Image Quality Assessment
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Conditional Image Synthesis With Auxiliary Classifier GANs
ICML 2017
• eriklindernoren/PyTorch-GAN
• 
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.
#6 best model for
Conditional Image Generation
on CIFAR-10
NIMA: Neural Image Assessment
• idealo/image-quality-assessment
• 
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.
#4 best model for
Aesthetics Quality Assessment
on AVA
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.
ACTIVE LEARNING CROWD COUNTING IMAGE QUALITY ASSESSMENT LEARNING-TO-RANK
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: A Survey and Taxonomy
• sheqi/GAN_Review
•
We propose loss-variants and architecture-variants for classifying the most popular GANs, and discuss the potential improvements with focusing on these two aspects.
IMAGE INPAINTING IMAGE QUALITY ASSESSMENT IMAGE QUALITY ESTIMATION IMAGE SUPER-RESOLUTION IMAGE-TO-IMAGE TRANSLATION
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).
Video Imagination from a Single Image with Transformation Generation
• gitpub327/VideoImagination
•
To overcome those problems, we propose a new framework that produce imaginary videos by transformation generation.
A Probabilistic Quality Representation Approach to Deep Blind Image Quality Prediction
Recognizing this, we propose a new representation of perceptual image quality, called probabilistic quality representation (PQR), to describe the image subjective score distribution, whereby a more robust loss function can be employed to train a deep BIQA model.
Exploiting High-Level Semantics for No-Reference Image Quality Assessment of Realistic Blur Images
To guarantee a satisfying Quality of Experience (QoE) for consumers, it is required to measure image quality efficiently and reliably.
BLIND IMAGE QUALITY ASSESSMENT IMAGE QUALITY ESTIMATION NO-REFERENCE IMAGE QUALITY ASSESSMENT
Which Has Better Visual Quality: The Clear Blue Sky or a Blurry Animal?
IEEE Transactions on Multimedia 2018 • lidq92/SFA
The proposed method, SFA, is compared with nine representative blur-specific NR-IQA methods, two general-purpose NR-IQA methods, and two extra full-reference IQA methods on Gaussian blur images (with and without Gaussian noise/JPEG compression) and realistic blur images from multiple databases, including LIVE, TID2008, TID2013, MLIVE1, MLIVE2, BID, and CLIVE.
BLIND IMAGE QUALITY ASSESSMENT IMAGE CLASSIFICATION IMAGE QUALITY ESTIMATION NO-REFERENCE IMAGE QUALITY ASSESSMENT