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A Statistical Reduced-Reference Approach to Digital Image Quality Assessment

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Book cover Computer Vision and Graphics (ICCVG 2008)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5337))

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Abstract

In the paper a fast method of the digital image quality estimation is proposed. Our approach is based on the Monte Carlo method applied for some classical and modern full-reference image quality assessment methods, such as Structural Similarity and SVD-based measure. Obtained results are compared to the effects achieved using the full analysis techniques. Significant reduction of the number of analysed pixels or blocks leads to fast and efficient estimation of image quality especially in low performance systems where the processing speed is much more important than the accuracy of the quality assessment.

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Author information

Authors and Affiliations

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

    Krzysztof Okarma & Piotr Lech

Authors
  1. Krzysztof Okarma
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  2. Piotr Lech
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Editor information

Editors and Affiliations

  1. Polish-Japanese Institute of Information Technology, 86 Koszykowa St, 02-008, Warsaw, Poland

    Leonard Bolc

  2. Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Księcia Trojdena 4, 02-109, Warsaw, Poland

    Juliusz L. Kulikowski

  3. Polish-Japanese Institute of Information Technology, The Silesian University of Technology, Institute of Computer Science, 86 Koszykowa St, 02-008, Warsaw, Poland

    Konrad Wojciechowski

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Okarma, K., Lech, P. (2009). A Statistical Reduced-Reference Approach to Digital Image Quality Assessment. In: Bolc, L., Kulikowski, J.L., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2008. Lecture Notes in Computer Science, vol 5337. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02345-3_5

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  • DOI: https://doi.org/10.1007/978-3-642-02345-3_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02344-6

  • Online ISBN: 978-3-642-02345-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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