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摘要: 图像的模糊问题影响人们对信息的感知、获取及图像的后续处理. 无参考模糊图像质量评价是该问题的主要研究方向之一. 本文分析了近20年来无参考模糊图像质量评价相关技术的发展. 首先, 本文结合主要数据集对图像模糊失真进行分类说明; 其次, 对主要的无参考模糊图像质量评价方法进行分类介绍与详细分析; 随后, 介绍了用来比较无参考模糊图像质量评价方法性能优劣的主要评价指标; 接着, 选择典型数据集及评价指标, 并采用常见的无参考模糊图像质量评价方法进行性能比较; 最后, 对无参考模糊图像质量评价的相关技术及发展趋势进行总结与展望.Abstract: The blurriness distortion of image affects information perception, acquisition and subsequent processing. No-reference blurred image quality assessment is one of main research directions for the problem. This paper analyzes the relevant technique development of no-reference blurred image quality assessment in recent 20 years. Firstly, combining with main databases, different types of blurriness distortions are described. Secondly, main methods for no-reference blurred image quality assessment are classified and analyzed in detail. Thirdly, performance measures for no-reference blurred image assessment are introduced. Then, the typical databases, performance measures and methods are introduced for performance comparisons. Finally, the relevant technologies and development trends of no-reference blurred image assessment are summarized and prospected.
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图 2 基于空域/频域的NR-IQA方法分类
Fig. 2 Classification of spatial/spectral domain-based NR-IQA methods
图 4 不同类型NR-IQA方法在不同人工模糊数据集中平均性能评价指标值比较
Fig. 4 Average performance evaluation result comparison through different types of NR-IQA methods for different artificial blur databases
图 5 不同类型NR-IQA方法在不同自然模糊数据集中平均性能评价指标值比较
Fig. 5 Average performance evaluation result comparison through different types of NR-IQA methods for different natural blur databases
表 1 含有模糊图像的主要图像质量评价数据集
Table 1 Main image quality assessment databases including blurred images
数据集 时间 参考图像 模糊图像 模糊类型 主观评价 分值范围 IVC[28] 2005 4 20 高斯模糊 MOS 模糊−清晰 [1 5] LIVE[22] 2006 29 145 高斯模糊 DMOS 清晰−模糊 [0 100] A57[30] 2007 3 9 高斯模糊 DMOS 清晰−模糊 [0 1] TID2008[26] 2009 25 100 高斯模糊 MOS 模糊−清晰 [0 9] CSIQ[25] 2009 30 150 高斯模糊 DMOS 清晰−模糊 [0 1] VCL@FER[29] 2012 23 138 高斯模糊 MOS 模糊−清晰 [0 100] TID2013[27] 2013 25 125 高斯模糊 MOS 模糊−清晰 [0 9] KADID-10k 1[31] 2019 81 405 高斯模糊 MOS 模糊−清晰 [1 5] KADID-10k 2[31] 2019 81 405 镜头模糊 MOS 模糊−清晰 [1 5] KADID-10k 3[31] 2019 81 405 运动模糊 MOS 模糊−清晰 [1 5] MLIVE1[33] 2012 15 225 高斯模糊和高斯白噪声 DMOS 清晰−模糊 [0 100] MLIVE2[33] 2012 15 225 高斯模糊和JEPG压缩 DMOS 清晰−模糊 [0 100] MDID2013[32] 2013 12 324 高斯模糊、JEPG压缩和白噪声 DMOS 清晰−模糊 [0 1] MDID[34] 2017 20 1600 高斯模糊、对比度变化、高斯噪声、
JPEG或JPEG2000MOS 模糊−清晰 [0 8] BID[21] 2011 — 586 自然模糊 MOS 模糊−清晰 [0 5] CID2013[35] 2013 — 480 自然模糊 MOS 模糊−清晰 [0 100] CLIVE[36-37] 2016 — 1162 自然模糊 MOS 模糊−清晰 [0 100] KonIQ-10k [38] 2018 — 10073 自然模糊 MOS 模糊−清晰 [1 5] 
