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 76–100 of 155 papers

TitleStatusHype
Can No-Reference Quality-Assessment Methods Serve as Perceptual Losses for Super-Resolution?—0
Opinion-Unaware Blind Image Quality Assessment using Multi-Scale Deep Feature StatisticsCode0
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
Quality-Aware Image-Text Alignment for Real-World Image Quality AssessmentCode0
When No-Reference Image Quality Models Meet MAP Estimation in Diffusion Latents—0
Black-box Adversarial Attacks Against Image Quality Assessment Models—0
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
Towards adversarial robustness verification of no-reference image-and video-quality metricsCode0
Adaptive Feature Selection for No-Reference Image Quality Assessment by Mitigating Semantic Noise Sensitivity—0
Less is More: Learning Reference Knowledge Using No-Reference Image Quality Assessment—0
You Only Train Once: A Unified Framework for Both Full-Reference and No-Reference Image Quality AssessmentCode0
Interpretable Image Quality Assessment via CLIP with Multiple Antonym-Prompt Pairs—0
Test Time Adaptation for Blind Image Quality AssessmentCode0
Regression-free Blind Image Quality Assessment with Content-Distortion Consistency—0
Blind Image Quality Assessment: A Fuzzy Neural Network for Opinion Score Distribution PredictionCode0
Collaborative Auto-encoding for Blind Image Quality AssessmentCode0
MAMIQA: No-Reference Image Quality Assessment Based on Multiscale Attention Mechanism With Natural Scene StatisticsCode0
Fast Adversarial CNN-based Perturbation Attack of No-Reference Image Quality MetricsCode0
Subjective and Objective Quality Assessment for in-the-Wild Computer Graphics ImagesCode0
JNDMix: JND-Based Data Augmentation for No-reference Image Quality Assessment—0
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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