[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()

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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

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

Keywords:

no-reference image quality assessment;  reblurring effect;  gradient similarity;  saliency

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.

References:

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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.

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