A color image quality assessment using a reduced-reference image machine learning expert

Christophe Charrier; Gilles Lebrun; Olivier Lezoray

doi:10.1117/12.766473

28 January 2008 A color image quality assessment using a reduced-reference image machine learning expert

Christophe Charrier, Gilles Lebrun, Olivier Lezoray

Proceedings Volume 6808, Image Quality and System Performance V; 68080U (2008) https://doi.org/10.1117/12.766473
Event: Electronic Imaging, 2008, San Jose, California, United States

Abstract

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.

Citation Download Citation

Christophe Charrier, Gilles Lebrun, and Olivier Lezoray "A color image quality assessment using a reduced-reference image machine learning expert", Proc. SPIE 6808, Image Quality and System Performance V, 68080U (28 January 2008); https://doi.org/10.1117/12.766473

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