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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
Deep Neural Network for Blind Visual Quality Assessment of 4K Content—0
Deep Neural Networks for Blind Image Quality Assessment: Addressing the Data Challenge—0
Deep Shape-Texture Statistics for Completely Blind Image Quality Evaluation—0
dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs—0
Distilling Spatially-Heterogeneous Distortion Perception for Blind Image Quality Assessment—0
ExIQA: Explainable Image Quality Assessment Using Distortion Attributes—0
Feature Denoising Diffusion Model for Blind Image Quality Assessment—0
GreenBIQA: A Lightweight Blind Image Quality Assessment Method—0
GSBIQA: Green Saliency-guided Blind Image Quality Assessment Method—0
Learning to Rank for Blind Image Quality Assessment—0
Learning without Human Scores for Blind Image Quality Assessment—0
Lightweight High-Performance Blind Image Quality Assessment—0
Massive Online Crowdsourced Study of Subjective and Objective Picture Quality—0
On the Use of Deep Learning for Blind Image Quality Assessment—0
Quality-aware Pre-trained Models for Blind Image Quality Assessment—0
Bridging the Synthetic-to-Authentic Gap: Distortion-Guided Unsupervised Domain Adaptation for Blind Image Quality AssessmentCode0
FreqAlign: Excavating Perception-oriented Transferability for Blind Image Quality Assessment from A Frequency PerspectiveCode0
No-Reference Image Quality Assessment with Global-Local Progressive Integration and Semantic-Aligned Quality TransferCode0
UHD-IQA Benchmark Database: Pushing the Boundaries of Blind Photo Quality AssessmentCode0
DDR: Exploiting Deep Degradation Response as Flexible Image DescriptorCode0
Attention Down-Sampling Transformer, Relative Ranking and Self-Consistency for Blind Image Quality AssessmentCode0
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