Special Section on Perceptually Driven Visual Information Analysis

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

[+] Author Affiliations
Dakkar Borhen Eddine, Hachouf Fella

Université des Frères Mentouri, Département d'Electronique, Faculté des Sciences de la Technologie, Laboratoire d’Automatique et de Robotique, Campus A. Hamani, route Ain El Bey, Constantine 25000, Algérie

Beghdadi Azeddine

Université Paris 13-Sorbonne Paris Cité, Institut Galilée, Laboratoire de Traitement et Transport de l’Information, 99 Avenue Jean Baptiste Clément, Villetaneuse 93430, France

J. Electron. Imaging. 25(6), 061623 (Dec 20, 2016). doi:10.1117/1.JEI.25.6.061623
History: Received April 20, 2016; Accepted November 29, 2016
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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.

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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", J. Electron. Imaging. 25(6), 061623 (Dec 20, 2016). ; http://dx.doi.org/10.1117/1.JEI.25.6.061623


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