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Co-occurrence Matrixes for the Quality Assessment of Coded Images

Identifieur interne : 001470 ( Main/Exploration ); précédent : 001469; suivant : 001471

Co-occurrence Matrixes for the Quality Assessment of Coded Images

Auteurs : Judith Redi [Italie] ; Paolo Gastaldo [Italie] ; Rodolfo Zunino [Italie] ; Ingrid Heynderickx

Source :

RBID : ISTEX:792F63D6A04589F6262C19195EC1AB653BB44DF4

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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Le document en format XML

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