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

Motohiro Takagi, Akito Sakurai, Masafumi Hagiwara

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)2265-2266
Number of pages2
JournalIEICE Transactions on Information and Systems
VolumeE102D
Issue number11
DOIs
Publication statusPublished - 2019 Jan 1

Keywords

  • Convolutional neural network
  • Image quality assessment
  • Subjective image quality

ASJC Scopus subject areas

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

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