Towards Content Independent No-reference Image Quality Assessment Using Deep Learning

Research output: Chapter in Book/Report/Conference proceedingConference paper

1 Citation (Scopus)

Abstract

The study of image quality assessment (IQA) is divided on natural scene and document images which are processed using different models and quality metrics. This casts challenges for the development of content-independent no-reference (NR) IQA models which can operate on different types of images without requiring information regarding the content of the images. In this paper we propose a unified no-reference image quality assessment (UIQA) model using a deep learning approach, where a generalization of NR IQA across natural scene and document images is achieved using a deep convolutional neural network (DCNN). Without having to discriminate the type of the images, the proposed model can assess the quality of natural scene and document images in a blind and uniform manner. Testing results on two benchmarking datasets demonstrate that the proposed model achieves promising performances competitive with the state-of-the-art simultaneously on natural scene and document images.
Original languageEnglish
Title of host publication2019 IEEE 4th International Conference on Image, Vision and Computing (ICIVC)
Place of PublicationXiamen, China
PublisherIEEE
Pages276-280
Number of pages5
VolumeJuly-2019
EditionJuly-2019
ISBN (Electronic)978-1-7281-2325-7
ISBN (Print)978-1-7281-2326-4
DOIs
Publication statusPublished - Jul 2019
Event2019 4th IEEE International Conference on Image, Vision and Computing - Huaqiao University, Xiamen, China
Duration: 5 Jul 20197 Jul 2019
http://www.icivc.org/icivc19.html

Publication series

NameProceedings of 2019 IEEE 4th International Conference on Image, Vision and Computing (ICIVC)
PublisherIEEE
NumberJuly-2019

Conference

Conference2019 4th IEEE International Conference on Image, Vision and Computing
Abbreviated titleICIVC
CountryChina
CityXiamen
Period5/07/197/07/19
Internet address

Keywords

  • no-reference image quality assessment
  • perceptual score
  • OCR accuracy
  • deep convolutional neural network
  • transfer learning

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