Co-occurrence matrixes for the quality assessment of coded images

J.A. Redi, P. Gastaldo, R. Zunino, I.E.J. Heynderickx

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    3 Citations (Scopus)


    Intrinsic nonlinearity complicates the modeling of perceived quality of digital images, especially when using feature-based objective methods. The research described in this paper indicates that models from Computational Intelligence can predict quality and cope with multi-dimensional data characterized by complex perceptual relationships. A reduced-reference scheme exploits Support Vector Machines (SVMs) to assess the degradation in perceived image quality induced by three different distortion types: JPEG compression, white noise, and Gaussian blur. First, an objective description of the images is obtained by exploiting the co-occurrence matrix and its features; then, the SVM supports the nonlinear mapping between the objective description and the quality evaluation. Experimental results confirm the validity of the approach. © Springer-Verlag Berlin Heidelberg 2008.
    Original languageEnglish
    Title of host publication18th International Conference on Artificial Neural Networks, ICANN 2008, 3-6 September 2008, Prague
    Place of PublicationPrague
    Publication statusPublished - 2008

    Publication series

    NameLecture Notes in Computer Science
    ISSN (Print)0302-9743


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