Making image quality assessment robust

Abstract

We develop a robust framework for natural scene statistic (NSS) model based blind image quality assessment (IQA). The robustified IQA model utilizes a robust statistics approach based on L-moments. Such robust statistics based approaches are effective when natural or distorted images deviate from assumed statistical models, and achieves better prediction performance on distorted images relative to human subjective judgments. We also show how robustifying the model makes IQA approach resilient against deviation in model assumptions, small variations in the distortions and amount of data the model is trained on.