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
Deep Perceptual Preprocessing for Video Coding—0
Deep Quality: A Deep No-reference Quality Assessment System—0
A Novel Automatic Real-time Motion Tracking Method for Magnetic Resonance Imaging-guided Radiotherapy: Leveraging the Enhanced Tracking-Learning-Detection Framework with Automatic Segmentation—0
Domain Fingerprints for No-reference Image Quality Assessment—0
A Deep-Learning-Based Label-free No-Reference Image Quality Assessment Metric: Application in Sodium MRI Denoising—0
Evaluation of Rounding Functions in Nearest-Neighbor Interpolation—0
Exploring Vulnerabilities of No-Reference Image Quality Assessment Models: A Query-Based Black-Box Method—0
Beyond Score Changes: Adversarial Attack on No-Reference Image Quality Assessment from Two Perspectives—0
BIQ2021: A Large-Scale Blind Image Quality Assessment Database—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
Hallucinated-IQA: No-Reference Image Quality Assessment via Adversarial Learning—0
How Quality Affects Deep Neural Networks in Fine-Grained Image Classification—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
KonX: Cross-Resolution Image Quality Assessment—0
Language Integration in Fine-Tuning Multimodal Large Language Models for Image-Based Regression—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
Blind Document Image Quality Prediction Based on Modification of Quality Aware Clustering Method Integrating a Patch Selection Strategy—0
MSLIQA: Enhancing Learning Representations for Image Quality Assessment through Multi-Scale Learning—0
MSTRIQ: No Reference Image Quality Assessment Based on Swin Transformer with Multi-Stage Fusion—0
MUG: A Parameterless No-Reference JPEG Quality Evaluator Robust to Block Size and Misalignment—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