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结合全局与局部变化的图像质量评价

高敏娟 党宏社 魏立力 王海龙 张选德

高敏娟, 党宏社, 魏立力, 王海龙, 张选德. 结合全局与局部变化的图像质量评价. 自动化学报, 2020, 46(12): 2662−2671 doi: 10.16383/j.aas.c190697
引用本文: 高敏娟, 党宏社, 魏立力, 王海龙, 张选德. 结合全局与局部变化的图像质量评价. 自动化学报, 2020, 46(12): 2662−2671 doi: 10.16383/j.aas.c190697
Gao Min-Juan, Dang Hong-She, Wei Li-Li, Wang Hai-Long, Zhang Xuan-De. Combining global and local variation for image quality assessment. Acta Automatica Sinica, 2020, 46(12): 2662−2671 doi: 10.16383/j.aas.c190697
Citation: Gao Min-Juan, Dang Hong-She, Wei Li-Li, Wang Hai-Long, Zhang Xuan-De. Combining global and local variation for image quality assessment. Acta Automatica Sinica, 2020, 46(12): 2662−2671 doi: 10.16383/j.aas.c190697

结合全局与局部变化的图像质量评价

doi: 10.16383/j.aas.c190697
基金项目: 国家自然科学基金(61871260, 61871259), 陕西科技大学人工智能交叉学科PI团队培育专项基金资助
详细信息
    作者简介:

    高敏娟:陕西科技大学电气与控制工程学院博士研究生. 2010年获得山西大学工学硕士学位. 主要研究方向为图像处理, 图像质量评价.E-mail: gaominjuan1984@163.com

    党宏社:陕西科技大学电气与控制工程学院教授. 主要研究方向为工业过程与优化, 计算机控制, 图像处理.E-mail: danghs@sust.edu.cn

    魏立力:宁夏大学数学统计学院教授. 主要研究方向为应用统计与数据分析. E-mail: liliwei@nxu.edu.cn

    王海龙:宁夏师范学院数学与计算机科学学院讲师. 2011年获得香港公开大学教育硕士学位. 主要研究方向为代数.E-mail: wanghailong7903@163.com

    张选德:陕西科技大学电子信息与人工智能学院教授. 2013年获得西安电子科技大学理学博士学位. 主要研究方向为图像恢复, 图像质量评价, 稀疏表示和低秩逼近理论. 本文通信作者.E-mail: zhangxuande@sust.edu.cn

Combining Global and Local Variation for Image Quality Assessment

Funds: Supported by National Natural Science Foundation of China (61871260, 61871259) and Shaanxi University of Science and Technology Artificial Intelligence Interdisciplinary PI Team Cultivation Special Project
  • 摘要: 图像所包含的信息是通过灰度值在空域的变化呈现的. 梯度是度量变化的基本工具, 这使得梯度成为了目前大多数图像质量评价算法的重要组成部分. 但是梯度只能度量局部变化, 而当人类视觉系统(Human visual system, HVS)感知一幅图像时, 既能感知到局部变化, 也能感知到全局变化. 基于HVS的这一特性, 本文提出了一种结合全局与局部变化的图像质量评价算法(Global and local variation similarity, GLV-SIM). 该算法利用Grünwald-Letnikov分数阶导数来度量图像的全局变化, 利用梯度模来度量图像的局部变化. 然后结合二者计算参考图像和退化图像之间的相似度谱(Similarity map), 进而得到图像的客观评分. 在TID2013、TID2008、CSIQ与LIVE四个数据库上的仿真实验表明, 较之单一度量局部变化的方法, 本文算法能更准确地模拟HVS对图像质量的感知过程, 给出的客观评分与主观评分具有较好的一致性.
  • 图  1  Child-swimming图像

    Fig.  1  The image of child-swimming

    图  2  GLV-SIM算法框架

    Fig.  2  The framework of GLV-SIM algorithm

    图  3  参考图像(a)及其不同类型退化图像(b)~(f) (右下角为矩形区域局部放大图)

    Fig.  3  Reference image (a) and different types of distorted images (b)~(f)

    (The lower right corner is a enlarged view of the rectangular region)

    图  4  针对图3中各矩形区域对应的$DM$

    Fig.  4  The corresponding $DM$ map for each rectangular region in Fig.3

    表  1  图3(b)~(f)主观评分和不同算法客观评分

    Table  1  Subjective scores and objective scores of different algorithms for Fig. 3(b)~(f)

