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基于色彩空间自然场景统计的无参考图像质量评价

李俊峰

李俊峰. 基于色彩空间自然场景统计的无参考图像质量评价. 自动化学报, 2015, 41(9): 1601-1615. doi: 10.16383/j.aas.2015.c140616
引用本文: 李俊峰. 基于色彩空间自然场景统计的无参考图像质量评价. 自动化学报, 2015, 41(9): 1601-1615. doi: 10.16383/j.aas.2015.c140616
LI Jun-Feng. No-reference Image Quality Assessment Based on Natural Scene Statistics in RGB Color Space. ACTA AUTOMATICA SINICA, 2015, 41(9): 1601-1615. doi: 10.16383/j.aas.2015.c140616
Citation: LI Jun-Feng. No-reference Image Quality Assessment Based on Natural Scene Statistics in RGB Color Space. ACTA AUTOMATICA SINICA, 2015, 41(9): 1601-1615. doi: 10.16383/j.aas.2015.c140616

基于色彩空间自然场景统计的无参考图像质量评价

doi: 10.16383/j.aas.2015.c140616
基金项目: 

国家自然科学基金(61374022),浙江省新型网络标准及其应用技术重点实验室开放课题(2013E10012)资助

详细信息
    作者简介:

    李俊峰 浙江理工大学机械与自动控制学院副教授.2010年获得东华大学工学博士学位.主要研究方向为图像质量评价,图像融合.E-mail:ljf2003@zstu.edu.cn

No-reference Image Quality Assessment Based on Natural Scene Statistics in RGB Color Space

Funds: 

Supported by National Natural Science Foundation of China (61374022), Zhejiang Provincial Key Laboratory of New Network Standards and Technologies (2013E10012)

