|
[1] H. Sheikh, M. Sabir and A. Bovik, "A statistical evaluation of recent full reference image quality assessment algorithms," IEEE Trans. Image Process., Nov. 2006, vol. 15, no. 11, pp. 3440–3451. [2] M. Rubinstein, D. Gutierrez, O. Sorkine, and A. Shamir, “A comparative study of image retargeting,” ACM Trans. Graphics, 2010, vol. 29, no. 6, pp. 160:1–160:9. [3] J. Bromley, I. Guyon, Y. LeCun, E. Sa ̈ckinger, and R. Shah, "Signature verification using a siamese time delay neural network," in Proc. Neural Inf. Process. Syst. 6, 1994, pp. 737–744. [4] S. Chopra, R. Hadsell and Y. LeCun, "Learning a similarity metric discriminatively, with application to face verification," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., San Diego, CA, USA, June 2005, vol. 1, pp. 539– 546. [5] C. Hsu, C. Lin, Y. Fang and W. Lin, "Objective quality assessment for image retargeting based on perceptual geometric distortion and information loss," IEEE J. Sel. Topics Signal Process., June 2014, vol. 8, no. 3, pp. 377–389. [6] D. Chandler, "Most apparent distortion: Full-reference image quality assessment and the role of strategy," Journal of Electronic Imaging, Jan.–Mar. 2010, vol. 19, no. 1, pp. 011006. [7] D. Ghadiyaram and A. Bovik, "Massive online crowdsourced study of subjective and objective picture quality," IEEE Trans. Image Process., Nov. 2016, vol. 25, no. 1, pp. 372–387. [8] P. Krähenbühl, M. Lang, A. Hornung and M. Gross, "A system for retargeting of streaming video," ACM Transactions on Graphics, 2009, vol. 28, no. 5, pp. 126. [9] M. Rubinstein, A. Shamir, and S. Avidan, “Multi-operator media retargeting,” ACM Trans. Graphics, 2009, vol. 28, no. 3, pp. 23 :1–23:11. [10] S. Avidan and A. Shamir, “Seam carving for content-aware image resizing,” ACM Trans. Graphics, 2007, vol. 26, no. 3, pp. 10:1–10:9. [11] Y. Pritch, E. Kav-Venaki, and S. Peleg, “Shift-map image editing,” in Proc. Int. Conf. Comput. Vis., Kyoto, Japan, Oct. 2009, pp. 151–158. [12] L. Wolf, M. Guttmann, and D. Cohen-Or, “Non-homogeneous content-driven video retargeting,” in Proc. Int. Conf. Comput. Vis., Rio de Janeiro, Brazil, Oct. 2007, pp. 1–6. [13] Y.-S. Wang, C.-L. Tai, O. Sorkin, and T.-Y. Lee, “Optimized scaleand-stretch for image resizing,” ACM Trans. Graphics, 2008, vol. 27, no. 5, pp. 118:1–118:8. [14] D. Simakov, Y. Caspi, E. Shechtman, and M. Irani, “Summarizing visual data using bidirectional similarity,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Anchorage, AK, USA, Jun. 2008, pp. 1–8. [15] A. Mollahosseini and M. Mahoor, "Bidirectional warping of active appearance model," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Portland, OR, USA, Jun. 2013, pp. 875–880. [16] C. Liu, J. Yuen, and A. Torralba, “SIFT flow: Dense correspondence across scenes and its applications,” IEEE Trans. Pattern Anal. Mach. Intell., May 2011, vol. 33, no. 5, pp. 978–994. [17] O. Pele and M. Werman, “Fast and robust earth mover’s distances,” in Proc. Int. Conf. Comput. Vis., Kyoto, Japan, Oct. 2009, pp. 460–467. [18] Y. Zhang, Y. Fang, W. Lin, X. Zhang and L. Li, "Backward registration-based aspect ratio similarity for image retargeting quality assessment," IEEE Trans. Image Process., Jun. 2016, vol. 25, no. 9, pp. 4286–4297. [19] Y. Chen, Y. J. Liu "Learning to rank retargeted images," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Honolulu, HI, USA, Jul. 2017, pp. 2994–4002. [20] A. Krizhevsky, I. Sutskever, and G. Hinton, “ImageNet classification with deep convolutional neural networks,” in Proc. Neural Inf. Process. Syst. 25, 2012, pp. 1097–1105. [21] S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” in Proc. Neural Inf. Process. Syst. 28, 2015, pp. 91–99. [22] E. Shelhamer, J. Long and T. Darrell, "Fully convolutional networks for semantic segmentation," IEEE Trans. Pattern Anal. Mach. Intell., May 2017, vol. 39, no. 4, pp. 640–651.
