Image Quality Assessment
220 papers with code • 3 benchmarks • 12 datasets
Benchmarks
These leaderboards are used to track progress in Image Quality Assessment
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
Libraries
Use these libraries to find Image Quality Assessment models and implementationsDatasets
CSIQ
KonIQ-10k
MSU NR VQA Database
MSU FR VQA Database
TID2013
PIQ23
Hephaestus
Subtasks
Latest papers
GraFIQs: Face Image Quality Assessment Using Gradient Magnitudes
jankolf/grafiqs
•
•
We propose in this work a novel approach to assess the quality of face images based on inspecting the required changes in the pre-trained FR model weights to minimize differences between testing samples and the distribution of the FR training dataset.
AI-KD: Towards Alignment Invariant Face Image Quality Assessment Using Knowledge Distillation
lsibabnikz/ai-kd
•
•
To address this problem, we present in this paper a novel knowledge distillation approach, termed AI-KD that can extend on any existing FIQA technique, improving its robustness to alignment variations and, in turn, performance with different alignment procedures.
Defense Against Adversarial Attacks on No-Reference Image Quality Models with Gradient Norm Regularization
To be specific, we present theoretical evidence showing that the magnitude of score changes is related to the $\ell_1$ norm of the model's gradient with respect to the input image.
Quality-Aware Image-Text Alignment for Real-World Image Quality Assessment
In particular, we introduce a quality-aware image-text alignment strategy to make CLIP generate representations that correlate with the inherent quality of the images.
A Comprehensive Study of Multimodal Large Language Models for Image Quality Assessment
While Multimodal Large Language Models (MLLMs) have experienced significant advancement on visual understanding and reasoning, their potentials to serve as powerful, flexible, interpretable, and text-driven models for Image Quality Assessment (IQA) remains largely unexplored.
PICNIQ: Pairwise Comparisons for Natural Image Quality Assessment
Blind image quality assessment (BIQA) approaches, while promising for automating image quality evaluation, often fall short in real-world scenarios due to their reliance on a generic quality standard applied uniformly across diverse images.
A Benchmark for Multi-modal Foundation Models on Low-level Vision: from Single Images to Pairs
Q-Future/Q-Bench
•
•
To this end, we design benchmark settings to emulate human language responses related to low-level vision: the low-level visual perception (A1) via visual question answering related to low-level attributes (e. g. clarity, lighting); and the low-level visual description (A2), on evaluating MLLMs for low-level text descriptions.
High Resolution Image Quality Database
To demonstrate the importance of a high resolution image quality database for training BIQA models to predict mean opinion scores (MOS) of high resolution images accurately, we trained and tested several traditional and deep learning based BIQA methods on different resolution versions of our database.
Double Trouble? Impact and Detection of Duplicates in Face Image Datasets
Additional steps based on face recognition and face image quality assessment models reduce false positives, and facilitate the deduplication of the face images both for intra- and inter-subject duplicate sets.
TIER: Text-Image Encoder-based Regression for AIGC Image Quality Assessment
jiquan123/TIER
•
•
However, most existing AIGCIQA methods regress predicted scores directly from individual generated images, overlooking the information contained in the text prompts of these images.
