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联合多种边缘检测算子的无参考质量评价算法

沈丽丽 杭宁

沈丽丽, 杭宁. 联合多种边缘检测算子的无参考质量评价算法[J]. 工程科学学报, 2018, 40(8): 996-1004. doi: 10.13374/j.issn2095-9389.2018.08.014
引用本文: 沈丽丽, 杭宁. 联合多种边缘检测算子的无参考质量评价算法[J]. 工程科学学报, 2018, 40(8): 996-1004. doi: 10.13374/j.issn2095-9389.2018.08.014
SHEN Li-li, HANG Ning. No-reference image quality assessment using joint multiple edge detection[J]. Chinese Journal of Engineering, 2018, 40(8): 996-1004. doi: 10.13374/j.issn2095-9389.2018.08.014
Citation: SHEN Li-li, HANG Ning. No-reference image quality assessment using joint multiple edge detection[J]. Chinese Journal of Engineering, 2018, 40(8): 996-1004. doi: 10.13374/j.issn2095-9389.2018.08.014

联合多种边缘检测算子的无参考质量评价算法

doi: 10.13374/j.issn2095-9389.2018.08.014
基金项目: 

国家自然科学基金资助项目(61520106002,61471262)

详细信息
  • 中图分类号: TN911.73

No-reference image quality assessment using joint multiple edge detection

  • 摘要: 提出了一种联合多种边缘检测算子的无参考质量评价算法,同时考虑一阶和二阶边缘算子,避免了单一算子的局限性.该方法首先将彩色图像转换为灰度图像,然后计算灰度图像的梯度,相对梯度以及LOG特征.本文所使用的特征分为两部分,一部分提取相对梯度方向的标准差,另一部分利用条件熵来量化不同特征之间的相似性和相互关系,并且考虑到人眼特性进行多尺度计算,最后使用自适应增强(AdaBoost)神经网络进行训练和预测.在公共数据库LIVE和TID2008上进行实验,结果表明新方法对失真图像的预测评分与主观评分有较高的一致性,能很好地反映图像质量的视觉感知效果,仅使用10维特征,性能优于现有的主流无参考质量评价算法.
  • [1] Wang S Q, Gu K, Zhang X, et al. Subjective and objective quality assessment of compressed screen content images. IEEE J Emerging Sel Top Circuits Syst, 2016, 6(4):532
    [2] Zhang X F, Wang S Q, Gu K, et al. Just-noticeable difference-based perceptual optimization for JPEG compression. IEEE Signal Process Lett, 2017, 24(1):96
    [3] Li L D, Yan Y, Lu Z L, et al. No-reference quality assessment of deblurred images based on natural scene statistics. IEEE Access, 2017, 5:2163
    [4] Gu K, Zhai G T, Wang S Q, et al. A general histogram modification framework for efficient contrast enhancement//IEEE International Symposium on Circuits and Systems. Lisbon, 2015:2816
    [5] Ruderman D L. The statistics of natural images. Network Comput Neural Syst, 1994, 5(4):517
    [6] Saad M A, Bovik A C, Charrier C. Blind image quality assessment:a natural scene statistics approach in the DCT domain. IEEE Trans Image Process, 2012, 21(8):3339
    [7] Moorthy A K, Bovik A C. Blind image quality assessment:from natural scene statistics to perceptual quality. IEEE Trans Image Process, 2011, 20(12):3350
    [8] Mittal A, Moorthy A K, Bovik A C. No-reference image quality assessment in the spatial domain. IEEE Trans Image Process, 2012, 21(12):4695
    [9] Hubel D H, Wiesel T N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. J Physiol, 1962, 160(1):106
    [10] Clark M, Bovik A C. Experiments in segmenting texton patterns using localized spatial filters. Pattern Recognit, 1989, 22(6):707
    [11] Marziliano P, Dufaux F, Winkler S, et al. A no-reference perceptual blur metric//IEEE International Conference on Image Processing (ICIP). Rochester, 2002:Ⅲ-57
    [12] Marziliano P, Dufaux F, Winkler S, et al. Perceptual blur and ringing metrics:application to JPEG2000. Signal Process:Image Commun, 2004, 19(2):163
    [13] Liu L X, Hua Y, Zhao Q J, et al. Blind image quality assessment by relative gradient statistics and adaboosting neural network. Signal Process:Image Commun, 2016, 40:1
    [14] Zhang M, Muramatsu C, Zhou X R, et al. Blind image quality assessment using the joint statistics of generalized local binary pattern. IEEE Signal Process Lett, 2014, 22(2):207
    [15] Li Q H, Lin W S, Fang Y M. No-reference quality assessment for multiply-distorted images in gradient domain. IEEE Signal Process Lett, 2016, 23(4):541
    [16] Yue G H, Hou C P, Gu K, et al. No reference image blurriness assessment with local binary patterns. J Visual Commun Image Representation, 2017, 49:382
    [18] Ponomarenko N, Lukin V, Zelensky A, et al. TID2008-a database for evaluation of full-reference visual quality assessment metrics. Adv Mod Radioelectron, 2009, 10(4):30
    [19] Ghosh K, Sarkar S, Bhaumik K. Understanding image structure from a new multi-scale representation of higher order derivative filters. Image Vision Comput, 2007, 25(8):1228
    [20] Gu K, Li L D, Lu H, et al. A fast reliable image quality predictor by fusing micro- and macro-structures. IEEE Trans Ind Electron, 2017, 64(5):3903
    [21] Wang Z, Bovik A C, Sheikh H R, et al. Image quality assessment:from error visibility to structural similarity. IEEE Trans Image Process, 2004, 13(4):600
    [22] Ye P, Kumar J, Kang L, et al. Unsupervised feature learning framework for no-reference image quality assessment//IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Providence, 2012:1098
    [23] Zhang M, Muramatsu C, Zhou X R, et al. Blind image quality assessment using the joint statistics of generalized local binary pattern. IEEE Signal Process Lett, 2014, 22(2):207
    [24] Gu K, Zhai G T, Yang X K, et al. Using free energy principle for blind image quality assessment. IEEE Trans Multimedia, 2015, 17(1):50
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出版历程
  • 收稿日期:  2017-08-22

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