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表 2 基于空域/频域的不同方法优缺点对比
Table 2 Advantage and disadvantage comparison for different methods based on spatial/spectral domain
方法分类 优点 缺点 边缘信息 概念直观、计算复杂度低 容易因图像中缺少锐利边缘而影响评价结果 再模糊理论 对图像内容依赖小, 计算复杂度低 准确性依赖 FR-IQA 方法 奇异值分解 能较好地提取图像结构、边缘、纹理信息 计算复杂度较高 自由能理论 外部输入信号与其生成模型可解释部分之间的
差距与视觉感受的图像质量密切相关计算复杂度高 DFT/DCT/小波变换 综合了图像的频域特性和多尺度特征, 准确性和鲁棒性更高 计算复杂度高
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表 3 基于学习的不同方法优缺点对比
Table 3 Advantage and disadvantage comparison for different methods based on learning
方法分类 优点 缺点 SVM 在小样本训练集上能够取得比其他算法更好的效果 评价结果的好坏由提取的特征决定 NN 具有很好的非线性映射能力 样本较少时, 容易出现过拟合现象, 且
计算复杂度随着数据量的增加而增大深度学习 可以从大量数据中自动学习图像特征的多层表示 对数据集中数据量要求大 字典/码本 可以获得图像中的高级特征 字典/码本的大小减小时, 性能显著下降 MVG 无需图像的 MOS/DMOS 值 模型建立困难, 对数据集中数据量要求较大
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表 4 用于对比的不同NR-IQA方法
Table 4 Different NR-IQA methods for comparison
方法类别 方法 特征 模糊/通用 空域/频域 空域 边缘信息 JNB[43] 计算边缘分块所对应的边缘宽度 模糊 边缘信息 CPBD[44] 计算模糊检测的累积概率 模糊 边缘信息 MLV[47] 计算图像的最大局部变化得到反映图像对比度信息的映射图 模糊 自由能理论 ARISM[63] 每个像素 AR 模型系数的能量差和对比度差 模糊 边缘信息 BIBLE[49] 图像的梯度和 Tchebichef 矩量 模糊 边缘信息 Zhan 等[14] 图像中最大梯度及梯度变化量 模糊 频域 DFT变换 S3[65] 在频域测量幅度谱的斜率, 在空域测量空间变化情况 模糊 小波变换 LPC-SI[81] LPC 强度变化作为指标 模糊 小波变换 BISHARP[77] 计算图像的均方根来获取图像局部对比度信息,
同时利用小波变换中对角线小波系数模糊 HVS滤波器 HVS-MaxPol[85] 利用 MaxPol 卷积滤波器分解与图像清晰度相关的有意义特征 模糊 学习 机器学习 SVM+SVR BIQI[86] 对图像进行小波变换后, 利用 GGD 对得到的子带系数进行参数化 通用 SVM+SVR DIIVINE[87] 从小波子带系数中提取一系列的统计特征 通用 SVM+SVR SSEQ[88] 空间−频域熵特征 通用 SVM+SVR BLIINDS-II[91] 多尺度下的广义高斯模型形状参数特征、频率变化系数特征、
能量子带特征、基于定位模型的特征通用 SVR BRISQUE[96] GGD 拟合 MSCN 系数作为特征, AGGD 拟合 4 个相邻元素乘积系数作为特征 通用 SVR RISE[107] 多尺度图像空间中的梯度值和奇异值特征, 以及多分辨率图像的熵特征 模糊 SVR Liu 等[109] 局部模式算子提取图像结构信息, Toggle 算子提取边缘信息 模糊 SVR Cai 等[110] 输入图像与其重新模糊版本之间的 Log-Gabor 滤波器响应差异和基于方向
选择性的模式差异, 以及输入图像与其 4 个下采样图像之间的自相似性模糊 深度学习 CNN Kang＇s CNN[116] 对图像分块进行局部对比度归一化 通用 浅层CNN+GRNN Yu＇s CNN[127] 对图像分块进行局部对比度归一化 模糊 聚类技术+RBM MSFF[139] Gabor 滤波器提取不同方向和尺度的原始图像特征,
然后由 RBMs 生成特征描述符通用 DNN MEON[132] 原始图像作为输入 通用 CNN DIQaM-NR[131] 使用 CNN 提取失真图像块和参考图像块的特征 通用 CNN DIQA[118] 图像归一化后, 通过下采样及上采样得到低频图像 通用 CNN SGDNet[133] 使用 DCNN 作为特征提取器获取图像特征 通用 秩学习 Rank Learning[141] 选取一定比例的图像块集合作为输入, 梯度信息被用来指导图像块选择过程 模糊 DCNN+SFA SFA[128] 多个图像块作为输入, 并使用预先训练好的 DCNN 模型提取特征 模糊 DNN+NSS NSSADNN[134] 每个图像块归一化后用 CNNs 提取特征, 得到 1024 维向量 通用 CNN DB-CNN[123] 用预训练的 S-CNN 及 VGG-16 分别提取合成失真与真实图像的相关特征 通用 CNN CGFA-CNN[124] 用 VGG-16 以提取失真图像的相关特征 通用 字典/码本 聚类算法+码本 CORNIA[145] 未标记图像块中提取局部特征进行 K-means 聚类以构建码本 通用 聚类算法+码本 QAC[147] 用比例池化策略估计每个分块的局部质量,
通过 QAC 学习不同质量级别上的质心作为码本通用 稀疏学习+字典 SPARISH[143] 以图像块的方式表示模糊图像, 并使用稀疏系数计算块能量 模糊 MVG MVG模型 NIQE[150] 提取 MSCN 系数, 再用 GGD 和 AGGD 拟合得到特征 通用
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表 5 基于深度学习的方法所采用的不同网络结构
