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基于图像内容对比感知的图像质量客观评价

姚军财 申静

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基于图像内容对比感知的图像质量客观评价

姚军财, 申静

Objective assessment of image quality based on image content contrast perception

Yao Jun-Cai, Shen Jing
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  • 为了提出性能优异的图像质量评价(IQA)模型, 本文基于人类视觉感知特性和图像的灰度梯度、局部对比度和清晰度特征, 提出了一种基于图像内容对比感知的IQA方法. 在该方法中, 首先结合视觉感知特性, 基于物理学中对比度定义, 提出一种图像质量定义及其值计算方法; 之后, 基于灰度梯度共生矩阵, 提出一种图像灰度梯度熵的概念及其值的计算方法, 并基于图像灰度梯度熵、局部对比度和清晰度, 提出一种图像内容及其视觉感知的描述方法; 最后, 基于图像内容特征和图像质量定义, 综合分析, 提出IQA方法及其数学模型. 并且采用5个开源图像数据库中的119幅参考图像和6395幅失真图像对其进行了仿真测试, 同时分析和探讨了52种失真类型对IQA的影响; 另外, 为了说明所提IQA模型的优势, 将其与现有的7个典型IQA模型, 从精度、复杂性和泛化性能上进行了对比分析. 实验结果表明, 所提IQA模型的精度PLCC值在5个数据库中最低可以实现0.8616, 最高可达到0.9622, 其性能综合效益优于7个现有IQA模型. 研究结果表明, 所提IQA方法是有效的、可行的, 所提IQA模型是一个性能优异的IQA模型.
    Image quality assessment (IQA) plays a very important role in acquiring, storing, transmitting and processing image and video. Using the characteristics of human visual perception and the features of the gray, gradient, local contrast, and blurring of image, an IQA method based on the image content contrast perception is proposed in the paper, which is called MPCC. In the proposed method, firstly, combining with the characteristics of human visual perception, based on the definition of the contrast in physics, a novel definition for image quality and its calculation method are proposed. Then, based on the gray gradient co-occurrence matrix, a novel concept, namely the gray-gradient entropy of image, and its calculation method, are proposed. And based on the gray-gradient entropy, local contrast and blurring of image, a method of describing the image content and their visual perception are proposed. Finally, based on the image content features and the image quality definition, an IQA method and its mathematical model are proposed by comprehensive analysis. Further, the proposed IQA model MPCC is tested by using 119 reference images and 6395 distorted images from the five open image databases (LIVE, CSIQ, TID2008, TID2013 and IVC). Moreover, the influences of the 52 distortion types on IQA are analyzed. In addition, in order to illustrate the advantages of the MPCC model, it is compared with the seven existing typical IQA models in terms of the accuracy, complexity and generalization performance of model. The experimental results show that the accuracy PLCC of the MPCC model can achieve 0.8616 at lowest and 0.9622 at most in the five databases; among the 52 distortion types, the two distortion types, namely the change of color saturation and the local block-wise distortions of different intensity, have the greatest influence on IQA, and the accuracy PLCC values of the seven existing IQA models are almost all below 0.6, but the PLCC of the MPCC model can reach more than 0.68; and the comprehensive benefit of the performance of the MPCC model is better than those of the seven existing IQA models. These results of test and comparison above show that the proposed IQA method is effective and feasible, and the corresponding model has an excellent performance.
      通信作者: 姚军财, sxhzyjc@sina.com
    • 基金项目: 国家级-人眼对比度敏感视觉特性的测量及其在图像压缩中的应用研究(61301237)
      Corresponding author: Yao Jun-Cai, sxhzyjc@sina.com
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    丰卉, 孙彪, 马书根 2017 物理学报 66 180202Google Scholar

    Feng H, Sun B, Ma S G 2017 Acta Phys. Sin. 66 180202Google Scholar

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    Yao J C, Liu G Z 2019 IEEE Trans. Broadcast. 65 546Google Scholar

    [4]

    Chandler D M, Hemami S S 2007 IEEE Trans. Image Process. 16 2284Google Scholar

    [5]

    Wang Z, Bovik A C, Sheikh H R, Simoncelli E P 2004 IEEE Trans. Image Process. 13 600

    [6]

    Zhang L, Zhang L, Mou X, Zhang D 2011 IEEE Trans. Image Process. 20 2378Google Scholar

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    Xue W, Zhang L, Mou X, Bovik A C 2014 IEEE Trans. Image Process. 23 684Google Scholar

