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This paper uses robust statistics and curvelet transform to learn ageneral-purpose no-reference (NR) image quality assessment (IQA) model. The newapproach, here called M1, competes with the Curvelet Quality Assessmentproposed in 2014 (Curvelet2014). The central idea is to use descriptors basedon robust statistics to extract features and predict the human opinion aboutdegraded images. To show the consistency of the method the model is tested with3 different datasets, LIVE IQA, TID2013 and CSIQ. To test evaluation, it isused the Wilcoxon test to verify the statistical significance of results andpromote an accurate comparison between new model M1 and Curvelet2014. Theresults show a gain when robust statistics are used as descriptor.

Robust statistics and no-reference image quality assessment in Curvelet domain