Statistical modeling of image degradation based on quality metrics

A plethora of Image Quality Metrics (IQM) has been proposed during the last two decades. However, at present time, there is no accepted IQM able to predict the perceptual level of image degradation across different types of visual distortions. Some measures are more adapted for a set of degradations but inefficient for others. Indeed, the efficiency of any IQM has been shown to depend upon the type of degradation. Thus, we propose here a new approach for predicting the type of degradation before using IQMs. The basic idea is first to identify the type of distortion using a Bayesian approach, then select the most appropriate IQM for estimating image quality for that specific type of distortion. The performance of the proposed method is evaluated in terms of classification accuracy across different types of degradations.