Table 5 Different network structures of deep learning-based methods
方法 网络结构 Kang＇s CNN[116] 包括一个含有最大/最小池化的卷积层, 两个全连接层及一个输出结点 Yu＇s CNN[127] 采用单一特征层挖掘图像内在特征, 利用 GRNN 评价图像质量 MSFF[139] 图像的多个特征作为输入, 通过端到端训练学习特征权重 MEON[132] 由失真判别网络和质量预测网络两个子网络组成, 并采用 GDN 作为激活函数 DIQaM-NR[131] 包含 10 个卷积层和 5 个池化层用于特征提取, 以及 2 个全连接层进行回归分析 DIQA[118] 网络训练分为客观失真部分及与人类视觉系统相关部分两个阶段 SGDNet[133] 包括视觉显著性预测和图像质量预测的两个子任务 Rank Learning[141] 结合了 Siamese Mobilenet 及多尺度 patch 提取方法 SFA[128] 包括 4 个步骤: 图像的多 patch 表示, 预先训练好的 DCNN 模型提取特征,
通过 3 种不同统计结构进行特征聚合, 部分最小二乘回归进行质量预测NSSADNN[134] 采用多任务学习方式设计, 包括自然场景统计 (NSS) 特征预测任务和质量分数预测任务 DB-CNN[123] 两个卷积神经网络分别专注于两种失真图像特征提取, 并采用双线性池化实现质量预测 CGFA-CNN[124] 采用两阶段策略, 首先基于 VGG-16 网络的子网络 1 识别图像中的失真类型, 而后利用子网络 2 实现失真量化
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表 6 基于空域/频域的不同NR-IQA方法在不同数据集中比较结果
Table 6 Comparison of different spatial/spectral domain-based NR-IQA methods for different databases
方法 发表时间 LIVE CSIQ PLCC SROCC RMSE MAE PLCC SROCC RMSE MAE JNB[43] 2009 0.843 0.842 11.706 9.241 0.786 0.762 0.180 0.122 CPBD[44] 2011 0.913 0.943 8.882 6.820 0.874 0.885 0.140 0.111 S3[65] 2012 0.919 0.963 8.578 7.335 0.894 0.906 0.135 0.110 LPC-SI[81] 2013 0.907 0.923 9.177 7.275 0.923 0.922 0.111 0.093 MLV[47] 2014 0.959 0.957 6.171 4.896 0.949 0.925 0.091 0.071 ARISM[63] 2015 0.962 0.968 5.932 4.512 0.944 0.925 0.095 0.076 BIBLE[49] 2016 0.963 0.973 5.883 4.605 0.940 0.913 0.098 0.077 Zhan 等[14] 2018 0.960 0.963 6.078 4.697 0.967 0.950 0.073 0.057 BISHARP[77] 2018 0.952 0.960 6.694 5.280 0.942 0.927 0.097 0.078 HVS-MaxPol[85] 2019 0.957 0.960 6.318 5.076 0.943 0.921 0.095 0.077 方法 发表时间 TID2008 TID2013 PLCC SROCC RMSE MAE PLCC SROCC RMSE MAE JNB[43] 2009 0.661 0.667 0.881 0.673 0.695 0.690 0.898 0.687 CPBD[44] 2011 0.820 0.841 0.672 0.524 0.854 0.852 0.649 0.526 S3[65] 2012 0.851 0.842 0.617 0.478 0.879 0.861 0.595 0.480 LPC-SI[81] 2013 0.861 0.896 0.599 0.478 0.869 0.919 0.621 0.507 MLV[47] 2014 0.858 0.855 0.602 0.468 0.883 0.879 0.587 0.460 ARISM[63] 2015 0.843 0.851 0.632 0.492 0.895 0.898 0.558 0.442 BIBLE[49] 2016 0.893 0.892 0.528 0.413 0.905 0.899 0.531 0.426 Zhan 等[14] 2018 0.937 0.942 0.410 0.320 0.954 0.961 0.374 0.288 BISHARP[77] 2018 0.877 0.880 0.564 0.439 0.892 0.896 0.565 0.449 HVS-MaxPol[85] 2019 0.853 0.851 0.612 0.484 0.877 0.875 0.599 0.484
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表 7 基于学习的不同NR-IQA方法在不同人工模糊数据集中比较结果
Table 7 Comparison of different learning-based NR-IQA methods for different artificial blur databases
方法 发表
