结合熵、梯度、峰度特征的无参考噪声图像质量评价

姚恒; 马奔; 邹勔; 徐栋; 姚劲草

doi:10.1631/FITEE.2000716

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结合熵、梯度、峰度特征的无参考噪声图像质量评价

常规文章 | Updated：2022-06-14

- 结合熵、梯度、峰度特征的无参考噪声图像质量评价
  Enhanced Publication
- No-reference noisy image quality assessment incorporating features of entropy, gradient, and kurtosis
- 信息与电子工程前沿（英文） 2021年22卷第12期页码：1565-1582
- Affiliations：
  
  1.School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai200093, China
  2.School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai200093, China
  3.Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou310000, China
  4.Institute of Basic Medicine and Cancer, Chinese Academy of Sciences, Hangzhou310000, China
- Author bio：
  
  [ "Heng YAO, first author of this invited paper, received his BS degree from Hefei University of Technology, China, in 2004, his MS degree from Shanghai Normal University, China, in 2008, and his PhD degree in Signal and Information Processing from Shanghai University, China, in 2012. He is currently an associate professor at University of Shanghai for Science and Technology. His research interests include multimedia security, image processing, and pattern recognition. He has published more than 40 peer-reviewed papers in international journals." ]
  [ "Dong XU, corresponding author of this invited paper, received his BS and MS degrees in Medical Imaging from Southeast University and Zhejiang University in 2002 and 2008, respectively. In 2011, he received his PhD degree in Clinical Specialty of Integrated Traditional Chinese and Western Medicine from Zhejiang Chinese Medical University. He is now a professor at the Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences and Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital). His current research interests include medical image processing, machine learning, radiomics, and medical imaging diagnosis of tumor." ]
  [ "Jincao YAO, corresponding author of this invited paper, received his BS degree in Computer and Information Science from Hefei University of Technology in 2004, and his MS degree in Signal Processing from Shanghai Normal University in 2007. In 2017, he received his PhD degree in Signal and Information Processing at the College of Information Science and Electronic Engineering, Zhejiang University, China. He is now an imaging physicist at the IBMC, Chinese Academy of Sciences and Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital). His current research interests include machine learning, computer vision, medical image recognition, radiomics, and shape-driven techniques in image processing." ]
- Funds：
  
  National Natural Science Foundation of China(61702332);Zhejiang Provincial Natural Science Foundation of China(LZY21F030001;LSD19H180001)
- DOI：10.1631/FITEE.2000716
  中图分类号： TP753
- 纸质出版日期：2021-12-0 ，
  
  网络出版日期：2021-10-11，
  
  收稿日期：2020-12-23，
  
  修回日期：2021-06-08，
  
  录用日期：2021-03-04
- Accepted：
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姚恒, 马奔, 邹勔, 等. 结合熵、梯度、峰度特征的无参考噪声图像质量评价[J]. 信息与电子工程前沿（英文）, 2021,22(12):1565-1582.

HENG YAO, BEN MA, MIAN ZOU, et al. 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.
姚恒, 马奔, 邹勔, 等. 结合熵、梯度、峰度特征的无参考噪声图像质量评价[J]. 信息与电子工程前沿（英文）, 2021,22(12):1565-1582. DOI： 10.1631/FITEE.2000716.

HENG YAO, BEN MA, MIAN ZOU, et al. 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. DOI： 10.1631/FITEE.2000716.

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。

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.

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

Keywords

Noisy image quality assessmentNoise estimationKurtosisHuman visual systemSupport vector regression

references

S Bosse, , D Maniry, , T Wiegand, , et al., . 2016. . A deep neural network for image quality assessment. . Proc IEEE Int Conf on Image Processing, p.. 3773--3777. . doi: 10.1109/ICIP.2016.7533065http://doi.org/10.1109/ICIP.2016.7533065.

M Buczkowski, . 2018. . Non-reference image quality assessment based on noise estimation. . Proc 25th Int Conf on Systems, Signals and Image Processing, p., 1--4. . doi: 10.1109/IWSSIP.2018.8439331http://doi.org/10.1109/IWSSIP.2018.8439331.

CC Chang, , CJ Lin, . 2011. . LIBSVM: a library for support vector machines. . ACM Trans Intell Syst Technol, , 2((3):):27. doi: 10.1145/1961189.1961199http://doi.org/10.1145/1961189.1961199.

DQ Chen, , YZ Wang, , W Gao, . 2020. . No-reference image quality assessment: an attention driven approach. . IEEE Trans Image Process, , 29:6496--6506. . doi: 10.1109/TIP.2020.2990342http://doi.org/10.1109/TIP.2020.2990342.

