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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

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Showing 51–100 of 155 papers

TitleStatusHype
AutoDIR: Automatic All-in-One Image Restoration with Latent DiffusionCode1
Exploring Semantic Feature Discrimination for Perceptual Image Super-Resolution and Opinion-Unaware No-Reference Image Quality AssessmentCode1
A CNN-Based Blind Denoising Method for Endoscopic ImagesCode1
Self2Self+: Single-Image Denoising with Self-Supervised Learning and Image Quality Assessment LossCode1
Point Cloud Quality Assessment: Dataset Construction and Learning-based No-Reference MetricCode1
ATTIQA: Generalizable Image Quality Feature Extractor using Attribute-aware Pretraining—0
When No-Reference Image Quality Models Meet MAP Estimation in Diffusion Latents—0
Painting Outside as Inside: Edge Guided Image Outpainting via Bidirectional Rearrangement with Progressive Step Learning—0
Perceptual Image Restoration with High-Quality Priori and Degradation Learning—0
Pixel-by-pixel Mean Opinion Score (pMOS) for No-Reference Image Quality Assessment—0
Comprehensive evaluation of no-reference image quality assessment algorithms on KADID-10k database—0
Comprehensive evaluation of no-reference image quality assessment algorithms on authentic distortions—0
RAN4IQA: Restorative Adversarial Nets for No-Reference Image Quality Assessment—0
Rank-based No-reference Quality Assessment for Face Swapping—0
Real-Time No-Reference Image Quality Assessment Based on Filter Learning—0
Regression-free Blind Image Quality Assessment with Content-Distortion Consistency—0
An Attention-Driven Approach of No-Reference Image Quality Assessment—0
Convolutional Neural Networks for No-Reference Image Quality Assessment—0
Image Quality Assessment With Compressed Sampling—0
SOM: Semantic Obviousness Metric for Image Quality Assessment—0
Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment—0
Cross-Domain Underwater Image Enhancement Guided by No-Reference Image Quality Assessment: A Transfer Learning Approach—0
Subjective Quality Assessment for Images Generated by Computer Graphics—0
Cross-IQA: Unsupervised Learning for Image Quality Assessment—0
A No-Reference Medical Image Quality Assessment Method Based on Automated Distortion Recognition Technology: Application to Preprocessing in MRI-guided Radiotherapy—0
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