Co-occurrence Matrixes for the Quality Assessment of Coded Images
Identifieur interne : 000490 ( Istex/Checkpoint ); précédent : 000489; suivant : 000491Co-occurrence Matrixes for the Quality Assessment of Coded Images
Auteurs : Judith Redi [Italie] ; Paolo Gastaldo [Italie] ; Rodolfo Zunino [Italie] ; Ingrid HeynderickxSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2008.
Abstract
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.
Url:
DOI: 10.1007/978-3-540-87536-9_92
Affiliations:
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ISTEX:792F63D6A04589F6262C19195EC1AB653BB44DF4Le document en format XML
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<front><div type="abstract" xml:lang="en">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.</div>
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