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    Zhang L, Shen Y, Li H 2014 IEEE Trans. Image Process. 23 4270Google Scholar

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    Larson E C, Chandler D M 2010 J. Electron. Imaging 19 011006Google Scholar

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    Fang Y M, Yan J B, Li L D, Wu J J, Lin W S 2018 IEEE Trans. Image Process. 27 1600Google Scholar

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    方志明, 崔荣一, 金璟璇 2017 物理学报 66 109501Google Scholar

    Fang Z M, Cui R Y, Jin J X 2017 Acta Phys. Sin. 66 109501Google Scholar

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    Qi H, Jiao S H, Lin W S, Tang L, Shen W H 2014 Electron. Lett. 50 1435

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    Zheng L, Shen L, Chen J, An P, Luo J 2019 IEEE Trans. Multimedia 21 2057Google Scholar

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    Yang X, Wang T, Ji G 2020 IET Image Proc. 14 384Google Scholar

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    Ahar A, Barri A, Schelkens P 2018 IEEE Trans. Image Process. 27 879Google Scholar

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    Zhou W J, Yu L, Zhou Y, Qiu W W, Wu M W 2018 IEEE Trans. Image Process. 27 2086

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    Yao J C, Liu G Z 2018 IET Image Proc. 12 872Google Scholar

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    Wang X, Meng F, Huang X Y 2018 Proceeding of the 11 th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Beijing, China, October 13—15, 2018 p1

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    Ginesu G, Massidda F, Giusto D D 2006 Signal Process. Image Commun. 21 316Google Scholar

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    Nadenau M 2000 Ph. D Dissertation (Lausanne: École Polytechnique Fédérale de Lausanne)

    [21]

    Niu Y Z, Zhang H F, Guo W Z, Ji R R 2018 IEEE Trans. Circuits Syst. Video Technol. 28 849

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    王鸿南, 钟文, 汪静, 夏德深 2004 中国图象图形学报 9 828Google Scholar

    Wang H N, Zhong W, Wang J, Xia D S 2004 J. Image Graph. 9 828Google Scholar

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    Sheikh H R, Wang Z, Cormack L LIVE Image Quality Assessment Database Release 2 Available: http://live.ece. utexas.edu/research/quality [2019-12-20]

    [24]

    Larson E C, Chandler D M The CSIQ image database http://vision.okstate.edu/?loc=csiq [2019-12-20]

    [25]

    Ponomarenko N, Lukin V, Zelensky A, Egiazarian K, Carli M, Battisti F Tampere Image Database 2008 TID2008, version 1.0 http://www.ponomarenko.info/tid2008.htm [2019-12-20]

    [26]

    Ponomarenko N, Jin L, Ieremeiev O, Lukin V, Egiazarian K, Astola J, Vozel B, Chehdi K, Carli M, Battisti F, Kuo C C J 2015 Signal Process. Image Commun. 30 57

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    Athar S, Wang Z 2019 IEEE Access 7 140030Google Scholar

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    Callet L, Patrick A F Subjective quality assessment IRCCyN /IVC database http://www2.irccyn.ec-nantes.fr/ivcdb/ [2019-12-20]

    [29]

    Yi Z, Chandler D M 2018 IEEE Trans. Image Process. 27 5433

    [30]

    Dai T, Gu K, Niu L, et al. 2018 Neurocomputing 290 185

    [31]

    Zhang C, Cheng W, Hirakawa K 2019 IEEE Trans. Image Process. 28 1732

  • 图 1  所提IQA方法的流程图

    Fig. 1.  The architecture of the proposed IQA method.

    图 2  4个数据库中的图像主客观IQA结果之间的散点图 (a) LIVE; (b) CSIQ; (c) TID2008; (d) TID2013

    Fig. 2.  Scatter plots between the subjective and objective IQA results of images in four databases: (a) LIVE; (b) CSIQ; (c) TID2008; (d) TID2013

    图 3  所提模型对IVC数据库中灰度和单色图像评价结果

    Fig. 3.  IQA results of the gray and monochrome images in IVC database by the proposed model.

    图 4  基于TID2008数据库中的图像IQA结果比较所提模型与现有7个模型的精度 (a) PSNR-TID2008; (b) VSNR-TID2008; (c) SSIM-TID2008; (d) FSIMc-TID2008; (e) VSI-TID2008; (f) GMSD-TID2008; (g) MAD-TID2008; (h) MPCC-TID2008

    Fig. 4.  Comparing the accuracy of the proposed model with those of the existing 7 models based on the IQA results in TID2008 database: (a) PSNR-TID2008; (b) VSNR-TID2008; (c) SSIM-TID2008; (d) FSIMc-TID2008; (e) VSI-TID2008; (f) GMSD-TID2008; (g) MAD-TID2008; (h) MPCC-TID2008.

