|
[1] Z. Wang and A. Bovik, “Mean Squared Error: Love It or Leave It?,”IEEE Signal Processing Magazine, pp. 98–117, Jan. 2009. [2] U. Engelke and H. J. Zepernick, “Perceptual-based Quality Metrics for Image and Video Services: A Survey,” The 3rd EuroNGI Conference on Next Generation Internet Networks, pp. 190–197, May. 2007. [3] W. Lin, C.-C. J. Kuo, “Perceptual Visual Quality Metrics: A Survey,” Journal of Visual Communication and Image Representation, vol. 22(4), pp. 297-312, May 2011. [4] S. Winkler, and P. Mohandas, “The evolution of video quality measurement: From PSNR to hybrid metrics,” IEEE Trans. on Broadcasting, vol. 54, no. 3, pp. 660-668, Sep. 2008. [5] T.-J. Liu, W. Lin, and C.-C. J. Kuo, “Recent developments and future trends in visual quality assessment,” in Proc. Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), pp. 1-10, Oct. 2011. [6] T.-J. Liu, Y.-C. Lin, W. Lin and C.-C. J. Kuo, “Visual quality assessment: recent developments and coding applications,” APSIPA Transactions on Signal and Information Processing, vol. 2, e4, Jul. 2013. [7] P. Marziliano, F. Dufaux, S. Winkler, T. Ebrahimi, “A no-reference perceptual blur metric,” in Proc. of IEEE ICIP, pp. 57–60, Sep. 2002. [8] E. Ong, W. Lin, Z. Lu, S. Yao, X. Yang, L. Jiang, “No reference JPEG-2000 image quality metric,” in Proc. of IEEE International Conference Multimedia and Expo (ICME), pp. 545-548, 2003. [9] H. Tong, M. Li, H.-J. Zhang, and C. Zhang, “No-reference quality assessment for JPEG2000 compressed images,” in Proc. of IEEE ICIP, pp. 3539–3542, 2004. [10] M. H. Pinson and S. Wolf, “A new standardized method for objectively measuring video quality,” IEEE Trans. on Broadcasting, vol. 50, no. 3, pp. 312–322, Sep. 2004. [11] Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Trans. Image Processing, vol. 13, no. 4, pp. 600–612, Apr. 2004. [12] LIVE Image Quality Assessment Database. [Online]. Available: http://live.ece.utexas.edu/research/quality/subjective.htm [13] Categorical Image Quality (CSIQ) Database. [Online]. Available: http://vision.okstate.edu/csiq [14] Tampere Image Database 2008. [Online]. Available: http://www.ponomarenko.info/tid2008.htm [15] Tampere Image Database 2013. [Online]. Available: http://www.ponomarenko.info/tid2013.htm [16] T.-J. Liu, W. Lin, and C.-C. J. Kuo, “A multi-metric fusion approach to visual quality assessment,” in Proc., IEEE the 3rd international workshop on QoMEX, pp. 72-77, Sep. 2011. [17] M.J. Chen, L.K. Cormack, A.C. Bovik, No-reference quality assessment of natural stereo-pairs, IEEE Trans. Image Process. (2013) 3379–3391. [18] Z.M.P. Sazzad, S. Yamanaka, Y. Horita, Spatio-temporal segmentation based continuous no-reference stereoscopic video quality prediction, in: International Workshop on Quality of Multimedia Experience, 2010, pp. 106–111. [19] R. Akhter, J. Baltes, Z.M. Parvez Sazzad, Y. Horita, No reference stereoscopic image quality assessment, Proc. SPIE 7524 (February) (2010). [20] S. Ryu, K. Sohn, No-reference quality assessment for stereoscopic images based on binocular quality perception, IEEE Trans. Circuits Syst. Video Technol.(2014) 591–602. [21] L. Kang, P. Ye, Y. Li, D. Doermann, Convolutional neural networks for no-reference image quality assessment, in: IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 1733–1740. [22] Zhang, Wei, et al. "Learning structure of stereoscopic image for no-reference quality assessment with convolutional neural network." Pattern Recognition 59 (2016): 176-187. [23] Pan, Cenhui, et al. "Exploiting neural models for no-reference image quality assessment." Visual Communications and Image Processing (VCIP), 2016. IEEE, 2016. [24] Bianco, Simone, et al. "On the use of deep learning for blind image quality assessment." arXiv preprint arXiv:1602.05531 (2016). [25] Ghaderi, Amir, and Vassilis Athitsos. "Selective unsupervised feature learning with convolutional neural network (S-CNN)." Pattern Recognition (ICPR), 2016 23rd International Conference on. IEEE, 2016. [26] Li, Jun-yi, and Jian-hua Li. "Supervised hashing binary code with deep CNN for image retrieval." Biomedical Engineering and