CW Deng, , SG Wang, , AC Bovik, , et al., . 2020. . Blind noisy image quality assessment using sub-band kurtosis. . IEEE Trans Cybern, , 50((3):):1146--1156. . doi: 10.1109/TCYB.2018.2889376http://doi.org/10.1109/TCYB.2018.2889376.

Y Ding, , N Li, , Y Zhao, , et al., . 2016. . Image quality assessment method based on nonlinear feature extraction in kernel space. . Front Inform Technol Electron Eng, , 17((10):):1008--1017. . doi: 10.1631/FITEE.1500439http://doi.org/10.1631/FITEE.1500439.

L Dong, , JT Zhou, , YY Tang, . 2017. . Noise level estimation for natural images based on scale-invariant kurtosis and piecewise stationarity. . IEEE Trans Image Process, , 26((2):):1017--1030. . doi: 10.1109/TIP.2016.2639447http://doi.org/10.1109/TIP.2016.2639447.

K Gu, , GT Zhai, , XK Yang, , et al., . 2015. . Using free energy principle for blind image quality assessment. . IEEE Trans Multim, , 17((1):):50--63. . doi: 10.1109/TMM.2014.2373812http://doi.org/10.1109/TMM.2014.2373812.

R Guo, , XJ Shen, , XY Dong, , et al., . 2020. . Multi-focus image fusion based on fully convolutional networks. . Front Inform Technol Electron Eng, , 21((7):):1019--1033. . doi: 10.1631/FITEE.1900336http://doi.org/10.1631/FITEE.1900336.

B Hu, , LD Li, , JJ Wu, , et al., . 2020. . Subjective and objective quality assessment for image restoration: a critical survey. . Signal Process Image Commun, , 85:115839. doi: 10.1016/j.image.2020.115839http://doi.org/10.1016/j.image.2020.115839.

XT Huang, , L Chen, , J Tian, , et al., . 2014. . Blind noisy image quality assessment using block homogeneity. . Comput Electr Eng, , 40((3):):796--807. . doi: 10.1016/j.compeleceng.2013.08.002http://doi.org/10.1016/j.compeleceng.2013.08.002.

XH Jiang, , LQ Shen, , LW Yu, , et al., . 2020. . No-reference screen content image quality assessment based on multi-region features. . Neurocomputing, , 386:30--41. . doi: 10.1016/j.neucom.2019.12.027http://doi.org/10.1016/j.neucom.2019.12.027.

J Kennedy, , R Eberhart, . 1995. . Particle swarm optimization. . Proc Int Conf on Neural Networks, p.. 1942--1948. . doi: 10.1109/ICNN.1995.488968http://doi.org/10.1109/ICNN.1995.488968.

XF Kong, , K Li, , QX Yang, , et al., . 2013. . A new image quality metric for image auto-denoising. . Proc IEEE Int Conf on Computer Vision, p.. 2888--2895. . doi: 10.1109/ICCV.2013.359http://doi.org/10.1109/ICCV.2013.359.

EC Larson, , DM Chandler, . 2010. . Most apparent distortion: full-reference image quality assessment and the role of strategy. . J Electron Image, , 19((1):):011006. doi: 10.1117/1.3267105http://doi.org/10.1117/1.3267105.

LD Li, , WH Xia, , YM Fang, , et al., . 2016a. . Color image quality assessment based on sparse representation and reconstruction residual. . J Vis Commun Image Represent, , 38:550--560. . doi: 10.1016/j.jvcir.2016.04.006http://doi.org/10.1016/j.jvcir.2016.04.006.

LD Li, , WS Lin, , XS Wang, , et al., . 2016b. . No-reference image blur assessment based on discrete orthogonal moments. . IEEE Trans Cybern, , 46((1):):39--50. . doi: 10.1109/TCYB.2015.2392129http://doi.org/10.1109/TCYB.2015.2392129.

LD Li, , WH Xia, , WS Lin, , et al., . 2017. . No-reference and robust image sharpness evaluation based on multiscale spatial and spectral features. . IEEE Trans Multim, , 19((5):):1030--1040. . doi: 10.1109/TMM.2016.2640762http://doi.org/10.1109/TMM.2016.2640762.

PY Li, , KT Lo, . 2018. . A content-adaptive joint image compression and encryption scheme. . IEEE Trans Multim, , 20((8):):1960--1972. . doi: 10.1109/TMM.2017.2786860http://doi.org/10.1109/TMM.2017.2786860.

