Image Quality Assessment
70 papers with code • 1 benchmarks • 5 datasets
Benchmarks
| Trend | Dataset | Best Model | Paper Title | Paper | Code | Compare |
|---|---|---|---|---|---|---|
|
CelebA-HQ
CURE-TSR
TID2013
CROSS
Subtasks
Greatest papers with code
Conditional Image Synthesis With Auxiliary Classifier GANs
eriklindernoren/PyTorch-GAN
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ICML 2017
We expand on previous work for image quality assessment to provide two new analyses for assessing the discriminability and diversity of samples from class-conditional image synthesis models.
Ranked #10 on
Conditional Image Generation
on CIFAR-10
NIMA: Neural Image Assessment
idealo/image-quality-assessment
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Automatically learned quality assessment for images has recently become a hot topic due to its usefulness in a wide variety of applications such as evaluating image capture pipelines, storage techniques and sharing media.
Ranked #4 on
Aesthetics Quality Assessment
on AVA
SDD-FIQA: Unsupervised Face Image Quality Assessment with Similarity Distribution Distance
Thus, we propose a novel unsupervised FIQA method that incorporates Similarity Distribution Distance for Face Image Quality Assessment (SDD-FIQA).
Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank
Our results show that networks trained to regress to the ground truth targets for labeled data and to simultaneously learn to rank unlabeled data obtain significantly better, state-of-the-art results for both IQA and crowd counting.
RankIQA: Learning from Rankings for No-reference Image Quality Assessment
Furthermore, on the LIVE benchmark we show that our approach is superior to existing NR-IQA techniques and that we even outperform the state-of-the-art in full-reference IQA (FR-IQA) methods without having to resort to high-quality reference images to infer IQA.
Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy
sheqi/GAN_Review
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While several reviews for GANs have been presented to date, none have considered the status of this field based on their progress towards addressing practical challenges relevant to computer vision.
SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness
Face image quality is an important factor to enable high performance face recognition systems.
Ranked #1 on
Face Quality Assessement
on LFW
Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment
We present a deep neural network-based approach to image quality assessment (IQA).
Comparison of Image Quality Models for Optimization of Image Processing Systems
dingkeyan93/IQA-optimization
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The performance of objective image quality assessment (IQA) models has been evaluated primarily by comparing model predictions to human quality judgments.
Image Quality Assessment: Unifying Structure and Texture Similarity
Objective measures of image quality generally operate by comparing pixels of a "degraded" image to those of the original.