Naturalization Module in Neural Networks for Screen Content Image Quality Assessment

Deep learning approaches have demonstrated success in no-reference image quality assessment tasks. However, due to the specific properties of screen content images (SCIs), deep neural networks for SCI quality assessment are not as optimal as those designed for images depicting natural scenes. In ord...

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Bibliographic Details
Published inIEEE signal processing letters Vol. 25; no. 11; pp. 1685 - 1689
Main Authors Chen, Jianan, Shen, Liquan, Zheng, Linru, Jiang, Xuhao
Format Journal Article
LanguageEnglish
Published New York IEEE 01.11.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Artificial neural networks
Convolution
Deep learning
Distortion
image processing
Image quality
image quality assessment
Interpolation
Machine learning
Measurement
naturalization
Neural networks
Quality
Quality assessment
screen content images
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Summary:Deep learning approaches have demonstrated success in no-reference image quality assessment tasks. However, due to the specific properties of screen content images (SCIs), deep neural networks for SCI quality assessment are not as optimal as those designed for images depicting natural scenes. In order to tackle this discrepancy, a "naturalization" module composed of an upsampling layer and a convolutional layer is proposed to transform SCIs to have characteristics more similar to that of natural images. In addition, a new deep learning model architecture along with data augmentation techniques tailored to SCIs are implemented. The performance of the proposed approach is evaluated on the Screen Image Quality Assessment Database and Screen Content Image Database and has shown to have superior performance to state-of-the-art methods in predicting the perceptual quality of SCIs.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2018.2871250