Training Quality-Aware Filters for No-Reference Image Quality Assessment

With the rapid increase of digital imaging and communication technology usage, there´s now great demand for fast and practical image quality assessment (IQA) algorithms that can predict an image´s quality as consistently as humans. The authors propose a general-purpose, no-reference image quality assessment (NR-IQA) with the goal of developing a model that does not require prior knowledge about nondistorted reference images and the types of distortions. The key is to obtain effective image representations using learning quality-aware filters (QAFs). Unlike other regression models, they also use a random forest to train the mapping from the feature space. Extensive experiments conducted on the LIVE and CSIQ datasets demonstrate that the proposed NR-IQA metric QAF can achieve better prediction performance than the other state-of-the-art approaches in terms of both prediction accuracy and generalization capability.