No-reference image quality assessment based on hybrid model

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The aim of research on the no-reference image quality assessment problem is to design models that can predict the quality of distorted images consistently with human visual perception. Due to the little prior knowledge of the images, it is still a difficult problem. This paper proposes a computational algorithm based on hybrid model to automatically extract vision perception features from raw image patches. Convolutional neural network (CNN) and support vector regression (SVR) are combined for this purpose. In the hybrid model, the CNN is trained as an efficient feature extractor, and the SVR performs as the regression operator. Extensive experiments demonstrate very competitive quality prediction performance of the proposed method.

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This work has been supported by Natural Science Foundation of China (No. 61501334).

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Correspondence to Jia Yan.

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Li, J., Yan, J., Deng, D. et al. No-reference image quality assessment based on hybrid model. SIViP 11, 985–992 (2017) doi:10.1007/s11760-016-1048-5

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  • No-reference image quality assessment
  • Convolutional neural network
  • Support vector regression
  • Hybrid model
  • Machine learning