    图 5  基于平均每10幅图像的评价运行时间比较8个IQA模型的复杂性

    Fig. 5.  Comparison of the complexity of 8 IQA models based on the IQA running time per 10 images.

    图 6  基于3个数据库中28类失真图像评价结果的PLCC值以8个IQA模型的精度对比 (a) CSIQ; (b) LIVE; (c) TID2008

    Fig. 6.  Accuracy comparisons among 8 IQA metrics based on PLCC of IQA results from 28 types of distortion images in three databases: (a) CSIQ; (b) LIVE; (c) TID2008.

    图 7  所提IQA模型对CSIQ库中6种失真类型的失真图像评价结果的散点图 (a) awgn; (b) jpeg; (c) jpeg2k; (d) fnoise; (e) blur; (f) contrast

    Fig. 7.  Scatter plots of the IQA results of 6 kinds of distorted images in CSIQ database evaluating by the proposed IQA model: (a) awgn; (b) jpeg; (c) jpeg2k; (d) fnoise; (e) blur; (f) contrast.

    图 10  所提IQA模型对TID2013库中24种失真类型的失真图像评价结果的散点图 (a) AGN; (b) NCC; (c) SCN; (d) MN; (e) HFN; (f) IN; (g) QN; (h) GB; (i) ID; (j) JPEG; (k) JPEG2k; (l) JPEGtrans; (m) JPEG2ktrans; (n) NEPN; (o) LBWD; (p) MS; (q) CC; (r) CCS; (s) MGN; (t) CN; (u) LCN; (v) CQWD; (w) CA; (x) SSR

    Fig. 10.  Scatter plots of the IQA results of 24 kinds of distorted images in TID2013 database evaluating by the proposed IQA model: (a) AGN; (b) NCC; (c) SCN; (d) MN; (e) HFN; (f) IN; (g) QN; (h) GB; (i) ID; (j) JPEG; (k) JPEG2k; (l) JPEGtrans; (m) JPEG2ktrans; (n) NEPN; (o) LBWD; (p) MS; (q) CC; (r) CCS; (s) MGN; (t) CN; (u) LCN; (v) CQWD; (w) CA; (x) SSR.

    图 8  所提IQA模型对LIVE库中5种失真类型的失真图像评价结果的散点图 (a) jpeg2k; (b) jpeg; (c) WN; (d) gblur; (e) fastfading

    Fig. 8.  Scatter plots of the IQA results of 5 kinds of distorted images in LIVE database evaluating by the proposed IQA model: (a) jpeg2k; (b) jpeg; (c) WN; (d) gblur; (e) fastfading.

    图 9  所提IQA模型对TID2008库中17种失真类型的失真图像评价结果的散点图 (a) AGN; (b) ANCC; (c) SCN; (d) MN; (e) HFN; (f) IN; (g) QN; (h) GB; (i) ID; (j) JPEG; (k) JPEG2k; (l) JPEGtrans; (m) JPEG2ktrans; (n) NEPN; (o) LBWD; (p) MS; (q) CC

    Fig. 9.  Scatter plots of the IQA results of 17 kinds of distorted images in TID2008 database evaluating by the proposed IQA model: (a) AGN; (b) ANCC; (c) SCN; (d) MN; (e) HFN; (f) IN; (g) QN; (h) GB; (i) ID; (j) JPEG; (k) JPEG2k; (l) JPEGtrans; (m) JPEG2ktrans; (n) NEPN; (o) LBWD; (p) MS; (q) CC.

    表 1  4个数据库中的图像主客观IQA分数之间的相关性参数计算结果

    Table 1.  Calculated 4 correlation parameters between the subjective and objective IQA scores of images in 4 databases.

    数据库LIVE(779)CSIQ(866)TID2008(1700)TID2013(3000)加权
    PLCC0.96220.95860.87780.86160.8915
    SROCC0.96600.95690.88310.84520.8854
    RMSE7.43970.07470.64270.6293
    OR0.15310.26900.12870.1198
    下载: 导出CSV

    表 2  基于CSIQ, LIVE和TID2013数据库中的图像IQA结果比较所提模型与现有7个模型的精度

    Table 2.  Comparing the accuracy of the proposed model with those of the existing 7 models based on the IQA results in CSIQ, LIVE, and TID2013 databases.

