Enhanced image no-reference quality assessment based on colour space distribution

doi:10.1049/iet-ipr.2019.0856

IR > 新能源学院

	Enhanced image no-reference quality assessment based on colour space distribution
	Liu Hao 1; Li Ce1 ; Zhang Dong1 ; Zhou Yannan 1; Du Shaoyi 2
	2020-04-17
发表期刊	IET IMAGE PROCESSING
ISSN	1751-9659
卷号	14 期号:5 页码:807-817
摘要	In 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.
关键词	feature 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
DOI	10.1049/iet-ipr.2019.0856
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation (NSFC) of China[61866022][61971343][61573274] ; Gansu Province Basic Research Innovation Group[1506RJIA031]
WOS研究方向	Computer Science ; Engineering ; Imaging Science & Photographic Technology
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Imaging Science & Photographic Technology
WOS记录号	WOS:000526816700001
出版者	INST ENGINEERING TECHNOLOGY-IET
引用统计	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/76303
专题	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
通讯作者	Li Ce
作者单位	1.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
第一作者单位	电气工程与信息工程学院
通讯作者单位	电气工程与信息工程学院
第一作者的第一单位	电气工程与信息工程学院
推荐引用方式 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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