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: 6025
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,in press.https://doi.org/10.1631/FITEE.2000716 @article{title="No-reference noisy image quality assessment incorporating features of entropy, gradient, and kurtosis", %0 Journal Article TY - JOUR
结合熵、梯度、峰度特征的无参考噪声图像质量评价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。 关键词组: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
Reference[1]Bosse S, Maniry D, Wiegand T, et al., 2016. A deep neural network for image quality assessment. Proc IEEE Int Conf on Image Processing, p.3773-3777. [2]Buczkowski M, 2018. Non-reference image quality assessment based on noise estimation. Proc 25th Int Conf on Systems, Signals and Image Processing, p.1-4. [3]Chang CC, Lin CJ, 2011. LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol, 2(3):27. [4]Chen DQ, Wang YZ, Gao W, 2020. No-reference image quality assessment: an attention driven approach. IEEE Trans Image Process, 29:6496-6506. [5]Deng CW, Wang SG, Bovik AC, et al., 2020. Blind noisy image quality assessment using sub-band kurtosis. IEEE Trans Cybern, 50(3):1146-1156. [6]Ding Y, Li N, Zhao Y, et al., 2016. Image quality assessment method based on nonlinear feature extraction in kernel space. Front Inform Technol Electron Eng, 17(10):1008-1017. [7]Dong L, Zhou JT, Tang YY, 2017. Noise level estimation for natural images based on scale-invariant kurtosis and piecewise stationarity. IEEE Trans Image Process, 26(2):1017-1030. [8]Gu K, Zhai GT, Yang XK, et al., 2015. Using free energy principle for blind image quality assessment. IEEE Trans Multim, 17(1):50-63. [9]Guo R, Shen XJ, Dong XY, et al., 2020. Multi-focus image fusion based on fully convolutional networks. Front Inform Technol Electron Eng, 21(7):1019-1033. [10]Hu B, Li LD, Wu JJ, et al., 2020. Subjective and objective quality assessment for image restoration: a critical survey. Signal Process Image Commun, 85:115839. [11]Huang XT, Chen L, Tian J, et al., 2014. Blind noisy image quality assessment using block homogeneity. Comput Electr Eng, 40(3):796-807. [12]Jiang XH, Shen LQ, Yu LW, et al., 2020. No-reference screen content image quality assessment based on multi-region features. Neurocomputing, 386:30-41. [13]Kennedy J, Eberhart R, 1995. Particle swarm optimization. Proc Int Conf on Neural Networks, p.1942-1948. [14]Kong XF, Li K, Yang QX, et al., 2013. A new image quality metric for image auto-denoising. Proc IEEE Int Conf on Computer Vision, p.2888-2895. [15]Larson EC, Chandler DM, 2010. Most apparent distortion: full-reference image quality assessment and the role of strategy. J Electron Image, 19(1):011006. [16]Li LD, Xia WH, Fang YM, et al., 2016a. Color image quality assessment based on sparse representation and reconstruction residual. J Vis Commun Image Represent, 38: 550-560. [17]Li LD, Lin WS, Wang XS, et al., 2016b. No-reference image blur assessment based on discrete orthogonal moments. IEEE Trans Cybern, 46(1):39-50. [18]Li LD, Xia WH, Lin WS, et al., 2017. No-reference and robust image sharpness evaluation based on multiscale spatial and spectral features. IEEE Trans Multim, 19(5):1030-1040. [19]Li PY, Lo KT, 2018. A content-adaptive joint image compression and encryption scheme. IEEE Trans Multim, 20(8):1960-1972. [20]Li QH, Lin WS, Fang YM, 2017. BSD: blind image quality assessment based on structural degradation. Neurocomputing, 236:93-103. [21]Liu M, Zhai GT, Zhang ZY, et al., 2014. Blind image quality assessment for noise. Proc IEEE Int Symp on Broadband Multimedia Systems and Broadcasting, p.1-5. [22]Lyu SW, Pan XY, Zhang X, 2014 Exposing region splicing forgeries with blind local noise estimation. Int J Comput Vis, 110(2):202-221. [23]Ma B, Yao JC, Le YF, et al., 2020. Efficient image noise estimation based on skewness invariance and