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

Motohiro TAKAGI  Akito SAKURAI  Masafumi HAGIWARA  

IEICE TRANSACTIONS on Information and Systems   Vol.E102-D   No.11   pp.2265-2266
Publication Date: 2019/11/01
Publicized: 2019/08/09
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2018EDL8272
Type of Manuscript: LETTER
Category: Image Recognition, Computer Vision
image quality assessment,  subjective image quality,  convolutional neural network,  

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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.