Abstract
To match human perception, extracting perceptual features effectively plays an important role in image quality assessment. In contrast to most existing methods that use linear transformations or models to represent images, we employ a complex mathematical expression of high dimensionality to reveal the statistical characteristics of the images. Furthermore, by introducing kernel methods to transform the linear problem into a nonlinear one, a full-reference image quality assessment method is proposed based on high-dimensional nonlinear feature extraction. Experiments on the LIVE, TID2008, and CSIQ databases demonstrate that nonlinear features offer competitive performance for image inherent quality representation and the proposed method achieves a promising performance that is consistent with human subjective evaluation.
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Project supported by the National High-Tech R&D Program (863) of China (No. 2015AA016704c), the National Science & Technology Support Program of China (No. 2013BAH03B01), and the Zhejiang Provincial Natural Science Foundation of China (No. LY14F020028)
ORCID: Yong DING, http://orcid.org/0000-0002-5226-7511
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Ding, Y., Li, N., Zhao, Y. et al. Image quality assessment method based on nonlinear feature extraction in kernel space. Frontiers Inf Technol Electronic Eng 17, 1008–1017 (2016). https://doi.org/10.1631/FITEE.1500439
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DOI: https://doi.org/10.1631/FITEE.1500439
Keywords
- Image quality assessment
- Full-reference method
- Feature extraction
- Kernel space
- Support vector regression