Discriminative convolutional neural network for image quality assessment with fixed convolution filters

Motohiro Takagi, Akito Sakurai, Masafumi Hagiwara

研究成果: Article査読

抄録

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.

本文言語English
ページ(範囲)2265-2266
ページ数2
ジャーナルIEICE Transactions on Information and Systems
E102D
11
DOI
出版ステータスPublished - 2019 1 1

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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