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[1] Jia Huizhen, Lei Chucong, Wang Tonghan, Li Tan, et al. No-reference blur assessment methodbased on gradient and saliency [J]. Journal of Southeast University (English Edition), 2021, 37 (2): 184-191. [doi:10.3969/j.issn.1003-7985.2021.02.008]
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No-reference blur assessment methodbased on gradient and saliency()
基于梯度和显著性的无参考模糊图像质量评价方法
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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
37
Issue:
2021 2
Page:
184-191
Research Field:
Information and Communication Engineering
Publishing date:
2021-06-20

Info

Title:
No-reference blur assessment methodbased on gradient and saliency
基于梯度和显著性的无参考模糊图像质量评价方法
Author(s):
Jia Huizhen1 Lei Chucong1 Wang Tonghan1 Li Tan1 Wu Jiasong2 Li Guang1He Jianfeng1 Shu Huazhong2
1Jiangxi Engineering Laboratory on Radioactive Geoscience and Big Data Technology, East China University of Technology, Nanchang 330013, China
2Laboratory of Image Science and Technology, Southeast University, Nanjing 210096, China
贾惠珍1 雷初聪1 王同罕1 李潭1 伍家松2 李广1 何剑锋1 舒华忠2
1东华理工大学江西省放射性地学大数据技术工程实验室, 南昌 330013; 2东南大学影像科学与技术实验室, 南京 210096
Keywords:
no-reference image quality assessment reblurring effect gradient similarity saliency
无参考图像质量评价 再模糊效应 梯度相似度 显著性
PACS:
TN911.73
DOI:
10.3969/j.issn.1003-7985.2021.02.008
Abstract:
To evaluate the quality of blurred images effectively, this study proposes a no-reference blur assessment method based on gradient distortion measurement and salient region maps. First, a Gaussian low-pass filter is used to construct a reference image by blurring a given image. Gradient similarity is included to obtain the gradient distortion measurement map, which can finely reflect the smallest possible changes in textures and details. Second, a saliency model is utilized to calculate image saliency. Specifically, an adaptive method is used to calculate the specific salient threshold of the blurred image, and the blurred image is binarized to yield the salient region map. Block-wise visual saliency serves as the weight to obtain the final image quality. Experimental results based on the image and video engineering database, categorial image quality database, and camera image database demonstrate that the proposed method correlates well with human judgment. Its computational complexity is also relatively low.
为了更加有效评价模糊图像的图像质量, 提出一种基于梯度失真测度图和显著区域图的无参考模糊图像质量评价方法.首先, 利用高斯低通滤波对待评价图像进行模糊化处理, 以构造参考图像, 并结合梯度相似度, 得到能精细反映图像微小细节的反差和纹理变化的梯度失真测度图.然后, 利用显著模型计算原模糊图像的显著性, 采用自适应算法计算原模糊图像的特定显著性阈值, 再通过二值化处理得到最终的显著区域图.最后, 利用显著区域子块显著值加权得到模糊图像的最终评价结果.在LIVE、CSIQ和CID2013数据库上的实验结果表明, 预测结果与人的主观判断具有较好的一致性, 且计算复杂度较低.

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Memo

Memo:
Biography: Jia Huizhen(1983—), female, doctor, lecturer, hzjianlg@126.com.
Foundation items: The National Natural Science Foundation of China(No. 61762004, 61762005), the National Key Research and Development Program(No. 2018YFB1702700), the Science and Technology Project Founded by the Education Department of Jiangxi Province, China(No. GJJ200702, GJJ200746), the Open Fund Project of Jiangxi Engineering Laboratory on Radioactive Geoscience and Big Data Technology(No. JETRCNGDSS201901, JELRGBDT202001, JELRGBDT202003).
Citation: Jia Huizhen, Lei Chucong, Wang Tonghan, et al. No-reference blur assessment method based on gradient and saliency[J].Journal of Southeast University(English Edition), 2021, 37(2):184-191.DOI:10.3969/j.issn.1003-7985.2021.02.008.
Last Update: 2021-06-20