[23] L. Kang, P. Ye, Y. Li and D. Doermann, "Convolutional neural networks for no- reference image quality assessment," in Proc. Int. Conf. Comput. Vis., Columbus,
OH, USA, Jun. 2014, pp. 1733–1740. [24] S. Bosse, D. Maniry, T. Wiegand and W. Samek, "A deep neural network for image quality assessment," in Proc. 23th IEEE Int. Conf. Image Process., Phoenix, AZ, USA, Sep. 2016, pp. 3773–3777. [25] J. Kim and S. Lee, "Fully deep blind image quality predictor," IEEE J. Sel. Topics Signal Process., Dec. 2017, vol. 11, no. 1, pp. 206-220. [26] Y. Liang, J. Wang, X. Wan, Y. Gong, and N. Zheng, “Image quality assessment using similar scene as reference,” in Proc Eur. Conf. Comput. Vis., Amsterdam, Netherlands, Oct. 2016, pp. 3–18. [27] F. Gao, Y. Wang, P. Li, M. Tan, J. Yu, and Y. Zhu, “DeepSim: Deep similarity for image quality assessment,” Neurocomputing, Sep. 2017, vol. 125, pp. 104–114. [28] J. Kim and S. Lee. "Deep learning of human visual sensitivity in image quality assessment framework," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Honolulu, HI, USA, Jul. 2017, pp. 1676–1684. [29] Jia Deng, Wei Dong, R. Socher, Li-Jia Li, Kai Li and Li Fei-Fei, "ImageNet: A large-scale hierarchical image database," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Miami, FL, USA, Jun. 2009, pp. 248–255. [30] E. Ilg, N. Mayer, T. Saikia, M. Keuper, A. Dosovitskiy, and T. Brox, "Flownet 2.0: Evolution of optical flow estimation with deep networks," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Honolulu, HI, USA, Jul. 2017, pp. 2462–2470. [31] R. Zhao, W. Ouyang, H. Li and X. Wang, "Saliency detection by multi-context deep learning," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Boston, MA, USA, Jun. 2015, pp. 1265–1274. [32] V. Nair and G.E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in Proc. 27nd Int. Conf. Mach. Learn., Haifa, Israel, Jun. 2010, pp. 807–814. [33] S. Ioffe, C. Szegedy, "Batch normalization: Accelerating deep network training by reducing internal covariate shift," in Proc. 32nd Int. Conf. Mach. Learn., Lille, France, Jun. 2015, pp. 448–456. [34] Z. Wang, A. Bovik, H. Sheikh, E. Simoncelli, "Image quality assessment: From error visibility to structural similarity," IEEE Trans. Image Process., Apr. 2004, vol. 13, no. 4, pp. 600–612. [35] Z. Wang, E. P. Simoncelli, A. C. Bovik, M. Matthews, "Multiscale structural similarity for image quality assessment," in Proc. IEEE Asilomar Conf. Signals Systems and Computers, Nov. 2003, Pacific Grove, CA, USA, vol. 2, pp. 1398– 1402. [36] W. Xue, L. Zhang, X. Mou and A. Bovik, "Gradient magnitude similarity deviation: A highly efficient perceptual image quality index," IEEE Trans. Image Process., Feb. 2014, vol. 23, no. 2, pp. 684–695. [37] Y. Lv, G. Jiang, M. Yu, H. Xu, F. Shao and S. Liu, "Difference of Gaussian statistical features based blind image quality assessment: A deep learning approach," in Proc. 22th IEEE Int. Conf. Image Process., Quebec City, QC, Canada, Sep. 2015, pp. 2344–2348. [38] A. Mittal, A. Moorthy and A. Bovik, "No-reference image quality assessment in the spatial domain," IEEE Trans. Image Process., Aug. 2012, vol. 21, no. 12, pp. 4695–4708. [39] P. Ye, J. Kumar, L. Kang, D. Doermann, "Unsupervised feature learning framework for no-reference image quality assessment," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Providence, RI, USA, Jun. 2012, pp. 1098–1105. [40] W. Xue, X. Mou, L. Zhang, A. C. Bovik, X. Feng, "Blind image quality assessment using joint statistics of gradient magnitude and Laplacian features," IEEE Trans. Image Process., Nov. 2014, vol. 23, no. 11, pp. 4850–4862. [41] K. Gu, G. Zhai, X. Yang and W. Zhang, "Using free energy principle for blind image quality assessment," IEEE Trans. Multimedia, Nov. 2015, vol. 17, no. 1, pp. 50–63. [42] Q. Li, W. Lin, J. Xu and Y. Fang, "Blind image quality assessment using statistical structural and luminance features," IEEE Trans. Multimedia, Aug. 2016, vol. 18, no. 12, pp. 2457–2469. [43] J. Kim, H. Zeng, D. Ghadiyaram, S. Lee and L. Zhang, "Deep convolutional neural models for picture quality prediction," 2017. [Online]. Available: https://www.cs.utexas.edu/~deepti/publications/deep_iqa.pdf. [44] M. Kendall, "A new measure of rank correlation," Biometrika, 1938, vol. 30, no. 12, pp. 81. [45] R. Bradley and M. Terry, "Rank analysis of incomplete block designs: I. the method of paired comparisons," Biometrika, 1952, vol. 39, no. 34, pp. 324.
|