• Infrared and Laser Engineering
  • Vol. 47, Issue 7, 703004 (2018)
Yao Wang1、2、3, Liu Yunpeng1、3, and Zhu Changbo1、2、4
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
  • 3[in Chinese]
  • 4[in Chinese]
  • show less
    DOI: 10.3788/irla201847.0703004 Cite this Article
    Yao Wang, Liu Yunpeng, Zhu Changbo. Deep learning of full-reference image quality assessment based on human visual properties[J]. Infrared and Laser Engineering, 2018, 47(7): 703004 Copy Citation Text show less
    References

    [1] Wang Z, Bovik A C, Sheikh H R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Trans Image Process, 2004, 13(4): 600-612.

    [2] Wang Z, Li Q. Information content weighting for perceptual image quality assessment[J]. IEEE Transactions on Image Processing, 2011, 20(5): 1185-1198.

    [3] Sheikh H R, Bovik A C. Image information and visual quality[J]. IEEE Transactions on Image Processing, 2006, 15(2): 430-444.

    [4] Cheng G, Huang J C, Zhu C, et al. Perceptual image quality assessment using a geometric structural distortion model[C]//IEEE International Conference on Image Processing, 2010: 325-328.

    [5] Zhang D. FSIM: A feature similarity index for image quality assessment[J]. IEEE Transactions on Image Processing, 2011, 20(8): 2378-2386.

    [6] Xue W, Zhang L, Mou X, et al. Gradient magnitude similarity deviation: A highly efficient perceptual image quality index[J]. IEEE Transactions on Image Processing, 2014, 23(2): 684-695.

    [7] Luo Haibo, He Miao, Hui Bin, et al. Pedestrian detection algorithm based on dual-model fused fully convolutional networks[J]. Infrared and Laser Engineering, 2018, 47(2):0203001. (in Chinese)

    [8] Luo Haibo, Xu Lingyun, Hui Bin, et al. Status and prospect of target tracking based on deep learning[J]. Infrared and Laser Engineering, 2017, 46(5): 0502002. (in Chinese)

    [9] Kang L, Ye P, Li Y, et al. Convolutional neural networks for No-reference image quality assessment[C]//Computer Vision and Pattern Recognition, IEEE, 2014: 1733-1740.

    [10] Li Y, Po L M, Feng L, et al. No-reference image quality assessment with deep convolutional neural networks[C]// IEEE International Conference on Digital Signal Processing, 2017: 685-689.

    [11] Kim J, Lee S. Fully deep blind image quality predictor[J]. IEEE Journal of Selected Topics in Signal Processing, 2017, 11(1): 206-220.

    [12] Ali Amirshahi S, Pedersen M, Yu S X. Image quality assessment by comparing CNN features between images[J]. Electronic Imaging, 2016, 60(6): 6041010.

    [13] Gao F, Wang Y, Li P, et al. Deep Sim: Deep similarity for image quality assessment[J]. Neurocomputing, 2017(1): 104-114.

    [14] Mahendran A, Vedaldi A. Visualizing deep convolutional neural Networks using natural pre-images[J]. International Journal of Computer Vision, 2016, 120(4): 1-23.

    [15] Ponomarenko N, Lukin V, Zelensky A, et al. TID2008-a database for evaluation of full-reference visual quality assessment metrics[J]. Adv Modern Radioelectron, 2009, 10(1): 30-45.

    Yao Wang, Liu Yunpeng, Zhu Changbo. Deep learning of full-reference image quality assessment based on human visual properties[J]. Infrared and Laser Engineering, 2018, 47(7): 703004
    Download Citation