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基于彩色三要素的无参考对比度失真图像质量评价方法

丁盈秋 杨杨 成茗 张卫明

丁盈秋, 杨杨, 成茗, 等 . 基于彩色三要素的无参考对比度失真图像质量评价方法[J]. 北京航空航天大学学报, 2022, 48(8): 1418-1427. doi: 10.13700/j.bh.1001-5965.2021.0509
引用本文: 丁盈秋, 杨杨, 成茗, 等 . 基于彩色三要素的无参考对比度失真图像质量评价方法[J]. 北京航空航天大学学报, 2022, 48(8): 1418-1427. doi: 10.13700/j.bh.1001-5965.2021.0509
DING Yingqiu, YANG Yang, CHENG Ming, et al. No reference quality assessment method for contrast-distorted images based on three elements of color[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(8): 1418-1427. doi: 10.13700/j.bh.1001-5965.2021.0509(in Chinese)
Citation: DING Yingqiu, YANG Yang, CHENG Ming, et al. No reference quality assessment method for contrast-distorted images based on three elements of color[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(8): 1418-1427. doi: 10.13700/j.bh.1001-5965.2021.0509(in Chinese)

基于彩色三要素的无参考对比度失真图像质量评价方法

doi: 10.13700/j.bh.1001-5965.2021.0509
基金项目: 

安徽省高等学校自然科学基金 KJ2021A0016

国家自然科学基金 61502007

国家自然科学基金 61871411

详细信息
    通讯作者:

    杨杨, E-mail: sky_yang@ahu.edu.cn

  • 中图分类号: TP391

No reference quality assessment method for contrast-distorted images based on three elements of color

Funds: 

Natural Science Foundation of the Anhui Higher Education Institutions of China KJ2021A0016

National Natural Science Foundation of China 61502007

National Natural Science Foundation of China 61871411

More Information
  • 摘要:

    图像质量评价是图像处理领域中基本且具有挑战性的问题。对比度失真对图像质量的感知影响较大,目前针对对比度失真图像的无参考图像质量评价研究相对较少。基于此,提出了基于彩色三要素的无参考对比度失真图像质量评价方法,利用彩色三要素的亮度、色调和饱和度3个参数实现了对比度失真图像的质量评价方法。在亮度方面,提取矩特征及图像直方图与均匀分布之间的Kullback-Leibler散度特征。在色调和饱和度方面,分别在HSV空间的H和S通道中提取颜色加权局部二值模式(LBP)直方图特征。利用AdaBoosting BP神经网络训练预测模型。在5个标准图像数据库中进行广泛的实验分析和交叉验证,结果表明,所提方法与现有的对比度失真图像质量评价方法相比,性能有明显的提升。

     

  • 图 1  原图及其对比度失真版本图像的相关参数

    Figure 1.  Related parameters of original image and its contrast-distorted version

    图 2  本文方法流程

    Figure 2.  Flow chart of the proposed method

    图 3  图 1(a)~(c)所示图像的矩特征

    Figure 3.  Moment features of images shown in Fig. 1(a)-Fig. 1(c)

    图 4  均匀分布、原图及不同程度对比度失真图像的概率密度

    Figure 4.  Probability density of uniform distribution, original image and different degree of contrast distortion image

    图 5  H和S通道下LBP各模式的累积值

    Figure 5.  Accumulated amplitude of each mode of LBP under H and S channels

    图 6  AdaBoosting BP神经网络模型

    Figure 6.  AdaBoosting BP neural network model

    图 7  CID2013数据库上的消融结果

    Figure 7.  Ablation results on CID2013 database

    表  1  五个图像质量数据库的特征

    Table  1.   Features of five image quality databases

    数据库 原始图像数量 对比度失真图像数量 失真类型 图像尺寸 分数类型 主观分数范围
    CID2013[15] 15 400 1 768×512 MOS [1~5]
    CCID2014[4] 15 655 1 768×512 MOS [1~5]
    CSIQ[16] 30 116 6 512×512 DMOS [0~1]
    TID2008[17] 25 200 17 512×384 MOS [0~9]
    TID2013[18] 25 250 24 512×384 MOS [0~9]
    下载: 导出CSV

