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A hybrid learning-based framework for blind image quality assessment

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Abstract

Blind image quality assessment aims to evaluate the quality of a given image without the availability of the original ground truth image and without the prior knowledge of the types of distortions present. Instead of using hand-crafted features to address specific type of image distortions, a hybrid learning-based blind image quality assessment approach is proposed in this paper to address more challenging mixed types of image distortions. The proposed approach integrates the convolution neural network (CNN) as a feature extractor, plus the support vector regression method to learn a mapping function from the CNN-trained features to the quality score of the input image. Extensive experiments are conducted using both standard image dataset and real-world surveillance video dataset to demonstrate the superior performance of the proposed approach.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (No. 61375017), and the innovation foundation of Wuhan University of Science and Technology graduate student (JCX2015010).

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Authors and Affiliations

  1. School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430081, China

    Meiyin Wu, Li Chen & Jing Tian

  2. Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan University of Science and Technology, Wuhan, 430081, China

    Meiyin Wu, Li Chen & Jing Tian

Authors
  1. Meiyin Wu
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  2. Li Chen
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  3. Jing Tian
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Correspondence to Jing Tian.

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Wu, M., Chen, L. & Tian, J. A hybrid learning-based framework for blind image quality assessment. Multidim Syst Sign Process 29, 839–849 (2018). https://doi.org/10.1007/s11045-017-0475-y

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