Research Guide: Image Quality Assessment for Deep Learning

The authors of this paper compared 8 algorithms for blind IQA. They applied the AutoFolio system that trains an algorithm selector to choose the best-performing algorithm. They also trained a deep neural network to predict the best method. A CNN is trained to classify images according to which IQA method attains the best results. InceptionResNetV2 was used for the image classification problem.