adaptive noise injection. IET Image Process, 14(7):1393-1401. [24]Min XK, Zhai GT, Gu K, et al., 2018. Blind image quality estimation via distortion aggravation. IEEE Trans Broadcast, 64(2):508-517. [25]Mittal A, Moorthy AK, Bovik AC, 2012. No-reference image quality assessment in the spatial domain. IEEE Trans Image Process, 21(12):4695-4708. [26]Mittal A, Soundararajan R, Bovik AC, 2013. Making a “completely blind” image quality analyzer. IEEE Signal Process Lett, 20(3):209-212. [27]Moorthy AK, Bovik AC, 2011. Blind image quality assessment: from natural scene statistics to perceptual quality. IEEE Trans Image Process, 20(12):3350-3364. [28]Ospina-Borras JE, Restrepo HDB, 2016. Non-reference assessment of sharpness in blur/noise degraded images. J Vis Commun Image Represent, 39:142-151. [29]Oszust M, 2019. No-reference quality assessment of noisy images with local features and visual saliency models. Inform Sci, 482:334-349. [30]Pan CH, Xu Y, Yan YC, et al., 2016. Exploiting neural models for no-reference image quality assessment. Proc Visual Communications and Image Processing, p.1-4. [31]Ponomarenko N, Ieremeiev O, Lukin V, 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. [32]Saad MA, Bovik AC, Charrier C, 2012. Blind image quality assessment: a natural scene statistics approach in the DCT domain. IEEE Trans Image Process, 21(8):3339-3352. [33]Sheikh HR, Sabir MF, Bovik AC, 2006. A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Trans Image Process, 15(11):3440-3451. [34]Shen LL, Hang N, Hou CP, 2020. Feature-segmentation strategy based convolutional neural network for no-reference image quality assessment. Multim Tool Appl, 79(17-18):11891-11904. [35]Tang LJ, Li LD, Sun KZ, 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. [36]Tang ZJ, Huang ZQ, Yao H, et al., 2018. Perceptual image hashing with weighted DWT features for reduced-reference image quality assessment. Comput J, 61(11):1695-1709. [37]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.org [38]Wang Q, Chu J, Xu L, et al., 2016. A new blind image quality framework based on natural color statistic. Neurocomputing, 173:1798-1810. [39]Wang Z, Bovik AC, Sheikh HR, et al., 2004. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process, 13(4):600-612. [40]Wu JJ, Zhang M, Li LD, et al., 2019. No-reference image quality assessment with visual pattern degradation. Inform Sci, 504:487-500. [41]Xu L, Huang G, Chen QL, et al., 2020. An improved method for image denoising based on fractional-order integration. Front Inform Technol Electron Eng, 21(10):1485-1493. [42]Ye P, Kumar J, Kang L, 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. [43]Zhai GT, Wu XL, 2011. Noise estimation using statistics of natural images. Proc 18th IEEE Int Conf on Image Processing, p.1857-1860. [44]Zhai GT, Wu XL, Yang XK, et al., 2012. A psychovisual quality metric in free-energy principle. IEEE Trans Image Process, 21(1):41-52. [45]Zhai GT, Kaup A, Wang J, et al., 2015. A dual-model approach to blind quality assessment of noisy images. APSIPA Trans Signal Inform Process, 4:e4. [46]Zhang L, Zhang L, Bovik AC, 2015. A feature-enriched completely blind image quality evaluator. IEEE Trans Image Process, 24(8):2579-2591. [47]Zhou WJ, Yu L, Qiu WW, 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. [48]Zhu HC, Li LD, Wu JJ, 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. [49]Zhu T, Karam L, 2014. A no-reference objective image quality metric based on perceptually weighted local noise. EURASIP J Image Video Process, 2014(1):1-8. [50]Zoran D, Weiss Y, 2009. Scale invariance and noise in natural images. Proc IEEE Int Conf on Computer Vision, p.2209-2216. Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou
310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE |
Open peer comments: Debate/Discuss/Question/Opinion
<1>