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Binary Image Quality Assessment—A Hybrid Approach Based on Binarization Evaluation Methods

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Artificial Intelligence Perspectives in Intelligent Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 464))

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Abstract

In the paper the idea of multiple metrics fusion for binary image quality assessment is presented together with experimental results obtained using the images from Bilevel Image Similarity Ground Truth Archive. As the performance evaluation of any full-reference image quality assessment metric requires both the knowledge of reference images with perfect quality and the results of subjective evaluation of distorted images, several such datasets have been developed during recent years. Nevertheless, the specificity of binary images requires the use of some other metrics which should also be verified in view of their correlation with subjective perception. Such task can be done using a dedicated database of binary images followed by the combination of multiple metrics leading to even higher correlation with subjective scores presented in this paper.

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Authors and Affiliations

  1. Department of Signal Processing and Multimedia Engineering, Szczecin Faculty of Electrical Engineering, West Pomeranian University of Technology, 26. Kwietnia 10, 71-126, Szczecin, Poland

    Krzysztof Okarma

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  1. Krzysztof Okarma
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Correspondence to Krzysztof Okarma .

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Editors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Radek Silhavy

  2. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Roman Senkerik

  3. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Zuzana Kominkova Oplatkova

  4. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Petr Silhavy

  5. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Zdenka Prokopova

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Okarma, K. (2016). Binary Image Quality Assessment—A Hybrid Approach Based on Binarization Evaluation Methods. In: Silhavy, R., Senkerik, R., Oplatkova, Z., Silhavy, P., Prokopova, Z. (eds) Artificial Intelligence Perspectives in Intelligent Systems. Advances in Intelligent Systems and Computing, vol 464. Springer, Cham. https://doi.org/10.1007/978-3-319-33625-1_24

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  • DOI: https://doi.org/10.1007/978-3-319-33625-1_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-33623-7

  • Online ISBN: 978-3-319-33625-1

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