No-Reference Image Quality Assessment
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Benchmarks
Most implemented
SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness
Test Time Adaptation for Blind Image Quality Assessment
Deep learning techniques for blind image super-resolution: A high-scale multi-domain perspective evaluation
Re-IQA: Unsupervised Learning for Image Quality Assessment in the Wild
Papers
Bridging the Perceptual Gap: Residual-Enhanced Downscaling and Manifold-Aware Perception Alignment Adaptation for NR-IQA
Leveraging Large Vision-Language Models like CLIP has recently set new benchmarks for No-Reference Image Quality Assessment (NR-IQA). However, the contrastive pretraining of CLIP inherently prioritizes semantic invarianc…
No-Reference Image Quality AssessmentBlindPSNR: A No-Reference Fidelity Predictor for Low-Light Image Enhancement
Low-light image enhancement (LLIE) methods involve tunable parameters that are typically fixed, often leading to performance degradation when applied across scenes. Manually selecting the best configuration, however, can…
No-Reference Image Quality AssessmentLow-Light Image EnhancementSpatially Localized Image Degradation Embeddings for Image Quality Assessment
Self-supervised learning (SSL) currently drives state-of-the-art performance in no-reference image quality assessment (NR-IQA). However, standard SSL pipelines uniformly apply synthetic distortions across the entire imag…
No-Reference Image Quality AssessmentSelf-Supervised LearningClinReadNet: A clinical reading-inspired network for low-dose abdominal CT image quality assessment
In abdominal CT imaging, developing a low-dose, no-reference image quality assessment (No-reference IQA) model that mimics doctors' reading habits for evaluating CT image quality has significant practical value. This pap…
No-Reference Image Quality AssessmentACPO: Anchor-Constrained Perceptual Optimization for Diffusion Models with No-Reference Quality Guidance
Diffusion models have achieved remarkable success in image generation, yet their training is predominantly driven by full-reference objectives that enforce pixel-wise similarity to ground-truth images.Such supervision, w…
No-Reference Image Quality AssessmentImage GenerationA Lightweight Multi-Metric No-Reference Image Quality Assessment Framework for UAV Imaging
Reliable image quality assessment is essential in applications where large volumes of images are acquired automatically and must be filtered before further analysis. In many practical scenarios, a pristine reference imag…
No-Reference Image Quality Assessment