Abstract
Conference Title: 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC) Conference Start Date: 2017, Jan. 9 Conference End Date: 2017, Jan. 11 Conference Location: Las Vegas, NV, USA We develop an efficient general-purpose no-reference (NR) image quality assessment (IQA) model that based on a hypothesis that an effective combination of image features can be used to develop NR-IQA approaches having competitive performance with the state-of-the-art. First, we design a collection of features, then evaluate the usefulness of each feature on different kinds of distortions using different features evaluation techniques. Therefore, we came up with a set of optimal features for each learning model in the framework. Our experimental results show that our approach outperforms state- of-the-art blind image quality prediction models.