    评价方法图3(b)图3(c)图3(d)图3(e)图3(f)
    MOS5.00003.83874.18754.76676.2903
    PSNR30.530430.578426.130327.480827.3498
    VSNR29.730121.148020.534230.707220.2681
    IFC4.73893.43514.93192.995611.3746
    SSIM 0.92500.84610.94590.94750.9568
    MS-SSIM 0.96060.91590.97380.97270.9821
    IW-SSIM0.96840.90750.96450.97040.9661
    GSIM0.99580.98880.99530.99660.9979
    FSIM0.98310.94620.95380.96990.9707
    GLV-SIM0.99590.98450.99270.99570.9961
    下载: 导出CSV

    表  2  针对表1评分排名

    Table  2  The rank of scores on Table 1

    评价方法图3(b)图3(c)图3(d)图3(e)图3(f)
    MOS25431
    PSNR21534
    VSNR23415
    IFC34251
    SSIM45321
    MS-SSIM45231
    IW-SSIM24513
    GSIM35421
    FSIM15432
    GLV-SIM25431
    下载: 导出CSV

    表  3  不同IQA算法在TID2013和TID2008数据库的实验结果比较

    Table  3  Comparison the performance results of different IQA algorithms on TID2013 and TID2008 databases

    数据库性能指标PSNRVSNRIFCSSIMMS-SSIMIW-SSIMGSIMFSIMGLV-SIM
    TID 2013SROCC0.63960.68120.53890.74170.78590.77790.79460.80150.8068
    KROCC0.46980.50840.39390.55880.60470.59770.62550.62890.6381
    PLCC0.70170.74020.55380.78950.83290.83190.84640.85890.8580
    RMSE0.88320.83921.03220.76080.68610.68800.66030.63490.6368
    TID 2008SROCC0.55310.70460.56750.77490.85420.85590.85040.88050.8814
    KROCC0.40270.53400.42360.57680.65680.66360.65960.69460.6956
    PLCC0.57340.68200.73400.77320.84510.85790.84220.87380.8648
    RMSE1.09940.98150.91130.85110.71730.68950.72350.65250.6739
    下载: 导出CSV

    表  4  不同IQA算法在CSIQ和LIVE数据库的实验结果比较

    Table  4  Comparison the performance results of different IQA algorithms on CSIQ and LIVE databases

    数据库性能指标PSNRVSNRIFCSSIMMS-SSIMIW-SSIMGSIMFSIMGLV-SIM
    CSIQSROCC0.80580.81060.76710.87560.91330.92130.91080.92420.9264
    KROCC0.60840.62470.58970.69070.73930.75290.73740.75670.7605
    PLCC0.80000.80020.83840.86130.89910.91440.89640.91200.9082
    RMSE0.15750.15750.14310.13340.11490.10630.11640.10070.1099
    LIVESROCC0.87560.92740.92590.94790.95130.95670.95610.96340.9521
    KROCC0.68650.76160.75790.79630.80450.81750.81500.83370.8179
    PLCC0.87230.92310.92680.94490.94890.95220.95120.95970.9368
    RMSE13.35910.50510.2648.94458.61888.34738.43277.67808.0864
    下载: 导出CSV

    表  5  不同IQA算法在TID2008数据库单一失真评价性能(SROCC)比较

    Table  5  Comparison SROCC for individual distortion of different IQA algorithms on TID2008 database

    数据库失真类型PSNRVSNRIFCSSIMMS-SSIMIW-SSIMGSIMFSIMGLV-SIM
    TID 2008AWN0.90730.77280.58060.81070.80940.78690.85730.85660.9125
    ANMC0.89940.77930.54600.80290.80640.79200.80950.85270.8979
    SCN0.91750.76650.59580.81440.81950.77140.89020.84830.9167
    MN0.85200.72950.67320.77950.81560.80870.74030.80210.8087
    HFN0.92730.88110.73180.87290.86850.86620.89320.90930.9175
    IMN0.87250.64700.53450.67320.68680.64650.77210.74520.7864
    QN0.87020.82710.58570.85310.85370.81770.87500.85640.8865
    GB0.87040.93300.85590.95440.96070.96360.95850.94720.9587
    DEN0.94220.92860.79730.95300.95710.94730.97230.96030.9666
    JPEG0.87230.91740.81800.92520.93480.91840.93910.92790.9534
    JP2K0.81310.95150.94370.96250.97360.97380.97550.97730.9751
    JGTE0.75250.80560.79090.86780.87360.85880.88320.87080.8793
    J2TE0.83120.79090.73010.85770.85220.82030.89250.85440.9021
    NEPN0.58120.57160.84180.71070.73360.77240.73720.74910.7271
    BLOCK0.61940.19260.67700.84620.76170.76230.88650.84920.8960
    MS0.69660.37150.42500.72310.73740.70670.71740.66980.6994
    CTC0.58670.42390.27130.52460.63980.63010.67360.64810.6689
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-10-08
  • 录用日期:  2019-12-15
  • 网络出版日期:  2020-01-04
  • 刊出日期:  2020-12-29

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