Blind image quality assessment method based on a particle swarm optimization support vector regression fusion scheme

Abstract

Quantifying image quality without reference is still a challenging problem, especially when different distortions affect the observed image. A no-reference image quality assessment (NR-IQA) metric is proposed. It is based on a fusion scheme of multiple distortion measures. This metric is built in two stages. First, a set of relevant IQA metrics is selected using a particle swarm optimization scheme. Then, a support vector regression (SVR)-based fusion strategy is adopted to derive the overall index of image quality. The obtained results demonstrate clearly that the proposed approach outperforms the state-of-the-art NR-IQA methods. Furthermore, the proposed approach is flexible and could be extended to other distortions.