Informatics (BMEI), 2015 8th International Conference on. IEEE, 2015. [27] Liu, Xingang, Kai Kang, and Yinbo Liu. "Stereoscopic Image Quality Assessment Based on Depth and Texture Information." IEEE Systems Journal(2016). [28] M.Carnec, P.LeCallet, D.Barba, An image quality assessment method based on perception of structural information, in: IEEE International Conference on Image Processing,vol.3,September2003,pp.185–193. [29] Ma, Lin, et al. "Reorganized DCT-based image representation for reduced reference stereoscopic image quality assessment." Neurocomputing 215 (2016): 21-31. [30] W.Zhou, G.Jiang, M.Yu, Z.Wang, Z.Peng, F.Shao, Reduced reference stereoscopic image quality assessment using digital watermarking, Comput. Electr. Eng. (2014)104–116. [31] Mittal, Anish, Anush Krishna Moorthy, and Alan Conrad Bovik. "No-reference image quality assessment in the spatial domain." IEEE Transactions on Image Processing 21.12 (2012): 4695-4708. [32] P. Ye, J. Kumar, L. Kang, and D. Doermann. Unsupervised feature learning framework for no-reference image quality assessment. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1098–1105, 2012. [33] P. Ye, J. Kumar, L. Kang, and D. Doermann. Real-time noreference image quality assessment based on filter learning. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 987–994, 2013. [34] Fukushima, Kunihiko. "Neocognitron: A hierarchical neural network capable of visual pattern recognition." Neural networks 1.2 (1988): 119-130. [35] "MatConvNet - Convolutional Neural Networks for MATLAB", A. Vedaldi and K. Lenc, Proc. of the ACM Int. Conf. on Multimedia, 2015. [36] Otsu, Nobuyuki. "A threshold selection method from gray-level histograms." IEEE transactions on systems, man, and cybernetics 9.1 (1979): 62-66. [37] LIVE 3Q Image Quality Assessment Database phase1. [Online]. Available: http://live.ece.utexas.edu/research/quality/live_3dimage_phase1.html [38] LIVE 3Q Image Quality Assessment Database phase2. [Online]. Available: http://live.ece.utexas.edu/research/quality/live_3dimage_phase2.html [39] Rui Song, Hyunsuk Ko, C. C. Jay Kuo. MCL-3D: a database for stereoscopic image quality assessment using 2D-image-plus-depth source. Journal of Visual Communication and Image Representation. [40] Liu, Xingang, Kai Kang, and Yinbo Liu. "Stereoscopic Image Quality Assessment Based on Depth and Texture Information." IEEE Systems Journal(2016). [41] L. Zhang, D. Zhang, X. Mou, A.C. Bovik, “FSIM: a feature similarity index for image quality assessment.” IEEE transactions on Image Processing 2011 [42] J. You, L. Xing, A. Perkis, X. Wang” Perceptual quality assessment for stereoscopic images based in 2D image quality metrics and disparity analysis.” Proc. of International Workshop on Video Processing and Quality Metrics for Consumer Electronics, Scottsdale, AZ, USA. 2010. [43] A. Benoit, P. Le Callet, P. Campisi, R. Couseau,” Quality assessment of stereoscopic images.” EURASIP journal on image and video processing 2009. [44] Bensalma, Rafik, and Mohamed-Chaker Larabi. "A perceptual metric for stereoscopic image quality assessment based on the binocular energy." Multidimensional Systems and Signal Processing (2013): 1-36. [45] Chen, Ming-Jun, et al. "Full-reference quality assessment of stereopairs accounting for rivalry." Signal Processing: Image Communication 28.9 (2013): 1143-1155. [46] Lin, Yu-Hsun, and Ja-Ling Wu. "Quality assessment of stereoscopic 3D image compression by binocular integration behaviors." IEEE transactions on Image Processing 23.4 (2014): 1527-1542. [47] Shao, Feng, et al. "Full-reference quality assessment of stereoscopic images by learning binocular receptive field properties." IEEE Transactions on Image Processing 24.10 (2015): 2971-2983. [48] Moorthy, Anush K., and Alan C. Bovik. "A two-stage framework for blind image quality assessment." Image Processing (ICIP), 2010 17th IEEE International Conference on. IEEE, 2010. [49] Moorthy, Anush Krishna, and Alan Conrad Bovik. "Blind image quality assessment: From natural scene statistics to perceptual quality." IEEE transactions on Image Processing 20.12 (2011): 3350-3364.
|