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模糊失真图像无参考质量评价综述

陈健 李诗云 林丽 王猛 李佐勇

陈健, 李诗云, 林丽, 王猛, 李佐勇. 模糊失真图像无参考质量评价综述. 自动化学报, 2021, x(x): 1−23 doi: 10.16383/j.aas.c201030
引用本文: 陈健, 李诗云, 林丽, 王猛, 李佐勇. 模糊失真图像无参考质量评价综述. 自动化学报, 2021, x(x): 1−23 doi: 10.16383/j.aas.c201030
Chen Jian, Li Shi-Yun, Lin Li, Wang Meng, Li Zuo-Yong. A review on no-reference quality assessment for blurred image. Acta Automatica Sinica, 2021, x(x): 1−23 doi: 10.16383/j.aas.c201030
Citation: Chen Jian, Li Shi-Yun, Lin Li, Wang Meng, Li Zuo-Yong. A review on no-reference quality assessment for blurred image. Acta Automatica Sinica, 2021, x(x): 1−23 doi: 10.16383/j.aas.c201030

模糊失真图像无参考质量评价综述

doi: 10.16383/j.aas.c201030
基金项目: 国家自然科学基金(61972187), 福建省自然科学基金重点项目(2020J02024), 福建省自然科学基金面上项目(2018J01637), 福州市科技计划项目(2020-RC-186), 福建省信息处理与智能控制重点实验室(闽江学院)开放课题(MJUKF-IPIC202110)资助
详细信息
    作者简介:

    陈健:福建工程学院电子电气与物理学院副教授. 2015年获得福州大学通信与信息系统专业博士学位. 研究方向为计算机视觉, 深度学习, 医学图像处理与分析. 本文通信作者. E-mail: jchen321@126.com

    李诗云:福建工程学院电子电气与物理学院硕士研究生. 主要研究方向为图像处理和机器学习. E-mail: 13997691527@163.com

    林丽:福建工程学院电子电气与物理学院讲师. 2009年获得福州大学信号与信息处理专业硕士学位. 主要研究方向为机器视觉及信号处理. E-mail: linli@fjut.edu.cn

    王猛:福建工程学院电子电气与物理学院硕士研究生. 主要研究方向为计算机视觉. E-mail: wm15720503705@163.com

    李佐勇:闽江学院计算机与控制工程学院教授. 2010年获得南京理工大学计算机应用专业博士学位. 主要研究方向为图像处理, 模式识别及深度学习. Email: fzulzytdq@126.com

A Review on No-reference Quality Assessment for Blurred Image

Funds: Supported by National Natural Science Foundation of P. R. China (61972187), Natural Science Foundation of Fujian Province (2020J02024), Natural Science Foundation of Fujian Province (2018J01637), Fuzhou Science and Technology Project (2020-RC-186), Open Fund Project of Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) (MJUKF-IPIC202110)
More Information
    Author Bio:

    CHEN Jian Associate Professor at the School of Electronic, Electrical Engineering and Physics, Fujian University of Technology. He received his Ph. D. degree in communication and information system from Fuzhou University in 2015. His research covers computer vision, deep learning, medical image processing and analysis. Corresponding author of this paper

    LI Shi-Yun Master student at the School of Electronic, Electrical Engineering and Physics, Fujian University of Technology. His research covers image processing and machine learning

    LIN Li Lecturer at the School of Electronic, Electrical and Physics, Fujian University of Technology. She received her master degree in signal and information processing from Fuzhou University in 2009. Her research covers machine vision and signal processing

    WANG Meng Master student at the School of Electronic, Electrical Engineering and Physics, Fujian University of Technology. His research covers computer vision

    LI Zuo-Yong Professor at the College of Computer and Control Engineering, Minjiang University, Fuzhou. He received his Ph.D. degree in computer application from Nanjing University of Science and Technology in 2010. His research covers image processing, pattern recognition, and deep learning

  • 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年来模糊图像无参考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, 对主要的模糊图像无参考质量评价方法进行分类介绍与详细分析; 随后, 介绍了用来衡量模糊图像无参考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, 并采用常见的模糊图像无参考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.
  • 图  1  不同类型模糊图像示例

