20 December 2016 Blind image quality assessment method based on a particle swarm optimization support vector regression fusion scheme
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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.
© 2016 SPIE and IS&T
Dakkar Borhen Eddine, Dakkar Borhen Eddine, Hachouf Fella, Hachouf Fella, Beghdadi Azeddine, Beghdadi Azeddine, } "Blind image quality assessment method based on a particle swarm optimization support vector regression fusion scheme," Journal of Electronic Imaging 25(6), 061623 (20 December 2016). https://doi.org/10.1117/1.JEI.25.6.061623 . Submission: Received: 20 April 2016; Accepted: 29 November 2016
Received: 20 April 2016; Accepted: 29 November 2016; Published: 20 December 2016
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