图像质量评估 Image Quality Assessment

图像质量评估（Image Quality Assessment, IQA）即评估图像的质量。

以下是最近一些方法及其代码。

(ACM MM 2017, TMM 2019) No reference image quality assessment based Semantic Feature Aggregation
https://github.com/lidq92/SFA
(CVPR 2018) Blind Predicting Similar Quality Map for Image Quality Assessment
(TIP 2018) NIMA: Neural Image Assessment
https://ai.googleblog.com/2017/12/introducing-nima-neural-image-assessment.html
https://github.com/idealo/image-quality-assessment
https://github.com/titu1994/neural-image-assessment
https://github.com/kentsyx/Neural-IMage-Assessment
(TIP 2018) Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment
https://github.com/dmaniry/deepIQA
https://github.com/lidq92/WaDIQaM
(ICCV 2017) RankIQA: Learning from Rankings for No-reference Image Quality Assessment
https://github.com/xialeiliu/RankIQA

(CVPR 2018) Hallucinated-IQA: No-reference Image Quality Assessment via Adversarial Learning
https://kwanyeelin.github.io/projects/HIQA/HIQA.html
(BMVC 2018) Self-supervised Deep Multiple Choice Learning Network for Blind Image Quality Assessment
(CVPR 2014) Convolutional Neural Networks for No-Reference Image Quality Assessment
https://github.com/lidq92/CNNIQA
(TIP 2012) No Reference Image Quality Assessment in the Spatial Domain
https://github.com/krshrimali/No-Reference-Image-Quality-Assessment-using-BRISQUE-Model

(CVPR 2019) Effective Aesthetics Prediction with Multi-level Spatially Pooled Features
https://github.com/subpic/ava-mlsp
(PRIA 2019) Personalised aesthetics with residual adapters
https://github.com/crp94/Personalised-aesthetic-assessment-using-residual-adapters