A crucial step in image compression is the evaluation of its performance, and more precisely available ways to measure the quality of compressed images. In this paper, a machine learning expert, providing a quality score is proposed. This quality measure is based on a learned classification process in order to respect that of human observers. The proposed method namely Machine Learning-based Image Quality Measurment (MLIQM) first classifies the quality using multi Support Vector Machine (SVM) classification according to the quality scale recommended by the ITU. This quality scale contains 5 ranks ordered from 1 (the worst quality) to 5 (the best quality). To evaluate the quality of images, a feature vector containing visual attributes describing images content is constructed. Then, a classification process is performed to provide the final quality class of the considered image. Finally, once a quality class is associated to the considered image, a specific SVM regression is performed to score its quality. Obtained results are compared to the one obtained applying classical Full-Reference Image Quality Assessment (FRIQA) algorithms to judge the efficiency of the proposed method.
Christophe Charrier, Olivier Lézoray, Gilles Lebrun, "A Machine Learning Regression scheme to design a FR-Image Quality Assessment Algorithm" in Proc. IS&T CGIV 2012 6th European Conf. on Colour in Graphics, Imaging, and Vision, 2012, pp 35 - 42, https://doi.org/10.2352/CGIV.2012.6.1.art00007