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

Dakkar Borhen Eddine; Hachouf Fella; Beghdadi Azeddine

doi:10.1117/1.JEI.25.6.061623

20 December 2016 Blind image quality assessment method based on a particle swarm optimization support vector regression fusion scheme

Dakkar Borhen Eddine, Hachouf Fella, Beghdadi Azeddine

Author Affiliations +

Journal of Electronic Imaging, Vol. 25, Issue 6, 061623 (December 2016). https://doi.org/10.1117/1.JEI.25.6.061623

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.

Citation Download Citation

Dakkar Borhen Eddine, Hachouf Fella, and 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

Received: 20 April 2016; Accepted: 29 November 2016; Published: 20 December 2016

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