An Improved DIQaM_FR/NR Image Quality Assessment Model

doi:10.19678/j.issn.1000-3428.0055000

Abstract

Abstract: Image quality assessment models evaluate image quality by extracting and analyzing image features which are consistent with the human visual system.In recent years,with the development of deep learning technologies,many image quality assessment models based on deep learning have emerged,but most of them are prone to over-fitting problem with a small scale of data.To address the problem,this paper establishes a Res-DIQaM_FR/NR image quality assessment model by improving the DIQaM_FR/NR model.The improved model uses the transfer learning method to replace the original feature extraction layers of DIQaM_FR/NR with the pre-trained ResNet50 network for image feature extraction.Also,the global average pooling layer is used to replace the fully connected layer FC-512 of DIQaM_FR/NR for regressive learning of extracted features.Experimental results show that the proposed model can reduce the complexity of DIQaM_FR/NR while deepening its network structure,and can successfully simulate the visual system of human beings with a small scale of data to accurately assess image quality.

Key words: image quality assessment, feature extraction, transfer learning, deep learning, ResNet50 network

摘要： 图像质量评价模型通过提取并分析与人类视觉系统相一致的图像特征来达到评价图像质量的目的。随着深度学习技术的发展，很多基于深度学习的图像质量评价模型相继出现，但是多数模型在小数据量环境下容易出现过拟合问题。为此，通过对DIQaM_FR/NR模型进行改进，建立一种Res-DIQaM_FR/NR图像质量评价模型。采用迁移学习方法，利用预训练的ResNet50网络替代DIQaM_FR/NR的特征提取层以进行图像特征提取，使用全局平均池化层取代DIQaM_FR/NR中的全连接层FC-512从而对提取的特征进行回归学习。实验结果表明，该模型在降低DIQaM_FR/NR模型复杂度的同时能够深化其网络结构，在小数据量环境下也能较好地模拟人类视觉系统并准确评价图像质量。

关键词: 图像质量评价, 特征提取, 迁移学习, 深度学习, ResNet50网络

CLC Number:

TP391

XIE Rui, SHAO Kun, HUO Xing, MITHUN Md Masud Parvej. An Improved DIQaM_FR/NR Image Quality Assessment Model[J]. Computer Engineering, 2020, 46(8): 258-263,270.

谢瑞, 邵堃, 霍星, MITHUN Md Masud Parvej. 一种改进的DIQaM_FR/NR图像质量评价模型[J]. 计算机工程, 2020, 46(8): 258-263,270.

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References

[1] WANG Z,BOVIK A C,SHEIKH H R,et al.Image quality assessment:from error visibility to structural similarity[J].IEEE Transactions on Image Processing,2004,13(4):600-612.
[2] ZHANG L,ZHANG L,MOU X,et al.FSIM:a feature similarity index for image quality assessment[J].IEEE Transactions on Image Processing,2011,20(8):2378-2386.
[3] WANG Wei,LIU Jing,YANG Weiwei,et al.Blur image quality assessment based on DCT domain texture structure similarity[J].Computer Engineering,2015,41(11):253-256.(in Chinese) 王威,刘婧,杨蔚蔚,等.基于DCT域纹理结构相似度的模糊图像质量评价[J].计算机工程,2015,41(11):253-256.
[4] FENG Mingkun,ZHAO Shengmei,SUN Lihui,et al.Image quality assessment based on local Gaussian weighted fusion[J].Computer Engineering,2016,42(8):237-242.(in Chinese) 丰明坤,赵生妹,孙丽慧,等.基于局部高斯加权融合的图像质量评价[J].计算机工程,2016,42(8):237-242.
[5] MA L,LI S,ZHANG F,et al.Reduced-reference image quality assessment using reorganized DCT-based image representation[J].IEEE Transactions on Multimedia,2011,13(4):824-829.
[6] VAPNIK V,GOLOWICH S E,SMOLA A.Support vector method for function approximation,regression estimation,and signal processing[J].Advances in Neural Information Processing Systems,1970,9:281-287.
[7] MITTAL A,MOORTHY A K,BOVIK A C.No-reference image quality assessment in the spatial domain[J].IEEE Transactions on Image Processing,2012,21(12):4695-4708.
[8] ZHANG Y,CHANDLER D M.An algorithm for no-reference image quality assessment based on log-derivative statistics of natural scenes[C]//Proceedings of2013SPIE of Image Quality and System Performance.San Francisco,USA:SPIE Press,2013:256-263.
[9] MOORTHY A K,BOVIK A C.A two-step framework for constructing blind image quality indices[J].IEEE Signal Processing Letters,2010,17(5):513-516.
[10] KANG L,YE P,LI Y.Convolutional neural networks for no-reference image quality assessment[C]//Proceedings of2014IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2014:1733-1740.
[11] BIANCO S,CELONA L,NAPOLETANO P,et al.On the use of deep learning for blind image quality assessment[J].Signal,Image and Video Processing,2018,12(2):355-362.
[12] SUN C,LI H,LI W.No-reference image quality assessment based on global and local content perception[C]//Proceedings of2016IEEE Conference on Visual Communications and Image Processing.Washington D.C.,USA:IEEE Press,2016:23-63.
[13] BOSSE S,MANIRY D,MÜLLER K R,et al.Deep neural networks for no-reference and full-reference image quality assessment[J].IEEE Transactions on Image Processing,2016,27(1):206-219.
[14] KIM J,LEE S.Fully deep blind image quality predictor[J].IEEE Journal of Selected Topics in Signal Processing,2017,11(1):206-220.
[15] LI Y,PO L M,FENG L,et al.No-reference image quality assessment with deep convolutional neural networks[C]//Proceedings of IEEE International Conference on Digital Signal Processing.Washington D.C.,USA:IEEE Press,2016:266-278.
[16] HE K,ZHANG X,REN S,et al.Deep residual learning for image recognition[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2016:1203-1240.
[17] ZHUANG Fuzhen,LUO Ping,HE Qing,et al.Research progress of transfer learning[J].Journal of Software,2015,26(1):26-39.(in Chinese)庄福振,罗平,何清,等.迁移学习研究进展[J].软件学报,2015,26(1):26-39.
[18] NIKOLAY P,LIAN J,OLEG L,et al.Image database TID2013:peculiarities,results and perspectives[J].Signal Processing:Image Communication,2015,30:57-77.
[19] GHADIYARAM D,BOVIK A C.Massive online crowdsourced study of subjective and objective picture quality[J].IEEE Transactions on Image Processing,2016,25(1):372-387.
[20] WANG Z,LI Q.Information content weighting for perceptual image quality assessment[J].IEEE Transactions on Image Processing,2011,20(5):1185-1198.
[21] LIU A,LIN W,NARWARIA M.Image quality assessment based on gradient similarity[J].IEEE Transactions on Image Processing,2012,21(4):1500-1512.
[22] MITTAL A,SOUNDARARAJAN R,BOVIK A C.Making a "completely blind" image quality analyzer[J].IEEE Signal Processing Letters,2013,20(3):209-212.
[23] LIU L,DONG H,HUANG H,et al.No-reference image quality assessment in curvelet domain[J].Signal Processing:Image Communication,2014,29(4):494-505.
[24] HUANG G,LIU Z,MAATEN L V D,et al.Densely connected convolutional networks[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2017:52-63.

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