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.
CITATION STYLE
Redi, J., Gastaldo, P., Zunino, R., & Heynderickx, I. (2008). Co-occurrence matrixes for the quality assessment of coded images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5163 LNCS, pp. 897–906). https://doi.org/10.1007/978-3-540-87536-9_92
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