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Blind Image Quality Assessment

To avoid duplication and fragmentation, use the No-Reference Image Quality Assessment (NR-IQA) task.

Papers

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Showing 26–46 of 46 papers

TitleStatusHype
Spatial Moment Pooling Improves Neural Image Assessment—0
Source-free Unsupervised Domain Adaptation for Blind Image Quality Assessment—0
GreenBIQA: A Lightweight Blind Image Quality Assessment Method—0
Deep Neural Network for Blind Visual Quality Assessment of 4K Content—0
Deep Decomposition and Bilinear Pooling Network for Blind Night-Time Image Quality Evaluation—0
BIQ2021: A Large-Scale Blind Image Quality Assessment Database—0
Deep Neural Networks for Blind Image Quality Assessment: Addressing the Data Challenge—0
Blind Image Quality Assessment for MRI with A Deep Three-dimensional content-adaptive Hyper-Network—0
Troubleshooting Blind Image Quality Models in the Wild—0
Active Fine-Tuning from gMAD Examples Improves Blind Image Quality Assessment—0
Subjective and Objective De-raining Quality Assessment Towards Authentic Rain Image—0
Coupled Learning for Facial Deblur—0
dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs—0
Blind Predicting Similar Quality Map for Image Quality Assessment—0
On the Use of Deep Learning for Blind Image Quality Assessment—0
Massive Online Crowdsourced Study of Subjective and Objective Picture Quality—0
A proposal project for a blind image quality assessment by learning distortions from the full reference image quality assessments—0
Beyond Human Opinion Scores: Blind Image Quality Assessment based on Synthetic Scores—0
Blind Image Quality Assessment using Semi-supervised Rectifier Networks—0
Learning to Rank for Blind Image Quality Assessment—0
Learning without Human Scores for Blind Image Quality Assessment—0
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