Learning quality-aware filters for no-reference image quality assessment

With the rapid development of the usage of digital imaging and communication technologies, there appears to be a great demand for fast and practical approaches for image quality assessment (IQA) algorithms that can match human judgements. In this paper, we propose a novel general-purpose no-reference IQA (NR-IQA) framework by means of learning quality-aware filters (QAF). Using these filters for image encoding, we can obtain effective image representations for quality estimation. Additionally, random forest is used to learn the mapping from feature space to human subjective scores. Extensive experiments conducted on LIVE and CSIQ databases demonstrate that the proposed NR-IQA metric QAF can achieve better prediction performance than all the other state-of-the-art NR-IQA approaches in terms of both prediction accuracy and generalization capabilities.