• Acta Optica Sinica
  • Vol. 38, Issue 6, 0610003 (2018)
Sumei Li, Yongli Chang*, and Zhicheng Duan
Author Affiliations
  • School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
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    DOI: 10.3788/AOS201838.0610003 Cite this Article Set citation alerts
    Sumei Li, Yongli Chang, Zhicheng Duan. Objective Assessment of Stereoscopic Image Comfort Based on Convolutional Neural Network[J]. Acta Optica Sinica, 2018, 38(6): 0610003 Copy Citation Text show less
    References

    [1] Hou C P, Ma T T, Yue G H et al. Multiply-distorted image quality assessment based on high-order phase congruence[J]. Laser & Optoelectronics Progress, 54, 071001(2017).

    [2] Yang J C, Hou C P, Shen L L et al. Objective evaluation method for stereo image quality based on PSNR[J]. Journal of Tianjin University, 41, 1448-1452(2008).

    [3] Zhu Q S, Zhi L O, Liu R et al. Research on image conversion from planar into stereo[J]. Computer Science, 34, 225-228(2007).

    [4] Wang Z, Bovik A C, Sheikh H R et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 13, 600-612(2004). http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=1284395

    [5] Russo F, de Angelis A, Carbone P. A vector approach to quality assessment of color images. [C]∥Instrumentation and Measurement Technology Conference Proceedings, 814-818(2008).

    [6] Chen J C. Application of ICA and BT-SVM in stereo image quality assessment system[D]. Tianjin: Tianjin University, 41-45(2012).

    [7] Wang G H, Li S M, Zhu D et al. Application of extreme learning machine in objective stereo scopic image quality assessment[J]. Journal of Optoelectronics·Laser, 25, 1837-1842(2014).

    [8] Bai J J, Sun Q, Jing S B et al. Robust extreme learning machine and its application in analysis of near infrared spectroscopy data[J]. Laser & Optoelectronics Progress, 52, 103002(2015).

    [9] Goodfellow I, Bengio Y, Courville A[M]. Deep learning, 331-339(2016).

    [10] Ciresan D, Meier U, Masci J et al. Multi-column deep neural network for traffic sign classification[J]. Neural Networks, 32, 333-338(2012). http://europepmc.org/abstract/MED/22386783

    [11] Lv Y, Yu M, Jiang G et al. No-reference stereoscopic image quality assessment using binocular self-similarity and deep neural network[J]. Signal Processing Image Communication, 47, 346-357(2016). http://www.sciencedirect.com/science/article/pii/S0923596516301023

    [12] Cheng L Y, Mi G Y, Li S et al. Quality diagnosis of joints in laser brazing based on principal component analysis: support vector machine model[J]. Chinese Journal of Lasers, 44, 0302004(2017).

    [13] Li S M, Lei G Q, Fan R. Depth maps super-resolution reconstruction based on convolutional neural networks[J]. Acta Optica Sinica, 37, 1210002(2017).

    [14] Lecun Y, Bottou L, Bengio Y et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 86, 2278-2324(1998). http://ieeexplore.ieee.org/iel4/5/15641/00726791.pdf

    [15] Maas A L, Hannun A Y, Ng A Y. Rectifier nonlinearities improve neural network acoustic models. [C]∥Proceedings of 30 th International Conference on Machine Learning , 30, 3(2013).

    [16] Krizhevsky A, Sutskever I, Hinton G. ImageNet classification with deep convolutional neural networks[C]. International Conference on Neural Information Processing Systems, 1097-1105(2012).

    [17] Srivastava N, Hinton G, Krizhevsky A et al. Dropout: a simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning Research, 15, 1929-1958(2014). http://dl.acm.org/citation.cfm?id=2670313&preflayout=flat

    Sumei Li, Yongli Chang, Zhicheng Duan. Objective Assessment of Stereoscopic Image Comfort Based on Convolutional Neural Network[J]. Acta Optica Sinica, 2018, 38(6): 0610003
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