No Reference Opinion Unaware Quality Assessment of Authentically Distorted Images
Author:
Affiliation:
1. Indian Institute of Science,Bengaluru,India
2. Mercedes-Benz Research and Development,India
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10030081/10030084/10030129.pdf?arnumber=10030129
Reference37 articles.
1. Deep Convolutional Neural Models for Picture-Quality Prediction: Challenges and Solutions to Data-Driven Image Quality Assessment
2. A Feature-Enriched Completely Blind Image Quality Evaluator
3. Fully Deep Blind Image Quality Predictor
4. From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality
5. RankIQA: Learning from Rankings for No-Reference Image Quality Assessment
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1. Deep Blind Image Quality Assessment Using Dynamic Neural Model With Dual-Order Statistics;IEEE Transactions on Circuits and Systems for Video Technology;2024-07
2. Opinion Unaware Image Quality Assessment via Adversarial Convolutional Variational Autoencoder;2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV);2024-01-03
3. Learning Generalizable Perceptual Representations for Data-Efficient No-Reference Image Quality Assessment;2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV);2024-01-03
4. CDINet: Content Distortion Interaction Network for Blind Image Quality Assessment;IEEE Transactions on Multimedia;2024
5. Refining the Unseen: Self-supervised Two-stream Feature Extraction for Image Quality Assessment;2023 IEEE International Conference on Data Mining (ICDM);2023-12-01
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