Enhanced image no-reference quality assessment based on colour space distribution
Liu Hao1; Li Ce1; Zhang Dong1; Zhou Yannan1; Du Shaoyi2
2020-04-17
Source PublicationIET IMAGE PROCESSING
ISSN1751-9659
Volume14Issue:5Pages:807-817
AbstractIn this study, the authors investigate the problem of enhanced image no-reference (NR) quality assessment. For resolving the problem of the enhanced images, it is difficult to obtain reference images, this study proposes an NR image quality assessment (IQA) model based on colour space distribution. Given an enhanced image, our method first uses a gist to select a clear target image in which the scene, colour and quality are similar to the hypothetical reference images. And then, the colour transfer is used between the input images and target images to construct the reference image. Next, the appropriate IQA method is used to assess enhanced image quality. The absolute colour difference and feature similarity (FSIM) are used to measure the colour and grey-scale image quality, respectively. Extensive experiments demonstrate that the proposed method is good at evaluating enhanced image quality for X-ray, dust, underwater and low-light images. The experimental results are consistent with human subjective evaluation and achieve good assessment effects.
Keywordfeature extraction image enhancement image colour analysis image resolution reference image enhanced image quality grey-scale image quality low-light images no-reference quality assessment colour space distribution NR image quality assessment model clear target image hypothetical reference images input images X-ray images underwater images dust images FSIM
DOI10.1049/iet-ipr.2019.0856
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation (NSFC) of China[61866022][61971343][61573274] ; Gansu Province Basic Research Innovation Group[1506RJIA031]
WOS Research AreaComputer Science ; Engineering ; Imaging Science & Photographic Technology
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Imaging Science & Photographic Technology
WOS IDWOS:000526816700001
PublisherINST ENGINEERING TECHNOLOGY-IET
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.lut.edu.cn/handle/2XXMBERH/76303
Collection新能源学院
能源与动力工程学院
电气工程与信息工程学院
Corresponding AuthorLi Ce
Affiliation1.Lanzhou Univ Technol, Coll Elect & Informat Engn, Lanzhou, Peoples R China
2.Xi An Jiao Tong Univ, Inst Artificial Intelligence & Robot, Xian, Peoples R China
First Author AffilicationColl Elect & Informat Engn
Corresponding Author AffilicationColl Elect & Informat Engn
First Signature AffilicationColl Elect & Informat Engn
Recommended Citation
GB/T 7714
Liu Hao,Li Ce,Zhang Dong,et al. Enhanced image no-reference quality assessment based on colour space distribution[J]. IET IMAGE PROCESSING,2020,14(5):807-817.
APA Liu Hao,Li Ce,Zhang Dong,Zhou Yannan,&Du Shaoyi.(2020).Enhanced image no-reference quality assessment based on colour space distribution.IET IMAGE PROCESSING,14(5),807-817.
MLA Liu Hao,et al."Enhanced image no-reference quality assessment based on colour space distribution".IET IMAGE PROCESSING 14.5(2020):807-817.
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