• Laser & Optoelectronics Progress
  • Vol. 56, Issue 11, 111003 (2019)
Yindong Chen1, Chaofeng Li2, and Qingbing Sang1、*
Author Affiliations
  • 1 School of Internet of Things Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
  • 2 Institute of Logistics Science & Engineering, Shanghai Maritime University, Shanghai 200135, China;
  • show less
    DOI: 10.3788/LOP56.111003 Cite this Article Set citation alerts
    Yindong Chen, Chaofeng Li, Qingbing Sang. Quality Assessment Without Reference Images Based on Convolution Neural Network and Deep Forest[J]. Laser & Optoelectronics Progress, 2019, 56(11): 111003 Copy Citation Text show less
    References

    [1] Wang Z, Bovik A C. Modern image quality assessment[J]. Synthesis Lectures on Image, Video, and Multimedia Processing, 2, 1-156(2006).

         Wang Z, Bovik A C. Modern image quality assessment[J]. Synthesis Lectures on Image, Video, and Multimedia Processing, 2, 1-156(2006).

    [2] Wang Z M. Review of no-reference image quality assessment[J]. Acta Automatica Sinica, 41, 1062-1079(2015).

         Wang Z M. Review of no-reference image quality assessment[J]. Acta Automatica Sinica, 41, 1062-1079(2015).

    [3] Zhao W Z, Qin S Y. Image quality assessment and some solving approaches to current issues[J]. Laser & Optoelectronics Progress, 47, 041002(2010).

         Zhao W Z, Qin S Y. Image quality assessment and some solving approaches to current issues[J]. Laser & Optoelectronics Progress, 47, 041002(2010).

    [4] Zhang F, Zhang R Y, Li Z Z. Image quality assessment based on symmetry phase congruency[J]. Laser & Optoelectronics Progress, 54, 101003(2017).

         Zhang F, Zhang R Y, Li Z Z. Image quality assessment based on symmetry phase congruency[J]. Laser & Optoelectronics Progress, 54, 101003(2017).

    [5] Zhang Y, Jin W Q. Assessment method of fusion image quality in wavelet domain structural similarity[J]. Chinese Journal of Lasers, 39, s109007(2012).

         Zhang Y, Jin W Q. Assessment method of fusion image quality in wavelet domain structural similarity[J]. Chinese Journal of Lasers, 39, s109007(2012).

    [6] Moorthy A K, Bovik A C. Blind image quality assessment: from natural scene statistics to perceptual quality[J]. IEEE Transactions on Image Processing, 20, 3350-3364(2011). http://www.ncbi.nlm.nih.gov/pubmed/21521667

         Moorthy A K, Bovik A C. Blind image quality assessment: from natural scene statistics to perceptual quality[J]. IEEE Transactions on Image Processing, 20, 3350-3364(2011). http://www.ncbi.nlm.nih.gov/pubmed/21521667

    [7] Mittal A, Moorthy A K, Bovik A C. No-reference image quality assessment in the spatial domain[J]. IEEE Transactions on Image Processing, 21, 4695-4708(2012). http://europepmc.org/abstract/MED/22910118

         Mittal A, Moorthy A K, Bovik A C. No-reference image quality assessment in the spatial domain[J]. IEEE Transactions on Image Processing, 21, 4695-4708(2012). http://europepmc.org/abstract/MED/22910118

    [8] Saad M A, Bovik A C, Charrier C. A DCT statistics-based blind image quality index[J]. IEEE Signal Processing Letters, 17, 583-586(2010). http://ieeexplore.ieee.org/document/5430991

         Saad M A, Bovik A C, Charrier C. A DCT statistics-based blind image quality index[J]. IEEE Signal Processing Letters, 17, 583-586(2010). http://ieeexplore.ieee.org/document/5430991

    [9] Li C F, Bovik A C, Wu X J. Blind image quality assessment using a general regression neural network[J]. IEEE Transactions on Neural Networks, 22, 793-799(2011). http://www.ncbi.nlm.nih.gov/pubmed/21486713

         Li C F, Bovik A C, Wu X J. Blind image quality assessment using a general regression neural network[J]. IEEE Transactions on Neural Networks, 22, 793-799(2011). http://www.ncbi.nlm.nih.gov/pubmed/21486713

    [10] Li C F, Zhang Y, Wu X J et al. A multi-scale learning local phase and amplitude blind image quality assessment for multiply distorted images[J]. IEEE Access, 6, 64577-64586(2018).

