Perceptual image quality assessment based on Bayesian networks

The paper addresses the issue of perceptual image quality assessment. By using Bayesian networks, we propose a Bayesian composed quality measure (B-CQM). This metric can assess quality in images degraded by combined noise injection and frequency distortion. It presents some advantages with respect to the original CQM approach, such as upholding the stochastic nature of the subjective quality assessment and easier inclusion of the effect of new experimental data in the metric model by just updating its probability tables. Some examples are provided in order to verify the behavior of the proposed metric.