多特征融合的图像质量评价方法

doi:10.16451/j.cnki.issn1003-6059.201907011

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Abstract

To avoid the difficulties in choosing and explaining the visual features and pooling strategies in image quality assessment, a reference images quality assessment method based on multi-feature fusion is proposed. Various underlying features of reference and distorted images are extracted,and a machine learning method is applied to predict the quality of real images. Firstly, phase congruency, gradient, visual saliency and contrast of reference and distorted images are extracted. Then similarity maps of four features are calculated, respectively. The mean and variance characteristics of these similarity maps are extracted. Finally, the assessment model is learned by support vector regression. The experimental results on four benchmark databases demonstrate a high coherence between subjective and objective assessment by the proposed method.

Key words： Low-level Feature Similarity Map Statistical Features Pooling Support Vector Machine

Received: 28 November 2018

ZTFLH:

TN 911.73

Fund:

Supported by National Natural Science Foundation of China(No.61762004), Science and Technology Project Founded by Education Department of Jiangxi Province(No.GJJ170455), Open Fund of Jiangxi Engineering Laboratory on Radioactive Geoscience and Big Data Technology(No.JELRGBDT201702), Ph.D. Research Startup Foundation of East China University of Te-chnology(No.DHBK2016119,DHBK2016120)

About author:: JIA Huizhen(Corresponding author), Ph.D., lecturer. Her research interests include image processing and pattern recognition.WANG Tonghan, Ph.D., lecturer. His research interests include image processing and pattern recognition.FU Peng, Ph.D., lecturer. His research interests include image processing and pattern recognition.

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	JIA Huizhen
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	FU Peng

Cite this article:

JIA Huizhen,WANG Tonghan,FU Peng. Multi-feature Fusion Based Image Quality Assessment Method[J]. , 2019, 32(7): 669-675.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201907011 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I7/669

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