No-Reference Image Quality Assessment
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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.
Exploiting High-Level Semantics for No-Reference Image Quality Assessment of Realistic Blur Images
• lidq92/SFA
• 
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
No-Reference Quality Assessment of Contrast-Distorted Images using Contrast Enhancement
No-reference image quality assessment (NR-IQA) aims to measure the image quality without reference image.
Controllable List-wise Ranking for Universal No-reference Image Quality Assessment
First, to extend the authentically distorted image dataset, we present an imaging-heuristic approach, in which the over-underexposure is formulated as an inverse of Weber-Fechner law, and fusion strategy and probabilistic compression are adopted, to generate the degraded real-world images.
Quality Aware Generative Adversarial Networks
NeurIPS 2019
• lfovia/QAGANS
• 
Generative Adversarial Networks (GANs) have become a very popular tool for im- plicitly learning high-dimensional probability distributions.
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.
SOTA for Face Quality Assessement on LFW
FACE QUALITY ASSESSEMENT IMAGE QUALITY ESTIMATION NO-REFERENCE IMAGE QUALITY ASSESSMENT ROBUST FACE RECOGNITION
Robust statistics and no-reference image quality assessment in Curvelet domain
This paper uses robust statistics and curvelet transform to learn a general-purpose no-reference (NR) image quality assessment (IQA) model.
Potential of deep features for opinion-unaware, distortion-unaware, no-reference image quality assessment
However, majority of such methods either use hand-crafted features or require training on human opinion scores (supervised learning), which are difficult to obtain and standardise.