A color image quality assessment using a reduced-reference image machine learning expert
Christophe Charrier, Gilles Lebrun, Olivier Lézoray · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
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 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. Selected features are of two kinds: 1) full-reference features and 2) no-reference characteristics. That way, a machine learning expert, providing a final class number is designed.