Incorporating Semi-Supervised and Positive-Unlabeled Learning for Boosting Full Reference Image Quality Assessment
Author:
Affiliation:
1. School of Computer Science and Technology, Harbin Institute of Technology,China
2. College of Artificial Intelligence, Dalian Maritime University,China
Funder
National Key RD Program of China
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9878378/9878366/09879625.pdf?arnumber=9879625
Reference75 articles.
1. The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
2. FSIM: A Feature Similarity Index for Image Quality Assessment
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4. VSI: A Visual Saliency-Induced Index for Perceptual Image Quality Assessment
5. Blind Image Quality Assessment Using a Deep Bilinear Convolutional Neural Network
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