    数据库参数PSNRVSNRSSIMFSIMcVSIGMSDMADMPCC
    CSIQPLCC0.80000.80020.86130.91920.92790.95410.95020.9587
    SROCC0.80580.81060.87560.93100.94230.95700.94660.9569
    RMSE0.15750.15750.13340.10340.09790.07860.08180.0748
    OR0.42200.38320.35350.30410.28730.27420.28290.2738
    LIVEPLCC0.87230.92310.94490.96130.94820.96030.96750.9620
    SROCC0.87560.92740.94790.96450.95240.96030.96690.9660
    RMSE13.359710.50598.94557.52968.68167.62146.90737.4598
    OR0.21790.21510.18650.16270.18530.16430.15290.1606
    TID2013PLCC0.70620.74020.78950.87690.90000.85530.82670.8648
    SROCC0.69170.73160.74170.85100.89650.80440.78070.8452
    RMSE0.88870.83920.76080.59590.54040.64230.69750.6224
    OR0.16360.15520.14270.11320.10450.12420.13230.1179
    下载: 导出CSV

    表 3  基于TID2013库中24类失真图像评价结果的PLCC值以8个IQA模型的精度对比

    Table 3.  Accuracy comparisons among 8 IQA metrics based on PLCC of IQA results from 24 types of distortion images in TID2013 database.

    失真类别PSNRVSNRSSIMFSIMcVSIGMSDMADMPCC
    1 Additive Gaussian noise(AGN)0.95520.83190.86850.91520.95270.95030.88970.8706
    2 Noise in color comp. (NCC)0.92560.78140.80500.88730.91720.91180.84380.8324
    3 Spatially correl. noise (SCN)0.95250.81050.86210.89890.94720.93910.90080.7457
    4 Masked noise (MN)0.87070.77150.82190.84920.82030.75470.80090.6943
    5 High frequency noise (HFN)0.97310.90610.90810.94750.96550.95670.92330.9090
    6 Impulse noise (IN)0.88870.74420.74150.81710.86350.75720.32060.7408
    7 Quantization noise (QN)0.88800.83840.87020.87940.87470.91100.85710.8122
    8 Gaussian blur (GB)0.91690.94370.96340.95440.95510.90990.93570.9252
    9 Image denoising (ID)0.96400.94630.95890.96520.97070.97590.96450.9594
    10 JPEG compression (JPEG)0.91670.93860.95510.97540.98580.98430.96380.9509
    11 JPEG2000 compression (JPEG2 K)0.91700.95130.96580.97540.98450.98120.97400.9452
    12 JPEG transm. errors (JPEG trans.)0.81040.85970.91810.91760.94570.90790.90010.8805
    13 JPEG2000 transm. errors (JPEG2K trans)0.90020.84350.88010.89290.91920.90850.88380.8699
    14 Non ecc. patt. noise (NEPN)0.67460.67740.77730.80680.81620.81330.86080.8132
    15 Local block-wise dist. (LBWD)0.24100.36320.60220.55420.49840.65200.41870.6845
    16 Mean shift (MS)0.80560.51600.80190.78690.80210.77070.69340.7720
    17 Contrast change (CC)0.58110.42510.60260.72660.69740.71110.31990.8108
    18 Change of color saturation (CSS)0.32940.41840.45900.82280.80520.42340.28460.7583
    19 Multipl. Gauss. noise (MGN)0.92040.77300.78960.86600.91360.89110.85290.8759
    20 Comfort noise (CN)0.87020.90160.90220.94630.95460.95620.94440.8476
    21 Lossy compr. of noisy (LCN)0.94290.89600.91740.95640.96360.97030.95620.7889
    22 Image color quant. w. dither (CQWD)0.93080.87730.86190.89110.89630.91920.87790.8721
    23 Chromatic aberrations (CA)0.95560.95920.97700.97940.97480.97370.96960.9473
    24 Sparse sampl. and reconstr. (SSR)0.92960.94770.96670.97760.98080.98490.97660.9349
    Max0.97310.95920.97700.97940.98580.98490.97660.9594
    Min0.24100.36320.45900.55420.49840.42340.28460.6845
    波动范围宽度0.73210.59590.51810.42520.48730.56140.69200.2750
    所有整体精度0.70620.74020.78950.87690.90000.85530.82670.8648
    下载: 导出CSV
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    丰卉, 孙彪, 马书根 2017 物理学报 66 180202Google Scholar

