau, D.L., Arce, G.R.: Modern Digital Halftoning, Sec. Ed. In: Liu, K.J.R. (ed.). Signal Processing and Communications Series. Marcel Dekker, New York-Basel (2008)Google Scholar
  6. 6.
    Misevičius, A.: Experiments with hybrid genetic algorithm for the grey pattern problem. Informatica 17, 237–258 (2006)zbMATHGoogle Scholar
  7. 7.
    Misevičius, A.: Generation of grey patterns using an improved genetic evolutionary algorithm: Some new results. Inform. Technol. Contr. 40, 330–343 (2011)Google Scholar
  8. 8.
    Misevicius, A.: An implementation of the iterated tabu search algorithm for the quadratic assignment problem. OR Spectrum 34, 665–690 (2012), doi:10.1007/s00291-011-0274-zMathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Misevičius, A., Rubliauskas, D.: Performance of hybrid genetic algorithm for the grey pattern problem. Inform. Technol. Contr. 34, 15–24 (2005)Google Scholar
  10. 10.
    Sivanandam, S.N., Deepa, S.N.: Introduction to Genetic Algorithms. Springer, Heidelberg (2008)zbMATHGoogle Scholar
  11. 11.
    Taillard, E.D.: Comparison of iterative searches for the quadratic assignment problem. Locat. Sci. 3, 87–105 (1995)CrossRefzbMATHGoogle Scholar
  12. 12.
    Taillard, E.D., Gambardella, L.M.: Adaptive memories for the quadratic assignment problem. Techn. Report. IDSIA-87-97, Lugano, Switzerland (1997)Google Scholar
  13. 13.
    Ulichney, R.A.: Digital Halftoning. MIT Press, London (1987)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Alfonsas Misevičius
    • 1
  • Evaldas Guogis
    • 2
  • Evelina Stanevičienė
    • 1
  1. 1.Department of Multimedia EngineeringKaunas University of TechnologyKaunasLithuania
  2. 2.Singleton LabsKaunasLithuania

Personalised recommendations