         Li C F, Zhang Y, Wu X J et al. A multi-scale learning local phase and amplitude blind image quality assessment for multiply distorted images[J]. IEEE Access, 6, 64577-64586(2018).

    [11] Kang L, Ye P, Li Y et al. Convolutional neural networks for no-reference image quality assessment. [C]∥2014 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, OH, USA. New York: IEEE, 1733-1740(2014).

         Kang L, Ye P, Li Y et al. Convolutional neural networks for no-reference image quality assessment. [C]∥2014 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, OH, USA. New York: IEEE, 1733-1740(2014).

    [12] Wu M Y, Chen L. Blind image quality assessment via convolutional neural network. [C]∥2016 9th International Symposium on Computational Intelligence and Design (ISCID), December 10-11, 2016, Hangzhou, China. New York: IEEE, 221-224(2016).

         Wu M Y, Chen L. Blind image quality assessment via convolutional neural network. [C]∥2016 9th International Symposium on Computational Intelligence and Design (ISCID), December 10-11, 2016, Hangzhou, China. New York: IEEE, 221-224(2016).

    [13] Wu L X, Sang Q B. No-reference multiply distorted images quality assessment based on convolutional neural network[J]. Optical Technique, 44, 555-561(2018).

         Wu L X, Sang Q B. No-reference multiply distorted images quality assessment based on convolutional neural network[J]. Optical Technique, 44, 555-561(2018).

    [14] Zhang Y, Wang W J, Kang X P. A regression SVM selection ensemble approach[J]. Computer Science, 35, 178-180(2008).

         Zhang Y, Wang W J, Kang X P. A regression SVM selection ensemble approach[J]. Computer Science, 35, 178-180(2008).

    [15] Zhou Z H. -05-14)[2018-11-01]. https:∥arxiv.org/abs/1702.08835v2.(2018).

         Zhou Z H. -05-14)[2018-11-01]. https:∥arxiv.org/abs/1702.08835v2.(2018).

    [16] Zhou Z H[M]. Machine learning, 171-178(2016).

         Zhou Z H[M]. Machine learning, 171-178(2016).

    [17] Zhang C S, Cui L J, Yang G et al. Comparative study for ensemble learning algorithms[J]. Journal of Hebei University (Natural Science Edition), 27, 551-554(2007).

         Zhang C S, Cui L J, Yang G et al. Comparative study for ensemble learning algorithms[J]. Journal of Hebei University (Natural Science Edition), 27, 551-554(2007).

    [18] Sheikh H R, Wang Z, Cormack L et al[2018-11-01]. LIVE image quality assessment database release 2[2018-11-01]. http:∥live.ece.utexas.edu/research/quality..

         Sheikh H R, Wang Z, Cormack L et al[2018-11-01]. LIVE image quality assessment database release 2[2018-11-01]. http:∥live.ece.utexas.edu/research/quality..

    [19] Ponomarenko N, Lukin V, Zelensky A et al. TID2008-A database for evaluation of full-reference visual quality assessment metrics[J]. Advances of Modern Radioelectronics, 10, 30-45(2009).

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

    [20] Saad M A, Bovik A C, Charrier C. Blind image quality assessment: a natural scene statistics approach in the DCT domain[J]. IEEE Transactions on Image Processing, 21, 3339-3352(2012). http://www.ncbi.nlm.nih.gov/pubmed/22453635

         Saad M A, Bovik A C, Charrier C. Blind image quality assessment: a natural scene statistics approach in the DCT domain[J]. IEEE Transactions on Image Processing, 21, 3339-3352(2012). http://www.ncbi.nlm.nih.gov/pubmed/22453635

    [21] Ye P, Kumar J, Kang L et al. Unsupervised feature learning framework for no-reference image quality assessment. [C]∥2012 IEEE Conference on Computer Vision and Pattern Recognition, June 16-21, 2012, Providence, RI, USA. New York: IEEE, 1098-1105(2012).

         Ye P, Kumar J, Kang L et al. Unsupervised feature learning framework for no-reference image quality assessment. [C]∥2012 IEEE Conference on Computer Vision and Pattern Recognition, June 16-21, 2012, Providence, RI, USA. New York: IEEE, 1098-1105(2012).

    Yindong Chen, Chaofeng Li, Qingbing Sang. Quality Assessment Without Reference Images Based on Convolution Neural Network and Deep Forest[J]. Laser & Optoelectronics Progress, 2019, 56(11): 111003
    Download Citation