SOTAVerified

No-Reference Image Quality Assessment

An Image Quality Assessment approach where no reference image information is available to the model. Sometimes referred to as Blind Image Quality Assessment (BIQA).

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 26–50 of 155 papers

TitleStatusHype
Can No-Reference Quality-Assessment Methods Serve as Perceptual Losses for Super-Resolution?—0
DP-IQA: Utilizing Diffusion Prior for Blind Image Quality Assessment in the WildCode1
Opinion-Unaware Blind Image Quality Assessment using Multi-Scale Deep Feature StatisticsCode0
Adaptive Image Quality Assessment via Teaching Large Multimodal Model to CompareCode1
How Quality Affects Deep Neural Networks in Fine-Grained Image Classification—0
Cross-IQA: Unsupervised Learning for Image Quality Assessment—0
Image Quality Assessment With Compressed Sampling—0
Multi-Modal Prompt Learning on Blind Image Quality AssessmentCode0
Beyond Score Changes: Adversarial Attack on No-Reference Image Quality Assessment from Two Perspectives—0
Defense Against Adversarial Attacks on No-Reference Image Quality Models with Gradient Norm RegularizationCode1
Quality-Aware Image-Text Alignment for Real-World Image Quality AssessmentCode0
Pairwise Comparisons Are All You NeedCode2
When No-Reference Image Quality Models Meet MAP Estimation in Diffusion Latents—0
PromptIQA: Boosting the Performance and Generalization for No-Reference Image Quality Assessment via PromptsCode1
Black-box Adversarial Attacks Against Image Quality Assessment Models—0
Diffusion Model Based Visual Compensation Guidance and Visual Difference Analysis for No-Reference Image Quality AssessmentCode1
High Resolution Image Quality DatabaseCode1
GMC-IQA: Exploiting Global-correlation and Mean-opinion Consistency for No-reference Image Quality Assessment—0
Exploring Vulnerabilities of No-Reference Image Quality Assessment Models: A Query-Based Black-Box Method—0
Blind Image Quality Assessment Based on Geometric Order LearningCode1
Towards adversarial robustness verification of no-reference image-and video-quality metricsCode0
Transformer-based No-Reference Image Quality Assessment via Supervised Contrastive LearningCode1
Adaptive Feature Selection for No-Reference Image Quality Assessment by Mitigating Semantic Noise Sensitivity—0
UIEDP:Underwater Image Enhancement with Diffusion PriorCode1
Learning Generalizable Perceptual Representations for Data-Efficient No-Reference Image Quality AssessmentCode1
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All datasetsKADID-10kCSIQTID2013UHD-IQAKonIQ-10kLIVESPAQ

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1RvTC (image-only)SRCC0.98—Unverified
2UNIQASRCC0.94—Unverified
3CONTRIQUESRCC0.93—Unverified
4ARNIQASRCC0.91—Unverified
5Re-IQASRCC0.87—Unverified
6TReSSRCC0.86—Unverified
7HyperIQASRCC0.85—Unverified
8DB-CNNSRCC0.85—Unverified
9BRISQUESRCC0.53—Unverified
#ModelMetricClaimedVerifiedStatus
1UNIQASRCC0.96—Unverified
2ARNIQASRCC0.96—Unverified
3Re-IQASRCC0.95—Unverified
4DB-CNNSRCC0.95—Unverified
5CONTRIQUESRCC0.94—Unverified
6HyperIQASRCC0.92—Unverified
7TReSSRCC0.92—Unverified
8BRISQUESRCC0.75—Unverified
#ModelMetricClaimedVerifiedStatus
1UNIQASRCC0.95—Unverified
2ARNIQASRCC0.88—Unverified
3TReSSRCC0.86—Unverified
4CONTRIQUESRCC0.84—Unverified
5HyperIQASRCC0.84—Unverified
6DB-CNNSRCC0.82—Unverified
7Re-IQASRCC0.8—Unverified
8BRISQUESRCC0.6—Unverified
#ModelMetricClaimedVerifiedStatus
1LAR-IQA (KAN head)SRCC0.84—Unverified
2QualiCLIPSRCC0.77—Unverified
3CLIP-IQA+SRCC0.75—Unverified
4ARNIQASRCC0.74—Unverified
5CONTRIQUESRCC0.73—Unverified
6Effnet-2C-MLSPSRCC0.68—Unverified
7HyperIQASRCC0.55—Unverified
#ModelMetricClaimedVerifiedStatus
1RvTC (image-only)PLCC0.95—Unverified
#ModelMetricClaimedVerifiedStatus
1UNIQASRCC0.99—Unverified
#ModelMetricClaimedVerifiedStatus
1RvTC (image-only)PLCC0.93—Unverified