CLC number: TP753
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2021-06-08
Cited: 0
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Citations: Bibtex RefMan EndNote GB/T7714
https://orcid.org/0000-0002-3784-4157
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.
@article{title="No-reference noisy image quality assessment incorporating features of entropy, gradient, and kurtosis",
author="Heng Yao, Ben Ma, Mian Zou, Dong Xu, Jincao Yao",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="22",
number="12",
pages="1565-1582",
year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000716"
}
%0 Journal Article
%T No-reference noisy image quality assessment incorporating features of entropy, gradient, and kurtosis
%A Heng Yao
%A Ben Ma
%A Mian Zou
%A Dong Xu
%A Jincao Yao
%J Frontiers of Information Technology & Electronic Engineering
%V 22
%N 12
%P 1565-1582
%@ 2095-9184
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000716
TY - JOUR
T1 - No-reference noisy image quality assessment incorporating features of entropy, gradient, and kurtosis
A1 - Heng Yao
A1 - Ben Ma
A1 - Mian Zou
A1 - Dong Xu
A1 - Jincao Yao
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 22
IS - 12
SP - 1565
EP - 1582
%@ 2095-9184
Y1 - 2021
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000716
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.
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