A training-based no-reference image quality assessment algorithm

We present a new image quality assessment algorithm that does not rely on reference images. Our general framework is to emulate human quality assessment by first detecting visual components, then assessing quality against an empirical model. We describe an instance of this framework where visual component detection is realized as a face detection method, and quality modeling is realized using radial basis function (RBF) networks. Experiments with this prototype system yielded promising results.