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Image quality assessment based on multiscale fuzzy gradient similarity deviation

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Abstract

Although a great number of objective methods for assessing perceptual image quality have been proposed, their performances on different distortion types are still unsatisfactory. It is a great challenge for image quality assessment (IQA) that distorted images may have similar visual perception even if their distortion types and magnitudes are totally different, because some structural changes caused by distortions are visually imperceptible. In this paper, we propose a novel full-reference IQA (FR-IQA) scheme based on multiscale fuzzy gradient similarity deviation (MFGSD), where fuzzy inference system is introduced to reduce the negative impact of imperceptible distortions, and the standard deviation of fuzzy gradient similarity is utilized to measure their quality distinction. Extensive experiments are conducted on two publicly available image databases, and compared with many existing state-of-art FR-IQA schemes, and the proposed MFGSD has a better performance on different distortion types and strengths.

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Acknowledgments

The work in this paper was supported by the Fundamental Research Funds for the Central Universities (106112015CDJXY180003), the Program for New Century Excellent Talents in University (NCET-12-0589), and the Natural Science Foundation Project of CQ CSTC (cstc2012jjA40017 and cstc2013jcyjA40017).

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Authors and Affiliations

  1. College of Computer Science, Chongqing University, Chongqing, 400044, China

    Shangwei Guo, Tao Xiang & Xiaoguo Li

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  1. Shangwei Guo
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  2. Tao Xiang
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  3. Xiaoguo Li
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Correspondence to Tao Xiang.

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Communicated by V. Loia.

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Guo, S., Xiang, T. & Li, X. Image quality assessment based on multiscale fuzzy gradient similarity deviation. Soft Comput 21, 1145–1155 (2017). https://doi.org/10.1007/s00500-015-1844-9

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