时间LIVE CSIQ TID2008 TID2013 PLCC SROCC PLCC SROCC PLCC SROCC PLCC SROCC BIQI[86] 2010 0.920 0.914 0.846 0.773 0.794 0.799 0.825 0.815 DIIVINE[87] 2011 0.943 0.936 0.886 0.879 0.835 0.829 0.847 0.842 BLIINDS-II[91] 2012 0.939 0.931 0.886 0.892 0.842 0.859 0.857 0.862 BRISQUE[96] 2012 0.951 0.943 0.921 0.907 0.866 0.865 0.862 0.861 CORNIA[145] 2012 0.968 0.969 0.781 0.714 0.932 0.932 0.904 0.912 NIQE[150] 2013 0.939 0.930 0.918 0.891 0.832 0.823 0.816 0.807 QAC[147] 2013 0.916 0.903 0.831 0.831 0.813 0.812 0.848 0.847 SSEQ[88] 2014 0.961 0.948 0.871 0.870 0.858 0.852 0.863 0.862 Kang＇s CNN[116] 2014 0.963 0.983 0.774 0.781 0.880 0.850 0.931 0.922 SPARISH[143] 2016 0.960 0.960 0.939 0.914 0.896 0.896 0.902 0.894 Yu＇s CNN[127] 2017 0.973 0.965 0.942 0.925 0.937 0.919 0.922 0.914 RISE[107] 2017 0.962 0.949 0.946 0.928 0.929 0.922 0.942 0.934 MEON[132] 2018 0.948 0.940 0.916 0.905 — — 0.891 0.880 DIQaM-NR[131] 2018 0.972 0.960 0.893 0.885 — — 0.915 0.908 DIQA[118] 2019 0.952 0.951 0.871 0.865 — — 0.921 0.918 SGDNet[133] 2019 0.946 0.939 0.866 0.860 — — 0.928 0.914 Rank Learning[141] 2019 0.969 0.954 0.979 0.953 0.959 0.949 0.965 0.955 SFA[128] 2019 0.972 0.963 — — 0.946 0.937 0.954 0.948 NSSADNN[134] 2019 0.971 0.981 0.923 0.930 — — 0.857 0.840 CGFA-CNN[124] 2020 0.974 0.968 0.955 0.941 — — — — MSFF[139] 2020 0.954 0.962 — — 0.925 0.928 0.921 0.928 DB-CNN[123] 2020 0.956 0.935 0.969 0.947 — — 0.857 0.844 Liu 等[109] 2020 0.980 0.973 0.955 0.936 — — 0.972 0.964 Cai 等[110] 2020 0.958 0.955 0.952 0.923 — — 0.957 0.941
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表 8 基于学习的不同NR-IQA方法在不同自然模糊数据集中比较结果
Table 8 Comparison of different learning-based NR-IQA methods for different natural blur databases
方法 发表
时间BID CID2013 CLIVE PLCC SROCC PLCC SROCC PLCC SROCC BIQI[86] 2010 0.604 0.572 0.777 0.744 0.540 0.519 DIIVINE[87] 2011 0.506 0.489 0.499 0.477 0.558 0.509 BLIINDS-II[91] 2012 0.558 0.530 0.731 0.701 0.507 0.463 BRISQUE[96] 2012 0.612 0.590 0.714 0.682 0.645 0.607 CORNIA[145] 2012 — — 0.680 0.624 0.665 0.618 NIQE[150] 2013 0.471 0.469 0.693 0.633 0.478 0.421 QAC[147] 2013 0.321 0.318 0.187 0.162 0.318 0.298 SSEQ[88] 2014 0.604 0.581 0.689 0.676 — — Kang＇s CNN[116] 2014 0.498 0.482 0.523 0.526 0.522 0.496 SPARISH[143] 2016 0.356 0.307 0.678 0.661 0.484 0.402 Yu＇s CNN[127] 2017 0.560 0.557 0.715 0.704 0.501 0.502 RISE[107] 2017 0.602 0.584 0.793 0.769 0.555 0.515 MEON[132] 2018 0.482 0.470 0.703 0.701 0.693 0.688 DIQaM-NR[131] 2018 0.476 0.461 0.686 0.674 0.601 0.606 DIQA[118] 2019 0.506 0.492 0.720 0.708 0.704 0.703 SGDNet[133] 2019 0.422 0.417 0.653 0.644 0.872 0.851 Rank Learning[141] 2019 0.751 0.719 0.863 0.836 — — SFA[128] 2019 0.840 0.826 — — 0.833 0.812 NSSADNN[134] 2019 0.574 0.568 0.825 0.748 0.813 0.745 CGFA-CNN[124] 2020 — — — — 0.846 0.837 DB-CNN[123] 2020 0.475 0.464 0.686 0.672 0.869 0.851 Cai 等[110] 2020 0.633 0.603 0.880 0.874 — —
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