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http://dx.doi.org/10.5909/JBE.2016.21.3.380

No-Reference Image Quality Assessment Using Complex Characteristics of Shearlet Transform  

Mahmoudpour, Saeed (Department of Computer & Communications Engineering, Kangwon National University)
Kim, Manbae (Department of Computer & Communications Engineering, Kangwon National University)
Publication Information
Journal of Broadcast Engineering / v.21, no.3, 2016 , pp. 380-390 More about this Journal
Abstract
The field of Image Quality Measure (IQM) is growing rapidly in recent years. In particular, there was a significant progress in No-Reference (NR) IQM methods. In this paper, a general-purpose NR IQM algorithm is proposed based on the statistical characteristics of natural images in shearlet domain. The method utilizes a set of distortion-sensitive features extracted from statistical properties of shearlet coefficients. A complex version of the shearlet transform is employed to take advantage of phase and amplitude features in quality estimation. Furthermore, since shearlet transform can analyze the images at multiple scales, the effect of distortion on across-scale dependencies of shearlet coefficients is explored for feature extraction. For quality prediction, the features are used to train image classification and quality prediction models using a Support Vector Machine (SVM). The experimental results show that the proposed NR IQM is highly correlated with human subjective assessment and outperforms several Full-Reference (FR) and state-of-art NR IQMs.
Keywords
Image quality measure; no-reference; complex shearlet transform; SVM;
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