    表  2  使用不同散度的性能比较

    Table  2.   Performance comparison using different divergence

    散度类型 CID2013[15] CSIQ[16] TID2008[17]
    PLCC SRCC KRCC PLCC SRCC KRCC PLCC SRCC KRCC
    KL散度 0.930 0.926 0.768 0.949 0.915 0.767 0.897 0.860 0.686
    JS散度 0.935 0.931 0.777 0.947 0.931 0.777 0.896 0.862 0.683
    正逆KL散度 0.969 0.966 0.848 0.966 0.945 0.818 0.927 0.911 0.749
    注:黑体数据表示最好结果。
    下载: 导出CSV

    表  3  使用不同回归模型的性能比较

    Table  3.   Performance comparison using different regression models

    回归模型 CID2013[15] CSIQ[16] TID2008[17]
    PLCC SRCC KRCC PLCC SRCC KRCC PLCC SRCC KRCC
    SVR 0.906 0.895 0.718 0.905 0.851 0.708 0.912 0.867 0.693
    RF 0.921 0.905 0.739 0.920 0.871 0.712 0.922 0.881 0.703
    AdaBoosting BP神经网络 0.969 0.966 0.848 0.966 0.945 0.818 0.927 0.911 0.749
    注:黑体数据表示最好结果。
    下载: 导出CSV

    表  4  五个对比度失真图像数据库上本文方法和其他方法的性能比较

    Table  4.   Performance comparison of the proposed method and other methods on five contrast-distorted image databases

    数据库 秩相关系数 FR RR
    QMC[1] PCQI[2] QCCI[3] RIQMC[4] RCIQM[5] CIQM[6]
    CID2013[15] PLCC 0.806 0.924 6 0.934 5 0.899 5 0.918 7 0.913 9
    SRCC 0.767 4 0.923 2 0.929 3 0.900 5 0.920 3 0.920 6
    KRCC 0.578 5 0.758 0 0.762 0 0.716 2 0.754 3 0.724 0
    CCID2014[4] PLCC 0.895 2 0.872 1 0.888 0 0.872 6 0.884 5 0.885 3
    SRCC 0.870 5 0.886 9 0.895 7 0.846 5 0.856 5 0.869 7
    KRCC 0.684 6 0.682 0 0.702 1 0.650 7 0.669 5 0.685 4
    CSIQ[16] PLCC 0.960 5 0.948 2 0.946 6 0.965 2 0.964 5 0.946 2
    SRCC 0.953 2 0.948 8 0.951 2 0.957 9 0.956 9 0.949 6
    KRCC 0.816 5 0.814 4 0.798 2 0.827 9 0.819 8 0.810 5
    TID2008[17] PLCC 0.803 6 0.882 1 0.881 4 0.858 5 0.880 7 0.892 2
    SRCC 0.752 9 0.900 2 0.898 9 0.809 5 0.857 8 0.868 1
    KRCC 0.571 9 0.722 6 0.711 9 0.622 4 0.670 5 0.689 0
    TID2013[18] PLCC 0.797 2 0.873 8 0.873 3 0.865 1 0.886 6 0.897 0
    SRCC 0.733 6 0.917 5 0.912 6 0.804 4 0.854 1 0.862 1
    KRCC 0.551 3 0.709 3 0.685 4 0.617 8 0.667 5 0.687 3
    加权平均值 PLCC 0.851 4 0.892 0 0.900 6 0.883 0 0.898 5 0.899 4
    SRCC 0.815 3 0.906 6 0.911 0 0.856 7 0.879 2 0.886 6
    KRCC 0.633 4 0.719 4 0.722 4 0.671 0 0.701 0 0.704 6
    数据库 秩相关系数 NR
    CDIQA[7] ICDIQA[8] NIQMC[9] BIQME[10] HEFCS[11] 文献[12] 文献[13] 本文方法
    CID2013[15] PLCC 0.866 8 0.912 9 0.869 1 0.900 4 0.897 3 0.943 5 0.964 6 0.968 9
    SRCC 0.850 0 0.908 1 0.866 8 0.902 3 0.877 7 0.933 8 0.960 3 0.966 2
    KRCC 0.658 8 0.703 5 0.669 0 0.722 3 0.690 6 0.781 4 0.835 4 0.848 0
    CCID2014[4] PLCC 0.837 1 0.877 9 0.843 8 0.858 8 0.865 0 0.923 5 0.910 9 0.925 4
    SRCC 0.802 6 0.851 2 0.811 3 0.830 9 0.842 6 0.911 8 0.902 3 0.914 5
    KRCC 0.603 6 0.659 8 0.605 2 0.630 5 0.639 5 0.723 6 0.728 5 0.748 3
    CSIQ[16] PLCC 0.666 3 0.881 7 0.874 7 0.810 6 0.941 7 0.936 8 0.926 9 0.965 5
    SRCC 0.585 6 0.814 5 0.853 3 0.784 8 0.903 9 0.887 6 0.895 3 0.944 7
    KRCC 0.439 0 0.690 3 0.668 9 0.598 3 0.752 4 0.729 0 0.731 2 0.818 2
    TID2008[17] PLCC 0.632 0 0.756 8 0.776 7 0.899 3 0.865 0 0.865 4 0.876 3 0.926 5
    SRCC 0.572 3 0.703 6 0.732 4 0.848 8 0.804 2 0.800 3 0.817 6 0.911 3
    KRCC 0.425 3 0.498 9 0.541 9 0.646 0 0.630 2 0.609 8 0.638 9 0.748 7
    TID2013[18] PLCC 0.579 8 0.696 3 0.722 5 0.852 4 0.844 3 0.895 7 0.911 1 0.951 0
    SRCC 0.508 2 0.642 9 0.645 8 0.814 9 0.749 9 0.840 1 0.853 0 0.924 0
    KRCC 0.362 8 0.453 6 0.468 7 0.610 9 0.568 7 0.659 5 0.688 7 0.768 0
    加权平均值 PLCC 0.767 2 0.843 9 0.825 3 0.869 6 0.875 3 0.917 9 0.921 1 0.943 1
    SRCC 0.724 9 0.812 3 0.792 7 0.845 0 0.836 6 0.890 7 0.898 1 0.930 5
    KRCC 0.546 3 0.621 1 0.596 6 0.649 7 0.648 1 0.714 3 0.737 9 0.781 0
    注:黑体数据表示最好结果。
    下载: 导出CSV

