Full Text:   <1966>

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CLC number: TP753

On-line Access: 2021-12-23

Received: 2020-12-23

Revision Accepted: 2021-03-04

Crosschecked: 2021-06-08

Cited: 0

Clicked: 4025

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Heng Yao

https://orcid.org/0000-0002-3784-4157

Dong Xu

https://orcid.org/0000-0002-0583-240X

Jincao Yao

https://orcid.org/0000-0003-1543-6010

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Frontiers of Information Technology & Electronic Engineering  2021 Vol.22 No.12 P.1565-1582

http://doi.org/10.1631/FITEE.2000716


No-reference noisy image quality assessment incorporating features of entropy, gradient, and kurtosis


Author(s):  Heng Yao, Ben Ma, Mian Zou, Dong Xu, Jincao Yao

Affiliation(s):  School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China; more

Corresponding email(s):   hyao@usst.edu.cn, xudong@zjcc.org.cn, yaojc@zjcc.org.cn

Key Words:  Noisy image quality assessment, Noise estimation, Kurtosis, Human visual system, Support vector regression


Heng Yao, Ben Ma, Mian Zou, Dong Xu, Jincao Yao. No-reference noisy image quality assessment incorporating features of entropy, gradient, and kurtosis[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(12): 1565-1582.

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author="Heng Yao, Ben Ma, Mian Zou, Dong Xu, Jincao Yao",
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number="12",
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year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000716"
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Abstract: 
Noise is the most common type of image distortion affecting human visual perception. In this paper, we propose a no-reference image quality assessment (IQA) method for noisy images incorporating the features of entropy, gradient, and kurtosis. Specifically, image noise estimation is conducted in the discrete cosine transform domain based on skewness invariance. In the principal component analysis domain, kurtosis feature is obtained by statistically counting the significant differences between images with and without noise. In addition, both the consistency between the entropy and kurtosis features and the subjective scores are improved by combining them with the gradient coefficient. support vector regression is applied to map all extracted features into an integrated scoring system. The proposed method is evaluated in three mainstream databases (i.e., LIVE, TID2013, and CSIQ), and the results demonstrate the superiority of the proposed method according to the Pearson linear correlation coefficient which is the most significant indicator in IQA.

结合熵、梯度、峰度特征的无参考噪声图像质量评价

姚恒1,马奔2,邹勔2,徐栋3,4,姚劲草3,4
1上海理工大学光电信息与计算机工程学院,中国上海市,200093
2上海理工大学机械工程学院,中国上海市,200093
3中国科学院大学附属肿瘤医院(浙江省肿瘤医院),中国杭州市,310000
4中国科学院肿瘤与基础医学研究所,中国杭州市,310000
摘要:噪声是影响人类视觉感知最常见的图像失真类型。本文提出一种基于熵、梯度和峰度特征的无参考图像质量评估方法。具体来说,基于偏度不变性在离散余弦变换域进行图像噪声估计,进一步计算得到熵特征。在主成分分析变换域,通过统计有噪声图像与无噪声图像之间的显著差异得到峰度特征。此外,将熵和峰度特征与梯度系数结合,提高熵和峰度特征与主观得分之间的一致性。通过不同方向的滤波器对图像进行梯度特征提取,最后支持向量回归将所有提取的特征映射到综合评分系统中。为验证算法性能,在3个主流数据库(即LIVE、TID2013以及CSIQ)中对该方法进行评价。实验结果验证了该方法的优越性,尤其是在反映预测精度的皮尔逊线性相关系数方面的突出性能。

关键词:噪声图像质量评价;噪声估计;峰度;人类视觉系统;支持向量回归

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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