Discriminative Convolutional Neural Network for Image Quality Assessment with Fixed Convolution Filters

Motohiro Takagi, Akito Sakurai, Masafumi Hagiwara

Abstract: Current image quality assessment (IQA) methods require the original images for evaluation. However, recently, IQA methods that use machine learning have been proposed. These methods learn the relationship between the distorted image and the image quality automatically. In this paper, we propose an IQA method based on deep learning that does not require a reference image. We show that a convolutional neural network with distortion prediction and fixed filters improves the IQA accuracy.

External IDs:dblp:journals/ieicet/TakagiSH19