    Feng H, Sun B, Ma S G 2017 Acta Phys. Sin. 66 180202Google Scholar

    [3]

    Yao J C, Liu G Z 2019 IEEE Trans. Broadcast. 65 546Google Scholar

    [4]

    Chandler D M, Hemami S S 2007 IEEE Trans. Image Process. 16 2284Google Scholar

    [5]

    Wang Z, Bovik A C, Sheikh H R, Simoncelli E P 2004 IEEE Trans. Image Process. 13 600

    [6]

    Zhang L, Zhang L, Mou X, Zhang D 2011 IEEE Trans. Image Process. 20 2378Google Scholar

    [7]

    Xue W, Zhang L, Mou X, Bovik A C 2014 IEEE Trans. Image Process. 23 684Google Scholar

    [8]

    Zhang L, Shen Y, Li H 2014 IEEE Trans. Image Process. 23 4270Google Scholar

    [9]

    Larson E C, Chandler D M 2010 J. Electron. Imaging 19 011006Google Scholar

    [10]

    Fang Y M, Yan J B, Li L D, Wu J J, Lin W S 2018 IEEE Trans. Image Process. 27 1600Google Scholar

    [11]

    方志明, 崔荣一, 金璟璇 2017 物理学报 66 109501Google Scholar

    Fang Z M, Cui R Y, Jin J X 2017 Acta Phys. Sin. 66 109501Google Scholar

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    Qi H, Jiao S H, Lin W S, Tang L, Shen W H 2014 Electron. Lett. 50 1435

    [13]

    Zheng L, Shen L, Chen J, An P, Luo J 2019 IEEE Trans. Multimedia 21 2057Google Scholar

    [14]

    Yang X, Wang T, Ji G 2020 IET Image Proc. 14 384Google Scholar

    [15]

    Ahar A, Barri A, Schelkens P 2018 IEEE Trans. Image Process. 27 879Google Scholar

    [16]

    Zhou W J, Yu L, Zhou Y, Qiu W W, Wu M W 2018 IEEE Trans. Image Process. 27 2086

    [17]

    Yao J C, Liu G Z 2018 IET Image Proc. 12 872Google Scholar

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    Wang X, Meng F, Huang X Y 2018 Proceeding of the 11 th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Beijing, China, October 13—15, 2018 p1

    [19]

    Ginesu G, Massidda F, Giusto D D 2006 Signal Process. Image Commun. 21 316Google Scholar

    [20]

    Nadenau M 2000 Ph. D Dissertation (Lausanne: École Polytechnique Fédérale de Lausanne)

    [21]

    Niu Y Z, Zhang H F, Guo W Z, Ji R R 2018 IEEE Trans. Circuits Syst. Video Technol. 28 849

    [22]

    王鸿南, 钟文, 汪静, 夏德深 2004 中国图象图形学报 9 828Google Scholar

    Wang H N, Zhong W, Wang J, Xia D S 2004 J. Image Graph. 9 828Google Scholar

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    Sheikh H R, Wang Z, Cormack L LIVE Image Quality Assessment Database Release 2 Available: http://live.ece. utexas.edu/research/quality [2019-12-20]

    [24]

    Larson E C, Chandler D M The CSIQ image database http://vision.okstate.edu/?loc=csiq [2019-12-20]

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    Ponomarenko N, Lukin V, Zelensky A, Egiazarian K, Carli M, Battisti F Tampere Image Database 2008 TID2008, version 1.0 http://www.ponomarenko.info/tid2008.htm [2019-12-20]

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    Ponomarenko N, Jin L, Ieremeiev O, Lukin V, Egiazarian K, Astola J, Vozel B, Chehdi K, Carli M, Battisti F, Kuo C C J 2015 Signal Process. Image Commun. 30 57

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    Athar S, Wang Z 2019 IEEE Access 7 140030Google Scholar

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    Callet L, Patrick A F Subjective quality assessment IRCCyN /IVC database http://www2.irccyn.ec-nantes.fr/ivcdb/ [2019-12-20]

    [29]

    Yi Z, Chandler D M 2018 IEEE Trans. Image Process. 27 5433

    [30]

    Dai T, Gu K, Niu L, et al. 2018 Neurocomputing 290 185

    [31]

    Zhang C, Cheng W, Hirakawa K 2019 IEEE Trans. Image Process. 28 1732

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出版历程
  • 收稿日期:  2020-03-04
  • 修回日期:  2020-04-27
  • 上网日期:  2020-05-08
  • 刊出日期:  2020-07-20

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