QH Li, , WS Lin, , YM Fang, . 2017. . BSD: blind image quality assessment based on structural degradation. . Neurocomputing, , 236:93--103. . doi: 10.1016/j.neucom.2016.09.105http://doi.org/10.1016/j.neucom.2016.09.105.

M Liu, , GT Zhai, , ZY Zhang, , et al., . 2014. . Blind image quality assessment for noise. . Proc IEEE Int Symp on Broadband Multimedia Systems and Broadcasting, p.. 1--5. . doi: 10.1109/BMSB.2014.6873480http://doi.org/10.1109/BMSB.2014.6873480.

SW Lyu, , XY Pan, , X Zhang, . 2014. Exposing region splicing forgeries with blind local noise estimation. . Int J Comput Vis, , 110((2):):202--221. . doi: 10.1007/s11263-013-0688-yhttp://doi.org/10.1007/s11263-013-0688-y.

B Ma, , JC Yao, , YF Le, , et al., . 2020. . Efficient image noise estimation based on skewness invariance and adaptive noise injection. . IET Image Process, , 14((7):):1393--1401. . doi: 10.1049/iet-ipr.2019.1548http://doi.org/10.1049/iet-ipr.2019.1548.

XK Min, , GT Zhai, , K Gu, , et al., . 2018. . Blind image quality estimation via distortion aggravation. . IEEE Trans Broadcast, , 64((2):):508--517. . doi: 10.1109/TBC.2018.2816783http://doi.org/10.1109/TBC.2018.2816783.

A Mittal, , AK Moorthy, , AC Bovik, . 2012. . No-reference image quality assessment in the spatial domain. . IEEE Trans Image Process, , 21((12):):4695--4708. . doi: 10.1109/TIP.2012.2214050http://doi.org/10.1109/TIP.2012.2214050.

A Mittal, , R Soundararajan, , AC Bovik, . 2013. . Making a “completely blind” image quality analyzer. . IEEE Signal Process Lett, , 20((3):):209--212. . doi: 10.1109/LSP.2012.2227726http://doi.org/10.1109/LSP.2012.2227726.

AK Moorthy, , AC Bovik, . 2011. . Blind image quality assessment: from natural scene statistics to perceptual quality. . IEEE Trans Image Process, , 20((12):):3350--3364. . doi: 10.1109/TIP.2011.2147325http://doi.org/10.1109/TIP.2011.2147325.

JE Ospina-Borras, , HDB Restrepo, . 2016. . Non-reference assessment of sharpness in blur/noise degraded images. . J Vis Commun Image Represent, , 39:142--151. . doi: 10.1016/j.jvcir.2016.05.015http://doi.org/10.1016/j.jvcir.2016.05.015.

M Oszust, . 2019. . No-reference quality assessment of noisy images with local features and visual saliency models. . Inform Sci, , 482:334--349. . doi: 10.1016/j.ins.2019.01.034http://doi.org/10.1016/j.ins.2019.01.034.

CH Pan, , Y Xu, , YC Yan, , et al., . 2016. . Exploiting neural models for no-reference image quality assessment. . Proc Visual Communications and Image Processing, p.. 1--4. . doi: 10.1109/VCIP.2016.7805524http://doi.org/10.1109/VCIP.2016.7805524.

N Ponomarenko, , O Ieremeiev, , V Lukin, , et al., . 2013. . A new color image database TID2013: innovations and results. . Proc 15th Int Conf on Advanced Concepts for Intelligent Vision Systems, p., 402--413. . doi: 10.1007/978-3-319-02895-8_36http://doi.org/10.1007/978-3-319-02895-8_36.

MA Saad, , AC Bovik, , C Charrier, . 2012. . Blind image quality assessment: a natural scene statistics approach in the DCT domain. . IEEE Trans Image Process, , 21((8):):3339--3352. . doi: 10.1109/TIP.2012.2191563http://doi.org/10.1109/TIP.2012.2191563.

HR Sheikh, , MF Sabir, , AC Bovik, . 2006. . A statistical evaluation of recent full reference image quality assessment algorithms. . IEEE Trans Image Process, , 15((11):):3440--3451. . doi: 10.1109/TIP.2006.881959http://doi.org/10.1109/TIP.2006.881959.

LL Shen, , N Hang, , CP Hou, . 2020. . Feature-segmentation strategy based convolutional neural network for no-reference image quality assessment. . Multim Tool Appl, , 79((17-18):):11891--11904. . doi: 10.1007/s11042-019-08298-2http://doi.org/10.1007/s11042-019-08298-2.