    Fig.  1  Examples for different kinds of blurred images

    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  3  基于学习的NR-IQA方法分类

    Fig.  3  Classification of learning-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]2005420高斯模糊MOS模糊-清晰 [1 5]
    LIVE [22]200629174高斯模糊DMOS清晰-模糊 [0 100]
    A57 [30]200739高斯模糊MOS清晰-模糊 [0 1]
    TID2008 [26]200925100高斯模糊MOS模糊-清晰 [0 9]
    CSIQ [25]200930150高斯模糊DMOS清晰-模糊 [0 1]
    VCL@FER [29]201223138高斯模糊MOS模糊-清晰 [0 100]
    TID2013 [27]201325125高斯模糊MOS模糊-清晰 [0 9]
    KADID-10k 1 [31]201981405高斯模糊DMOS模糊-清晰 [1 5]
    KADID-10k 2 [31]201981405镜头模糊DMOS模糊-清晰 [1 5]
    KADID-10k 3 [31]201981405运动模糊DMOS模糊-清晰 [1 5]
    MLIVE1 [33]201215225高斯模糊和高斯白噪声DMOS清晰-模糊 [0 100]
    MLIVE2 [33]201215225高斯模糊和JEPG压缩DMOS清晰-模糊 [0 100]
    MDID2013 [32]201312324高斯模糊、JEPG压缩和白噪声DMOS清晰-模糊 [0 1]
    MDID [34]2017201600高斯模糊、对比度变化、高斯噪声、JPEG或JPEG2000MOS模糊-清晰 [0 8]
    BID [21]2011585自然模糊MOS模糊-清晰 [0 5]
    CID2013 [35]2013480自然模糊MOS模糊-清晰 [0 100]
    CLIVE [36,37]20161169自然模糊MOS模糊-清晰 [1 100]
    KonIQ-10k [38]201810073自然模糊MOS模糊-清晰 [1 100]
    下载: 导出CSV

    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
    奇异值分解能较好的提取图像结构、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
    下载: 导出CSV

    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
    下载: 导出CSV

    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]计算边缘分块的局部对比度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[14]图像中最大梯度及梯度变化量模糊
    频域


    DFT变换S3[65]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+SVRBIQI[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, 作为特征通用
    SVM+SVRDIIVINE[87]从小波子带系数中提取一系列的统计特征通用
    SVM+SVRSSEQ[88]空间熵和光谱熵特征通用
    SVM+SVRBLIINDS-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
    SVRBRISQUE[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
    SVRRISE[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
    SVRLiu[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
    SVRCai[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, 以及输入图像与其四个下采样图像之间的自相似性
    模糊
    深度学习CNNKang’s CNN[116]对图像分块进行局部对比度归一化通用
    浅层CNN+GRNNYu’s CNN[127]对图像分块进行局部对比度归一化模糊
    聚类技术+RBMMSFF[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
    DNNMEON[132]原始图像作为输入通用
    CNNDIQaM-NR[131]使用CNN提取失真图像块和参考图像块的特征通用
    CNNDIQA[118]图像归一化后, 通过下采样及上采样得到低频图像通用
    CNNSGDNet[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+SFASFA[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+NSSNSSADNN[134]每个图像块归一化后用CNNs提取特征, 得到1024维向量通用
    CNNDB-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
    CNNCGFA-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]用百分比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
    MVGMVG模型NIQE[150]提取MSCN系数, 再用GGD和AGGD拟合得到特征通用
    下载: 导出CSV

    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]包括四个步骤: 图像的多patch表示, 预先训练好的DCNN模型提取特征,
    通过三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
    下载: 导出CSV

    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

    数据集发表时间
    LIVECSIQ
    方法PLCCSROCCRMSEMAEPLCCSROCCRMSEMAE
    JNB[43]20090.8430.84211.7069.2410.7860.7620.1800.122
    CPBD[44]20110.9130.9438.8826.8200.8740.8850.1400.111
    S3[65]20120.9190.9638.5787.3350.8940.9060.1350.110
    LPC-SI[81]20130.9070.9239.1777.2750.9230.9220.1110.093
    MLV[47]20140.9590.9576.1714.8960.9490.9250.0910.071
    ARISM[63]20150.9620.9685.9324.5120.9440.9250.0950.076
    BIBLE[49]20160.9630.9735.8834.6050.9400.9130.0980.077
    Zhan[14]20170.9600.9636.0784.6970.9670.9500.0730.057
    BISHARP[77]20180.9520.9606.6945.2800.9420.9270.0970.078
    HVS-MaxPol[85]20190.9570.9606.3185.0760.9430.9210.0950.077
    数据集发表时间
    TID2008TID2013
    方法PLCCSROCCRMSEMAEPLCCSROCCRMSEMAE
    JNB[43]20090.6610.6670.8810.6730.6950.6900.8980.687
    CPBD[44]20110.8200.8410.6720.5240.8540.8520.6490.526
    S3[65]20120.8510.8420.6170.4780.8790.8610.5950.480
    LPC-SI[81]20130.8610.8960.5990.4780.8690.9190.6210.507
    MLV[47]20140.8580.8550.6020.4680.8830.8790.5870.460
    ARISM[63]20150.8430.8510.6320.4920.8950.8980.5580.442
    BIBLE[49]20160.8930.8920.5280.4130.9050.8990.5310.426
    Zhan[14]20170.9370.9420.4100.3200.9540.9610.3740.288
    BISHARP[77]20180.8770.8800.5640.4390.8920.8960.5650.449
    HVS-MaxPol[85]20190.8530.8510.6120.4840.8770.8750.5990.484
    下载: 导出CSV

