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Image Quality Assessment

Papers

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Showing 301–325 of 732 papers

TitleStatusHype
Causal Perception Inspired Representation Learning for Trustworthy Image Quality Assessment—0
PKU-AIGIQA-4K: A Perceptual Quality Assessment Database for Both Text-to-Image and Image-to-Image AI-Generated ImagesCode0
Assessing Image Quality Using a Simple Generative RepresentationCode0
Image Quality Assessment With Compressed Sampling—0
NTIRE 2024 Quality Assessment of AI-Generated Content Challenge—0
Adaptive Mixed-Scale Feature Fusion Network for Blind AI-Generated Image Quality Assessment—0
Multi-Modal Prompt Learning on Blind Image Quality AssessmentCode0
CrossScore: Towards Multi-View Image Evaluation and Scoring—0
Beyond Score Changes: Adversarial Attack on No-Reference Image Quality Assessment from Two Perspectives—0
PCQA: A Strong Baseline for AIGC Quality Assessment Based on Prompt Condition—0
Multi-Sensor Diffusion-Driven Optical Image Translation for Large-Scale Applications—0
AI-KD: Towards Alignment Invariant Face Image Quality Assessment Using Knowledge DistillationCode0
PHISWID: Physics-Inspired Underwater Image Dataset Synthesized from RGB-D ImagesCode0
AIGIQA-20K: A Large Database for AI-Generated Image Quality Assessment—0
AIGCOIQA2024: Perceptual Quality Assessment of AI Generated Omnidirectional ImagesCode0
An edge detection-based deep learning approach for tear meniscus height measurement—0
Quality-Aware Image-Text Alignment for Real-World Image Quality AssessmentCode0
Diffusion-based Iterative Counterfactual Explanations for Fetal Ultrasound Image Quality Assessment—0
IG-FIQA: Improving Face Image Quality Assessment through Intra-class Variance Guidance robust to Inaccurate Pseudo-LabelsCode0
When No-Reference Image Quality Models Meet MAP Estimation in Diffusion Latents—0
Black-box Adversarial Attacks Against Image Quality Assessment Models—0
Accelerated Real-time Cine and Flow under In-magnet Staged Exercise—0
Towards Open-ended Visual Quality Comparison—0
A Lightweight Parallel Framework for Blind Image Quality Assessment—0
Data Quality Aware Approaches for Addressing Model Drift of Semantic Segmentation Models—0
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All datasetsMSU NR VQA DatabaseKonIQ-10kMSU FR VQA Database

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SPAQ MT-SSRCC0.88—Unverified
2SPAQ BLSRCC0.88—Unverified
3SPAQ MT-ASRCC0.88—Unverified
4KonCept512SRCC0.84—Unverified
5MEONSRCC0.51—Unverified
#ModelMetricClaimedVerifiedStatus
1RealQASRCC0.95—Unverified
2OneAlignSRCC0.94—Unverified
3UNIQASRCC0.93—Unverified
4KonCept512SRCC0.92—Unverified
#ModelMetricClaimedVerifiedStatus
1FSIMSRCC0.9—Unverified
2DSSSRCC0.9—Unverified
3MS-GMSDSRCC0.89—Unverified
4GMSDSRCC0.89—Unverified