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

Onderzoeksoutput: Conference paper

5 Citaten (Scopus)

Samenvatting

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.
Originele taal-2English
Titel2019 IEEE 4th International Conference on Image, Vision and Computing (ICIVC)
Plaats van productieXiamen, China
UitgeverijIEEE
Pagina's276-280
Aantal pagina's5
VolumeJuly-2019
UitgaveJuly-2019
ISBN van elektronische versie978-1-7281-2325-7
ISBN van geprinte versie978-1-7281-2326-4
DOI's
StatusPublished - jul 2019
Evenement2019 4th IEEE International Conference on Image, Vision and Computing - Huaqiao University, Xiamen, China
Duur: 5 jul 20197 jul 2019
http://www.icivc.org/icivc19.html

Publicatie series

NaamProceedings of 2019 IEEE 4th International Conference on Image, Vision and Computing (ICIVC)
UitgeverijIEEE
NummerJuly-2019

Conference

Conference2019 4th IEEE International Conference on Image, Vision and Computing
Verkorte titelICIVC
Land/RegioChina
StadXiamen
Periode5/07/197/07/19
Internet adres

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