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NR-IQA

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

Papers

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Showing 1–25 of 50 papers

TitleStatusHype
VisualQuality-R1: Reasoning-Induced Image Quality Assessment via Reinforcement Learning to RankCode2
AIM 2024 Challenge on UHD Blind Photo Quality AssessmentCode1
Exploring Semantic Feature Discrimination for Perceptual Image Super-Resolution and Opinion-Unaware No-Reference Image Quality AssessmentCode1
Diffusion Model Based Visual Compensation Guidance and Visual Difference Analysis for No-Reference Image Quality AssessmentCode1
DGIQA: Depth-guided Feature Attention and Refinement for Generalizable Image Quality AssessmentCode0
Backdoor Attacks against No-Reference Image Quality Assessment Models via a Scalable TriggerCode0
Attention Down-Sampling Transformer, Relative Ranking and Self-Consistency for Blind Image Quality AssessmentCode0
UHD-IQA Benchmark Database: Pushing the Boundaries of Blind Photo Quality AssessmentCode0
When No-Reference Image Quality Models Meet MAP Estimation in Diffusion Latents—0
Comprehensive evaluation of no-reference image quality assessment algorithms on authentic distortions—0
Cross-Domain Underwater Image Enhancement Guided by No-Reference Image Quality Assessment: A Transfer Learning Approach—0
Cross-IQA: Unsupervised Learning for Image Quality Assessment—0
Deep Optimization model for Screen Content Image Quality Assessment using Neural Networks—0
Domain Fingerprints for No-reference Image Quality Assessment—0
Exploring Vulnerabilities of No-Reference Image Quality Assessment Models: A Query-Based Black-Box Method—0
GenzIQA: Generalized Image Quality Assessment using Prompt-Guided Latent Diffusion Models—0
Hallucinated-IQA: No-Reference Image Quality Assessment via Adversarial Learning—0
Interpretable Image Quality Assessment via CLIP with Multiple Antonym-Prompt Pairs—0
IQPFR: An Image Quality Prior for Blind Face Restoration and Beyond—0
JNDMix: JND-Based Data Augmentation for No-reference Image Quality Assessment—0
Learning a No-Reference Quality Assessment Model of Enhanced Images With Big Data—0
Less is More: Learning Reference Knowledge Using No-Reference Image Quality Assessment—0
Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions—0
Local Manifold Learning for No-Reference Image Quality Assessment—0
MSLIQA: Enhancing Learning Representations for Image Quality Assessment through Multi-Scale Learning—0
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