  • 摘要: RGB色彩空间中各色彩分量间存在强相关性, 图像发生失真会改变各分量间的相关性. 基于此, 本文提出了一种新的通用无参考图像质量评价方法. 首先, 根据人类视觉对RGB色彩空间中绿色分量更为敏感的颜色感知特性, 提取了G分量MSCN系数及其4方向邻域系数的统计特征; 其次, 在分析RGB色彩空间中R、G及B分量间相关性的基础上, 分别计算RGB色彩空间各色彩分量及其纹理、相位间的互信息, 利用互信息作为统计特征来描述其各分量间的相关性; 进而, 结合上述统计特征, 分别利用SVR和SVC构建无参考图像质量评价模型和图像失真类型识别模型; 最后, 在LIVE、CSIQ 及TID2008图像质量评价数据库上进行了算法与DMOS (Different mean opinion score)的相关性、失真类型识别及计算复杂性等方面的实验. 实验结果表明, 本文方法的评价结果与人类主观评价具有高度的一致性, 在LIVE 数据库上的斯皮尔曼等级相关系数和皮尔逊线性相关系数均在0.942以上; 而且, 图像失真类型识别模型的识别准确率也高达93.59%, 明显高于当今主流无参考图像质量评价方法.
  • [1] Brand ao T, Queluz M P. No-reference image quality assessment based on DCT domain statistics. Signal Processing, 2008, 88(4): 822-833
    [2] Golestaneh S A, Chandler D M. No-reference quality assessment of JPEG images via a quality relevance map. IEEE Signal Processing Letters, 2014, 21(2): 155-158
    [3] Sheikh H R, Bovik A C, Cormack L K. No-reference quality assessment using natural scene statistics: JPEG2000. IEEE Transactions on Image Processing, 2005, 14(11): 1918-1927
    [4] Zhang J, Ong S H, Le T M. Kurtosis-based no-reference quality assessment of JPEG2000 images. Signal Processing: Image Communication, 2011, 26(1): 13-23
    [5] Cheng Xiao-Gang, An Ming-Wei, Ruan Ya-Duan, Chen Qi-Mei. A modern image quality measurement method for blind image restoration. Acta Automatica Sinica, 2013, 39(4): 418-423 (成孝刚, 安明伟, 阮雅端, 陈启美. 基于变分的盲图像复原质量评价指标. 自动化学报, 2013, 39(4): 418-423)
    [6] Lu Ya-Nan, Xie Feng-Ying, Zhou Shi-Xin, Jiang Zhi-Guo, Meng Ru-Song. Non-reference quality assessment of dermoscopy images with defocus blur and uneven illumination distortion. Acta Automatica Sinica, 2014, 40(3): 480-488 (卢亚楠, 谢凤英, 周世新, 姜志国, 孟如松. 皮肤镜图像散焦模糊与光照不均混叠时的无参考质量评价. 自动化学报, 2014, 40(3): 480-488)
    [7] Serir A, Beghdadi A, Kerouh F. No-reference blur image quality measure based on multiplicative multiresolution decomposition. Journal of Visual Communication and Image Representation, 2013, 24(7): 911-925
    [8] Oh T, Park J, Seshadrinathan K, Lee S, Bovik A C. No-reference sharpness assessment of camera-shaken images by analysis of spectral structure. IEEE Transactions on Image Processing, 2014, 23(12): 5428-5439
    [9] Ye P, Doermann D. No-reference image quality assessment using visual codebooks. IEEE Transactions on Image Processing, 2012, 21(7): 3129-3138
    [10] Mittal A, Moorthy A K, Bovik A C. No-reference image quality assessment in the spatial domain. IEEE Transactions on Image Processing, 2012, 21(12): 4695-4708
    [11] Dong Hong-Ping, Liu Li-Xiong. No-reference image quality assessment in mutual information domain. Journal of Image and Graphics, 2014, 19(3): 484-492 (董宏平, 刘利雄. 互信息域中的无参考图像质量评价. 中国图象图形学报, 2014, 19(3): 484-492)
    [12] Liu L X, Liu B, Huang H, Bovik A C. No-reference image quality assessment based on spatial and spectral entropies. Signal Processing: Image Communication, 2014, 29(8): 856-863
    [13] Xue W F, Mou X Q, Zhang L, Bovik A C, Feng X C. Blind image quality assessment using joint statistics of gradient magnitude and Laplacian features. IEEE Transactions on Image Processing, 2014, 23(11): 4850-4862
    [14] Sang Q B, Wu X J, Li C F, Bovik A C. Blind image quality assessment using a reciprocal singular value curve. Signal Processing: Image Communication, 2014, 29(10): 1149-1157
    [15] Saad M A, Bovik A C, Charrier C. Blind image quality assessment: a natural scene statistics approach in the DCT domain. IEEE Transactions on Image Processing, 2012, 21(8): 3339-3352
    [16] Moorthy A K, Bovik A C. Blind image quality assessment: from natural scene statistics to perceptual quality. IEEE Transactions on Image Processing, 2011, 20(12): 3350-3364
    [17] Zhang Y, Moorthy A K, Chandler D M, Bovik A C. C-DIIVINE: No-reference image quality assessment based on local magnitude and phase statistics of natural scenes. Signal Processing: Image Communication, 2014, 29(7): 725-747
    [18] Liu L X, Dong H P, Huang H, Bovik A C. No-reference image quality assessment in curvelet domain. Signal Processing: Image Communication, 2014, 29(4): 494-505
    [19] Li Y M, Po L M, Xu X Y, Feng L T. No-reference image quality assessment using statistical characterization in the shearlet domain. Signal Processing: Image Communication, 2014, 29(7): 748-759
    [20] Lu F F, Zhao Q F, Yang G K. A no-reference image quality assessment approach based on steerable pyramid decomposition using natural scene statistics. Neural Computing and Applications, 2015, 26(1): 77-90
    [21] Li Y M, Po L M, Xu X Y, Feng L T, Yuan F, Cheung C H, Cheung K W. No-reference image quality assessment with shearlet transform and deep neural networks. Neurocomputing, 2015, 154: 94-109
    [22] Tsagarisv V, Anastassopoulos V. Multispectral image fusion for improved RGB representation based on perceptual attributes. International Journal of Remote Sensing, 2005, 26(15): 3241-3254
    [23] Ponomarenko N N, Lukin V V, Zelensky A, Egiazarian K, Carli M, Battisti F. TID2008---A database for evaluation of full-reference visual quality assessment metrics. Advances of Modern Radioelectronics, 2009, 10: 30-45
    [24] Mittal A, Soundararajan R, Bovik A C. Making a 'Completely Blind' image quality analyzer. IEEE Signal Processing Letters, 2012, 20(3): 209-212
    [25] Ruderman D L. The statistics of natural images. Network: Computation in Neural Systems, 1994, 5(4): 517-548
    [26] Kovesi P. Phase congruency detects corners and edges. In: Proceedings of the 7th International Conference on Digital Image Computing: Techniques and Applications. Sydney, Australia, 2003. 309-318
    [27] Klotz J G, Kracht D, Bossert M, Schober S. Canalizing boolean functions maximize mutual information. IEEE Transactions on Information Theory, 2014, 60(4): 2139-2147
    [28] Chang C C, Lin C C. LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3): Article No. 27
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出版历程
  • 收稿日期:  2014-09-02
  • 修回日期:  2015-04-28
  • 刊出日期:  2015-09-20

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