Combining neighbourhood-based and histogram similarity measures for the design of image quality measures

  • Authors:

  • Dietrich Van der Weken;Mike Nachtegael;Etienne Kerre

  • Affiliations:

  • Fuzziness and Uncertainty Modelling Research Unit, Department of Applied Mathematics and Computer Science, Ghent University, Krijgslaan 281 (building S9), 9000 Ghent, Belgium;Fuzziness and Uncertainty Modelling Research Unit, Department of Applied Mathematics and Computer Science, Ghent University, Krijgslaan 281 (building S9), 9000 Ghent, Belgium;Fuzziness and Uncertainty Modelling Research Unit, Department of Applied Mathematics and Computer Science, Ghent University, Krijgslaan 281 (building S9), 9000 Ghent, Belgium

  • Venue:
  • Image and Vision Computing
  • Year:
  • 2007

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Abstract

In this paper, we will show how fuzzy similarity measures are used in establishing measures for image quality evaluation. Similarity measures, originally introduced to express the degree of comparison between two fuzzy sets, can be applied to digital images. We will show how neighbourhood-based similarity measures and histogram similarity measures can be combined in order to improve the perceptive behaviour of these similarity measures. In this way, we obtained several new image quality measures, which outperform the Mean Squared Error in the sense of image quality evaluation because the results of the new measures coincide better with human perception.