Papers No-Reference Image Quality Assessment
“No-Reference Image Quality Assessment” 태그가 달린 논문 176편 · 필터 해제
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 AssessmentBanana100: Breaking NR-IQA Metrics by 100 Iterative Image Replications with Nano Banana Pro
The multi-step, iterative image editing capabilities of multi-modal agentic systems have transformed digital content creation. Although latest image editing models faithfully follow instructions and generate high-quality…
No-Reference Image Quality AssessmentImage EditingBeyond Ground-Truth: Leveraging Image Quality Priors for Real-World Image Restoration
Real-world image restoration aims to restore high-quality (HQ) images from degraded low-quality (LQ) inputs captured under uncontrolled conditions. Existing methods typically depend on ground-truth (GT) supervision, assu…
No-Reference Image Quality AssessmentImage RestorationPreference-Guided Debiasing for No-Reference Enhancement Image Quality Assessment
Current no-reference image quality assessment (NR-IQA) models for enhanced images often struggle to generalize, as they tend to overfit to the distinct patterns of specific enhancement algorithms rather than evaluating g…
No-Reference Image Quality AssessmentContrastive LearningSHAMISA: SHAped Modeling of Implicit Structural Associations for Self-supervised No-Reference Image Quality Assessment
No-Reference Image Quality Assessment (NR-IQA) aims to estimate perceptual quality without access to a reference image of pristine quality. Learning an NR-IQA model faces a fundamental bottleneck: its need for a large nu…
No-Reference Image Quality AssessmentMS-SCANet: A Multiscale Transformer-Based Architecture with Dual Attention for No-Reference Image Quality Assessment
We present the Multi-Scale Spatial Channel Attention Network (MS-SCANet), a transformer-based architecture designed for no-reference image quality assessment (IQA). MS-SCANet features a dual-branch structure that process…
No-Reference Image Quality AssessmentBeyond Cosine Similarity: Magnitude-Aware CLIP for No-Reference Image Quality Assessment
Recent efforts have repurposed the Contrastive Language-Image Pre-training (CLIP) model for No-Reference Image Quality Assessment (NR-IQA) by measuring the cosine similarity between the image embedding and textual prompt…
No-Reference Image Quality AssessmentSemantic SimilarityDP$^2$O-SR: Direct Perceptual Preference Optimization for Real-World Image Super-Resolution
Benefiting from pre-trained text-to-image (T2I) diffusion models, real-world image super-resolution (Real-ISR) methods can synthesize rich and realistic details. However, due to the inherent stochasticity of T2I models, …
No-Reference Image Quality AssessmentImage Super-ResolutionNo-reference Quality Assessment of Contrast-distorted Images using Contrast-enhanced Pseudo Reference
Contrast change is an important factor that affects the quality of images. During image capturing, unfavorable lighting conditions can cause contrast change and visual quality loss. While various methods have been propos…
No-Reference Image Quality AssessmentImage GenerationSEGA: A Transferable Signed Ensemble Gaussian Black-Box Attack against No-Reference Image Quality Assessment Models
No-Reference Image Quality Assessment (NR-IQA) models play an important role in various real-world applications. Recently, adversarial attacks against NR-IQA models have attracted increasing attention, as they provide va…
No-Reference Image Quality AssessmentRevisiting Vision Language Foundations for No-Reference Image Quality Assessment
Large-scale vision language pre-training has recently shown promise for no-reference image-quality assessment (NR-IQA), yet the relative merits of modern Vision Transformer foundations remain poorly understood. In this w…
No-Reference Image Quality AssessmentDescribe-to-Score: Text-Guided Efficient Image Complexity Assessment
Accurately assessing image complexity (IC) is critical for computer vision, yet most existing methods rely solely on visual features and often neglect high-level semantic information, limiting their accuracy and generali…
No-Reference Image Quality AssessmentA Lightweight Ensemble-Based Face Image Quality Assessment Method with Correlation-Aware Loss
Face image quality assessment (FIQA) plays a critical role in face recognition and verification systems, especially in uncontrolled, real-world environments. Although several methods have been proposed, general-purpose n…
No-Reference Image Quality AssessmentFace Image Quality AssessmentFace RecognitionHiRQA: Hierarchical Ranking and Quality Alignment for Opinion-Unaware Image Quality Assessment
Despite significant progress in no-reference image quality assessment (NR-IQA), dataset biases and reliance on subjective labels continue to hinder their generalization performance. We propose HiRQA (Hierarchical Ranking…
No-Reference Image Quality AssessmentContrastive LearningA Parametric Bi-Directional Curvature-Based Framework for Image Artifact Classification and Quantification
This work presents a novel framework for No-Reference Image Quality Assessment (NR-IQA) founded on the analysis of directional image curvature. Within this framework, we define a measure of Anisotropic Texture Richness (…
No-Reference Image Quality Assessment