Co-occurrence Matrixes for the Quality Assessment of Coded Images (original) (raw)

Abstract

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.

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Authors and Affiliations

  1. Dept. of Biophysical and Electronic Engineering (DIBE), Genoa University, Via Opera Pia 11a, 16145, Genoa, Italy
    Judith Redi, Paolo Gastaldo & Rodolfo Zunino
  2. Philips Research Laboratories Prof. Holstlaan 4 - 5656 AA Eindhoven - NL and Delft Technical University, Mekelweg 4, 2628 CD, Delft, NL
    Ingrid Heynderickx

Authors

  1. Judith Redi
  2. Paolo Gastaldo
  3. Rodolfo Zunino
  4. Ingrid Heynderickx

Editor information

Véra Kůrková Roman Neruda Jan Koutník

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Redi, J., Gastaldo, P., Zunino, R., Heynderickx, I. (2008). Co-occurrence Matrixes for the Quality Assessment of Coded Images. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87536-9\_92

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