NR-IQA
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DGIQA: Depth-guided Feature Attention and Refinement for Generalizable Image Quality Assessment
VisualQuality-R1: Reasoning-Induced Image Quality Assessment via Reinforcement Learning to Rank
Exploring Semantic Feature Discrimination for Perceptual Image Super-Resolution and Opinion-Unaware No-Reference Image Quality Assessment
Backdoor Attacks against No-Reference Image Quality Assessment Models via a Scalable Trigger
AIM 2024 Challenge on UHD Blind Photo Quality Assessment
Papers
DGIQA: Depth-guided Feature Attention and Refinement for Generalizable Image Quality Assessment
A long-held challenge in no-reference image quality assessment (NR-IQA) learning from human subjective perception is the lack of objective generalization to unseen natural distortions. To address this, we integrate a nov…
Image Quality AssessmentNo-Reference Image Quality AssessmentNR-IQAVisualQuality-R1: Reasoning-Induced Image Quality Assessment via Reinforcement Learning to Rank
DeepSeek-R1 has demonstrated remarkable effectiveness in incentivizing reasoning and generalization capabilities of large language models (LLMs) through reinforcement learning. Nevertheless, the potential of reasoning-in…
Image GenerationImage Quality AssessmentLearning-To-RankNR-IQA+2Semantically-Aware Game Image Quality Assessment
Assessing the visual quality of video game graphics presents unique challenges due to the absence of reference images and the distinct types of distortions, such as aliasing, texture blur, and geometry level of detail (L…
Feature ImportanceImage Quality AssessmentKnowledge DistillationNR-IQA+2Exploring Semantic Feature Discrimination for Perceptual Image Super-Resolution and Opinion-Unaware No-Reference Image Quality Assessment
Generative Adversarial Networks (GANs) have been widely applied to image super-resolution (SR) to enhance the perceptual quality. However, most existing GAN-based SR methods typically perform coarse-grained discriminatio…
Image Quality AssessmentImage Super-ResolutionNo-Reference Image Quality AssessmentNR-IQA+1Cross-Domain Underwater Image Enhancement Guided by No-Reference Image Quality Assessment: A Transfer Learning Approach
Single underwater image enhancement (UIE) is a challenging ill-posed problem, but its development is hindered by two major issues: (1) The labels in underwater reference datasets are pseudo labels, relying on these pseud…
Image EnhancementImage Quality AssessmentNo-Reference Image Quality AssessmentNR-IQA+2IQPFR: An Image Quality Prior for Blind Face Restoration and Beyond
Blind Face Restoration (BFR) addresses the challenge of reconstructing degraded low-quality (LQ) facial images into high-quality (HQ) outputs. Conventional approaches predominantly rely on learning feature representation…
Blind Face RestorationImage Quality AssessmentNo-Reference Image Quality AssessmentNR-IQA