    表  5  跨数据库验证的性能

    Table  5.   Performance of cross-database verification

    数据库 秩相关系数 CID2013[15] CCID2014[4] CSIQ[16] TID2008[17] TID2013[18]
    PLCC 0.922 0.663 0.580 0.540
    CID2013[15] SRCC 0 0.902 0.653 0.556 0.503
    KRCC 0.733 0.485 0.398 0.361
    PLCC[16] 0.966 0.649 0.541 0.503
    CCID2014[4] SRCC 0.965 0 0.626 0.500 0.458
    KRCC 0.839 0.434 0.350 0.317
    PLCC 0.588 0.540 0.771 0.769
    CSIQ[16] SRCC 0.586 0.443 0 0.701 0.670
    KRCC 0.401 0.302 0.520 0.490
    PLCC 0.475 0.468 0.858 0.955
    TID2008[17] SRCC 0.387 0.364 0.853 0 0.932
    KRCC 0.277 0.260 0.641 0.787
    PLCC 0.503 0.502 0.806 0.956
    TID2013[18] SRCC 0.364 0.323 0.779 0.948 0
    KRCC 0.261 0.248 0.553 0.809
    下载: 导出CSV

    表  6  三个数据库上不同训练集和测试集比例性能比较

    Table  6.   Performance comparison of different training set and test set ratios on three databases

    测试集比例/% CID2013[15] CSIQ[16] TID2008[17]
    PLCC SRCC KRCC PLCC SRCC KRCC PLCC SRCC KRCC
    20 0.903 0.904 0.727 0.766 0.755 0.553 0.828 0.789 0.601
    50 0.951 0.952 0.808 0.905 0.893 0.712 0.897 0.872 0.694
    80 0.969 0.966 0.848 0.966 0.945 0.818 0.927 0.911 0.749
    注:黑体数据表示最好结果。
    下载: 导出CSV

    表  7  在CSIQ数据库上不同失真类型图像的性能比较

    Table  7.   Performance comparison of different distortion types in CSIQ database

    秩相关系数 JPEG JPEG2K GB WGN PGN GCD
    PLCC 0.913 0.891 0.908 0.942 0.931 0.966
    SRCC 0.862 0.852 0.867 0.934 0.907 0.945
    KRCC 0.688 0.684 0.702 0.801 0.773 0.818
    注:黑体数据表示最好结果。
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-09-02
  • 录用日期:  2021-09-17
  • 刊出日期:  2021-11-02

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