Blind Image Quality Assessment Using a Deep Bilinear Convolutional Neural Network

We propose a deep bilinear model for blind image quality assessment that works for both synthetically and authentically distorted images. Our model constitutes two streams of deep convolutional neural networks (CNNs), specializing in two distortion scenarios separately. For synthetic distortions, we...

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Veröffentlicht in:	IEEE transactions on circuits and systems for video technology Jg. 30; H. 1; S. 36 - 47
Hauptverfasser:	Zhang, Weixia, Ma, Kede, Yan, Jia, Deng, Dexiang, Wang, Zhou
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.01.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Artificial neural networks bilinear pooling Blind image quality assessment Convolutional neural networks Degradation Distortion gMAD competition Ground truth Image classification Image coding Image quality Neural networks Nonlinear distortion perceptual image processing Quality assessment
ISSN:	1051-8215, 1558-2205
Online-Zugang:	Volltext
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