LJ Tang, , LD Li, , KZ Sun, , et al., . 2017. . An efficient and effective blind camera image quality metric via modeling quaternion wavelet coefficients. . J Vis Commun Image Represent, , 49:204--212. . doi: 10.1016/j.jvcir.2017.09.010http://doi.org/10.1016/j.jvcir.2017.09.010.

ZJ Tang, , ZQ Huang, , H Yao, , et al., . 2018. . Perceptual image hashing with weighted DWT features for reduced-reference image quality assessment. . Comput J, , 61((11):):1695--1709. . doi: 10.1093/comjnl/bxy047http://doi.org/10.1093/comjnl/bxy047.

Video Quality Experts Group, . 2003. . Final Report from the Video Quality Experts Group on the Validation of Objective Models of Video Quality Assessment, Phase II (fr_tv2). . http://www.vqeg.orghttp://www.vqeg.org, .

Q Wang, , J Chu, , L Xu, , et al., . 2016. . A new blind image quality framework based on natural color statistic. . Neurocomputing, , 173:1798--1810. . doi: 10.1016/j.neucom.2015.09.057http://doi.org/10.1016/j.neucom.2015.09.057.

Z Wang, , AC Bovik, , HR Sheikh, , et al., . 2004. . Image quality assessment: from error visibility to structural similarity. . IEEE Trans Image Process, , 13((4):):600--612. . doi: 10.1109/TIP.2003.819861http://doi.org/10.1109/TIP.2003.819861.

JJ Wu, , M Zhang, , LD Li, , et al., . 2019. . No-reference image quality assessment with visual pattern degradation. . Inform Sci, , 504:487--500. . doi: 10.1016/j.ins.2019.07.061http://doi.org/10.1016/j.ins.2019.07.061.

L Xu, , G Huang, , QL Chen, , et al., . 2020. . An improved method for image denoising based on fractional-order integration. . Front Inform Technol Electron Eng, , 21((10):):1485--1493. . doi: 10.1631/FITEE.1900727http://doi.org/10.1631/FITEE.1900727.

P Ye, , J Kumar, , L Kang, , et al., . 2012. . Unsupervised feature learning framework for no-reference image quality assessment. . Proc IEEE Conf on Computer Vision and Pattern Recognition, p.. 1098--1105. . doi: 10.1109/CVPR.2012.6247789http://doi.org/10.1109/CVPR.2012.6247789.

GT Zhai, , XL Wu, . 2011. . Noise estimation using statistics of natural images. . Proc 18th IEEE Int Conf on Image Processing, p., 1857--1860. . doi: 10.1109/ICIP.2011.6115828http://doi.org/10.1109/ICIP.2011.6115828.

GT Zhai, , XL Wu, , XK Yang, , et al., . 2012. . A psychovisual quality metric in free-energy principle. . IEEE Trans Image Process, , 21((1):):41--52. . doi: 10.1109/TIP.2011.2161092http://doi.org/10.1109/TIP.2011.2161092.

GT Zhai, , A Kaup, , J Wang, , et al., . 2015. . A dual-model approach to blind quality assessment of noisy images. . APSIPA Trans Signal Inform Process, , 4:e4. doi: 10.1017/ATSIP.2015.8http://doi.org/10.1017/ATSIP.2015.8.

L Zhang, , L Zhang, , AC Bovik, . 2015. . A feature-enriched completely blind image quality evaluator. . IEEE Trans Image Process, , 24((8):):2579--2591. . doi: 10.1109/TIP.2015.2426416http://doi.org/10.1109/TIP.2015.2426416.

WJ Zhou, , L Yu, , WW Qiu, , et al., . 2017. . Local gradient patterns (LGP): an effective local-statistical-feature extraction scheme for no-reference image quality assessment. . Inform Sci, , 397-398:1--14. . doi: 10.1016/j.ins.2017.02.049http://doi.org/10.1016/j.ins.2017.02.049.

HC Zhu, , LD Li, , JJ Wu, , et al., . 2020. . MetaIQA: deep meta-learning for no-reference image quality assessment. . IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.. 14143--14152. . doi: 10.1109/CVPR42600.2020.01415http://doi.org/10.1109/CVPR42600.2020.01415.

T Zhu, , L Karam, . 2014. . A no-reference objective image quality metric based on perceptually weighted local noise. . EURASIP J Image Video Process, , 2014((1):):1--8. . doi: 10.1186/1687-5281-2014-5http://doi.org/10.1186/1687-5281-2014-5.

D Zoran, , Y Weiss, . 2009. . Scale invariance and noise in natural images. . Proc IEEE Int Conf on Computer Vision, p.. 2209--2216. . doi: 10.1109/ICCV.2009.5459476http://doi.org/10.1109/ICCV.2009.5459476.

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