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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