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    ARTICLE

    Towards No-Reference Image Quality Assessment Based on Multi-Scale Convolutional Neural Network

    Yao Ma1, Xibiao Cai1, *, Fuming Sun2

    CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.1, pp. 201-216, 2020, DOI:10.32604/cmes.2020.07867

    Abstract Image quality assessment has become increasingly important in image quality monitoring and reliability assuring of image processing systems. Most of the existing no-reference image quality assessment methods mainly exploit the global information of image while ignoring vital local information. Actually, the introduced distortion depends on a slight difference in details between the distorted image and the non-distorted reference image. In light of this, we propose a no-reference image quality assessment method based on a multi-scale convolutional neural network, which integrates both global information and local information of an image. We first adopt the image pyramid method to generate four scale… More >

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