Enhanced image no-reference quality assessment based on colour space distribution | |
Liu Hao1; Li Ce1; Zhang Dong1; Zhou Yannan1; Du Shaoyi2 | |
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 | |
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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