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

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Showing 1–50 of 155 papers

TitleStatusHype
TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality AssessmentCode4
SEAGULL: No-reference Image Quality Assessment for Regions of Interest via Vision-Language Instruction TuningCode2
LAR-IQA: A Lightweight, Accurate, and Robust No-Reference Image Quality Assessment ModelCode2
Pairwise Comparisons Are All You NeedCode2
Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning PerspectiveCode2
Exploring CLIP for Assessing the Look and Feel of ImagesCode2
MANIQA: Multi-dimension Attention Network for No-Reference Image Quality AssessmentCode2
Exploring Semantic Feature Discrimination for Perceptual Image Super-Resolution and Opinion-Unaware No-Reference Image Quality AssessmentCode1
AIM 2024 Challenge on UHD Blind Photo Quality AssessmentCode1
MobileIQA: Exploiting Mobile-level Diverse Opinion Network For No-Reference Image Quality Assessment Using Knowledge DistillationCode1
DP-IQA: Utilizing Diffusion Prior for Blind Image Quality Assessment in the WildCode1
Adaptive Image Quality Assessment via Teaching Large Multimodal Model to CompareCode1
Defense Against Adversarial Attacks on No-Reference Image Quality Models with Gradient Norm RegularizationCode1
PromptIQA: Boosting the Performance and Generalization for No-Reference Image Quality Assessment via PromptsCode1
Diffusion Model Based Visual Compensation Guidance and Visual Difference Analysis for No-Reference Image Quality AssessmentCode1
High Resolution Image Quality DatabaseCode1
Blind Image Quality Assessment Based on Geometric Order LearningCode1
Transformer-based No-Reference Image Quality Assessment via Supervised Contrastive LearningCode1
UIEDP:Underwater Image Enhancement with Diffusion PriorCode1
Learning Generalizable Perceptual Representations for Data-Efficient No-Reference Image Quality AssessmentCode1
PKU-I2IQA: An Image-to-Image Quality Assessment Database for AI Generated ImagesCode1
VCISR: Blind Single Image Super-Resolution with Video Compression Synthetic DataCode1
ARNIQA: Learning Distortion Manifold for Image Quality AssessmentCode1
AutoDIR: Automatic All-in-One Image Restoration with Latent DiffusionCode1
Analysis of Video Quality Datasets via Design of Minimalistic Video Quality ModelsCode1
On the Effectiveness of Spectral Discriminators for Perceptual Quality ImprovementCode1
Self2Self+: Single-Image Denoising with Self-Supervised Learning and Image Quality Assessment LossCode1
Deep learning techniques for blind image super-resolution: A high-scale multi-domain perspective evaluationCode1
Blind Image Quality Assessment via Transformer Predicted Error Map and Perceptual Quality TokenCode1
Data-Efficient Image Quality Assessment with Attention-Panel DecoderCode1
Re-IQA: Unsupervised Learning for Image Quality Assessment in the WildCode1
Contrastive Semi-supervised Learning for Underwater Image Restoration via Reliable BankCode1
Universal Perturbation Attack on Differentiable No-Reference Image- and Video-Quality MetricsCode1
Perceptual Attacks of No-Reference Image Quality Models with Human-in-the-LoopCode1
Deep Feature Statistics Mapping for Generalized Screen Content Image Quality AssessmentCode1
REQA: Coarse-to-fine Assessment of Image Quality to Alleviate the Range EffectCode1
Conformer and Blind Noisy Students for Improved Image Quality AssessmentCode1
Content-Variant Reference Image Quality Assessment via Knowledge DistillationCode1
Image Quality Assessment using Contrastive LearningCode1
No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-ConsistencyCode1
No-Reference Image Quality Assessment by Hallucinating Pristine FeaturesCode1
Generalizable No-Reference Image Quality Assessment via Deep Meta-learningCode1
Continual Learning for Blind Image Quality AssessmentCode1
No-Reference Image Quality Assessment with Global Statistical FeaturesCode1
Point Cloud Quality Assessment: Dataset Construction and Learning-based No-Reference MetricCode1
Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality AssessmentCode1
Perceptual Quality Assessment of Smartphone PhotographyCode1
Blindly Assess Image Quality in the Wild Guided by a Self-Adaptive Hyper NetworkCode1
Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and WildCode1
MetaIQA: Deep Meta-learning for 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