No Reference Image Sharpness Assessment Based on Global Color Difference Variation

SHI Chenyang; LIN Yandan

doi:10.23919/cje.2022.00.058

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SHI Chenyang, LIN Yandan. No Reference Image Sharpness Assessment Based on Global Color Difference Variation[J]. Chinese Journal of Electronics. doi: 10.23919/cje.2022.00.058

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SHI Chenyang, LIN Yandan. No Reference Image Sharpness Assessment Based on Global Color Difference Variation[J]. Chinese Journal of Electronics. doi: 10.23919/cje.2022.00.058

SHI Chenyang, LIN Yandan. No Reference Image Sharpness Assessment Based on Global Color Difference Variation[J]. Chinese Journal of Electronics. doi: 10.23919/cje.2022.00.058

Citation:

SHI Chenyang, LIN Yandan. No Reference Image Sharpness Assessment Based on Global Color Difference Variation[J]. Chinese Journal of Electronics. doi: 10.23919/cje.2022.00.058

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No Reference Image Sharpness Assessment Based on Global Color Difference Variation

doi: 10.23919/cje.2022.00.058

SHI Chenyang^{1, 2, 3
,},
LIN Yandan^{3, 4
,}

1.
School of Artificial Intelligence, Anhui Polytechnic University, Wuhu 241000, China
2.
School of Mechanical Engineering, Anhui Polytechnic University, Wuhu 241000, China
3.
Department of Light Sources and Illuminating Engineering, Fudan University, Shanghai 200433, China
4.
Academy for Engineering & Technology, Fudan University, Shanghai 200433, China

Funds: This work was supported by the Research Start-up Foundation for Introduction of Talents of AHPU (2021YQQ027) and Scientific Research Fund of AHPU (Xjky20220003).

More Information

Author Bio:
Chenyang SHI was born in 1991. He received the B.S. degree from Anhui Normal University in 2013, the M.S. degree from South China University of Technology in 2016, and the Ph.D. degree from Fudan University in 2021. He is currently a Lecturer of Anhui Polytechnic University. His research interest includes optical design and image quality assessment and optimization. (Email: shichenyang@ahpu.edu.cn)

Yandan LIN (corresponding author) was born in 1978. She received the B.S. and Ph.D. degrees from the Department of Light Sources & Illuminating Engineering, Fudan University, in 1999 and 2005. Now she is Ph.D. Supervisor and Professor of Department of Light Sources & Illuminating Engineering, Fudan University. Her research interests include visual comfort, health and smart lighting and image processing. (Email: ydlin@fudan.edu.cn)
Received Date: 2022-03-21
Accepted Date: 2022-11-20

Available Online: 2022-12-27

Abstract

Abstract

Image quality assessment (IQA) model is designed to measure the image quality in consistent with subjective ratings by computational models. In this research, a valid no reference IQA (NR-IQA) model for color image sharpness assessment is proposed based on local color difference map in a color space. In the proposed model, the absolute color difference variation and relative color difference variation are combined to evaluate sharpness in YIQ color space (a color coordinate system for the development of the United States color television system). The difference between sharpest and blurriest spot of an image is represented by the absolute color difference variation, and relative color difference variation expresses the variation in the image content. Extensive experiments are performed on five publicly available benchmark synthetic blur databases and two real blur databases, and the results prove that the proposed model work better than the other state-of-the-art and latest NR-IQA models for the prediction accuracy on blurry images. Besides, the model maintains the lowest computational complexity.
- Image sharpness assessment,
- No reference,
- Color difference variation,
- Image quality

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References(63)

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