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Volume: 3 | Article ID: art00050
A Machine Learning-based Color Image Quality Metric
  DOI :  10.2352/CGIV.2006.3.1.art00050  Published OnlineJanuary 2006

A quality metric based on a classification process is introduced. The main idea of the proposed method is to avoid the error pooling step of many factors (in frequential and spatial domain) commonly applied to obtain a final quality score. A classification process based on the Support Vector Machine method is designed to obtain the final quality class with respect to the standard quality scale provided by the UIT. Thus, for each degraded color image, a feature vector is computed including several Human Visual System characteristics, such as, contrast masking effect, color correlation, and so on. In that way, a machine learning expert, providing a final class number is designed.

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Christophe Charrier, Gilles Lebrun, Olivier Lezoray, "A Machine Learning-based Color Image Quality Metricin Proc. IS&T CGIV 2006 3rd European Conf. on Colour in Graphics, Imaging, and Vision,  2006,  pp 251 - 256,

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