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A Class of Image Metrics Based on the Structural Similarity Quality Index

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Image Analysis and Recognition (ICIAR 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6753))

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Abstract

We derive mathematically a class of metrics for signals and images, considered as elements of R N, that are based upon the structural similarity (SSIM) index. The important feature of our construction is that we consider the two terms of the SSIM index, which are normally multiplied together to produce a scalar, as components of an ordered pair. Each of these terms is then used to produce a normalized metric, one of which operates on the means of the signals and the other of which operates on their zero-mean components. We then show that a suitable norm of an ordered pair of metrics defines a metric in R N.

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Author information

Authors and Affiliations

  1. Department of Applied Mathematics, Faculty of Mathematics, University of Waterloo, Waterloo, Ontario, Canada, N2L 3G1

    Dominique Brunet & Edward R. Vrscay

  2. Department of Electrical and Computer Engineering, Faculty of Engineering, University of Waterloo, Waterloo, Ontario, Canada, N2L 3G1

    Zhou Wang

Authors
  1. Dominique Brunet
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  2. Edward R. Vrscay
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  3. Zhou Wang
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Waterloo, N2L 3G1, Waterloo, ON, Canada

    Mohamed Kamel

  2. Faculty of Engineering, Institute of Biomedical Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    Aurélio Campilho

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© 2011 Springer-Verlag Berlin Heidelberg

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Brunet, D., Vrscay, E.R., Wang, Z. (2011). A Class of Image Metrics Based on the Structural Similarity Quality Index. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2011. Lecture Notes in Computer Science, vol 6753. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21593-3_11

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  • DOI: https://doi.org/10.1007/978-3-642-21593-3_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21592-6

  • Online ISBN: 978-3-642-21593-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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