    表  7  基于学习的不同NR-IQA方法在人工模糊不同数据集中比较结果

    Table  7  Comparison of different learning-based NR-IQA methods for different artificial blur databases

    数据集发表
    时间
    LIVECSIQTID2008TID2013
    方法PLCCSROCCPLCCSROCCPLCCSROCCPLCCSROCC
    BIQI[86]20100.9200.9140.8460.7730.7940.7990.8250.815
    DIIVINE[87]20110.9430.9360.8860.8790.8350.8290.8470.842
    BLIINDS-II[91]20120.9390.9310.8860.8920.8420.8590.8570.862
    BRISQUE[96]20120.9510.9430.9210.9070.8660.8650.8620.861
    CORNIA[145]20120.9680.9690.7810.7140.9090.9120.9040.912
    NIQE[150]20130.9390.9300.9180.8910.8320.8230.8160.807
    QAC[147]20130.9160.9030.8310.8310.8130.8120.8480.847
    SSEQ[88]20140.9610.9480.8710.8700.8580.8520.8630.862
    Kang’s CNN[116]20140.9630.9830.7740.7810.8800.8500.9310.922
    SPARISH[143]20160.9600.9600.9390.9140.8960.8960.9020.894
    Yu’s CNN[127]20170.9730.9650.9420.9250.9370.9190.9220.914
    RISE[107]20170.9620.9490.9460.9280.9290.9220.9420.934
    MEON[132]20180.9480.9400.9160.9050.8910.880
    DIQaM-NR[131]20180.9720.9600.8930.8850.9150.908
    DIQA[118]20190.9520.9510.8710.8650.9210.918
    SGDNet[133]20190.9460.9390.8660.8600.9280.914
    Rank Learning[141]20190.9690.9540.9790.9530.9590.9490.9650.955
    SFA[128]20190.9720.9630.9460.9370.9540.948
    NSSADNN[134]20190.9840.9860.9270.8930.9100.844
    CGFA-CNN[124]20200.9740.9680.9550.941
    MSFF[139]20200.9540.9620.9250.9280.9210.928
    DB-CNN[123]20200.9560.9350.9690.9470.8570.844
    Liu[109]20200.9800.9730.9550.9360.9720.964
    Cai[110]20200.9580.9550.9520.9230.9570.941
    下载: 导出CSV

    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

    数据集发表
    时间
    BIDCID2013CLIVE
    方法PLCCSROCCPLCCSROCCPLCCSROCC
    BIQI[86]20100.6040.5720.7770.7440.5400.519
    DIIVINE[87]20110.5060.4890.4990.4770.5580.509
    BLIINDS-II[91]20140.5580.5300.7310.7010.5070.463
    BRISQUE[96]20120.6120.5900.7140.6820.6450.607
    CORNIA[145]20120.6800.6240.6650.618
    NIQE[150]20120.4710.4690.6930.6330.4780.421
    QAC[147]20130.3210.3180.1870.1620.3180.298
    SSEQ[88]20130.6040.5810.6890.676
    Kang’s CNN[116]20140.4980.4820.5230.5260.5220.496
    SPARISH[143]20160.3560.3070.6780.6610.4840.402
    Yu’s CNN[127]20170.5600.5570.7150.7040.5010.502
    RISE[107]20170.6020.5840.7930.7690.5550.515
    MEON[132]20180.4820.4700.7030.7010.6930.688
    DIQaM-NR[131]20180.4760.4610.6860.6740.6060.601
    DIQA[118]20190.5060.4920.7200.7080.7040.703
    SGDNet[133]20190.4220.4170.6530.6440.8720.851
    Rank Learning[141]20190.7510.7190.8630.836
    SFA[128]20190.8400.8260.8330.812
    NSSADNN[134]20190.5740.5680.8250.7480.8130.745
    CGFA-CNN[124]20200.8460.837
    DB-CNN[123]20200.4750.4640.6860.6720.8690.851
    Cai[110]20200.6330.6030.8800.874
    下载: 导出CSV
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  • 收稿日期:  2020-12-17
  • 录用日期:  2021-05-12
  • 网络出版日期:  2021-06-20

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