Learning to Rank for Blind Image Quality Assessment

IEEE Transactions on Neural Networks and Learning Systems

Volumn 26, Issue 10, 2015, Pages 2275-2290

  Gao, Fei ^a   Tao, Dacheng ^b   Gao, Xinbo ^a   Li, Xuelong ^c  

^a XIDIAN UNIVERSITY   (China)

^b UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

^c XI AN INSTITUTE OF OPTICS AND PRECISION MECHANICS   (China)

Author keywords

Image quality assessment (IQA); learning preferences; learning to rank; multiple kernel learning (MKL); universal blind IQA (BIQA)

Indexed keywords

LEARNING ALGORITHMS; MILITARY PHOTOGRAPHY; QUALITY CONTROL;

IMAGE QUALITY ASSESSMENT (IQA); LEARNING PREFERENCES; LEARNING TO RANK; MULTIPLE KERNEL LEARNING; UNIVERSAL BLIND IQA (BIQA);

IMAGE QUALITY;

EID: 84943799494     PISSN: 2162237X     EISSN: 21622388     Source Type: Journal    
DOI: 10.1109/TNNLS.2014.2377181     Document Type: Article

References (59)

1. M. A. Saad, A. C. Bovik, and C. Charrier, "Blind image quality assessment: A natural scene statistics approach in the DCT domain," IEEE Trans. Image Process., vol. 21, no. 8, pp. 3339-3352, Aug. 2012.
2. C. Li, A. C. Bovik, and X. Wu, "Blind image quality assessment using a general regression neural network," IEEE Trans. Neural Netw., vol. 22, no. 5, pp. 793-799, May 2011.
3. A. K. Moorthy and A. C. Bovik, "Blind image quality assessment: From natural scene statistics to perceptual quality," IEEE Trans. Image Process., vol. 20, no. 12, pp. 3350-3364, Dec. 2011.
4. A. Mittal, A. K. Moorthy, and A. C. Bovik, "No-reference image quality assessment in the spatial domain," IEEE Trans. Image Process., vol. 21, no. 12, pp. 4695-4708, Dec. 2012.
5. H. Tang, N. Joshi, and A. Kapoor, "Learning a blind measure of perceptual image quality," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2011, pp. 305-312.
6. A. Mittal, A. K. Moorthy, and A. C. Bovik, "Making image quality assessment robust," in Proc. IEEE Conf. Rec. 46th Asilomar Conf. Signals, Syst., Comput. (ASILOMAR), Nov. 2012, pp. 1718-1722.
7. L. He, D. Tao, X. Li, and X. Gao, "Sparse representation for blind image quality assessment," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2012, pp. 1146-1153.
8. P. Ye, J. Kumar, L. Kang, and D. Doermann, "Unsupervised feature learning framework for no-reference image quality assessment," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2012, pp. 1098-1105.
9. P. Ye, J. Kumar, L. Kang, and D. Doermann, "Real-time no-reference image quality assessment based on filter learning," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2013, pp. 987-994.
10. P. Ye and D. Doermann, "No-reference image quality assessment using visual codebooks," IEEE Trans. Image Process., vol. 21, no. 7, pp. 3129-3138, Jul. 2012.
11. X. Gao, F. Gao, D. Tao, and X. Li, "Universal blind image quality assessment metrics via natural scene statistics and multiple kernel learning," IEEE Trans. Neural Netw. Learn. Syst., vol. 24, no. 12, pp. 2013-2026, Dec. 2013.
12. W. Hou and X. Gao, "Saliency-guided deep framework for image quality assessment," IEEE Trans. MultiMedia, vol. PP, no. 99, pp. 1-1, 2014. [Online]. Available: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6914467
13. W. Hou, X. Gao, D. Tao, and X. Li, "Blind image quality assessment via deep learning," IEEE Trans. Neural Netw. Learn. Syst., preprinted, 2014. [Online]. Available: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6872541
14. H. R. Sheikh, M. F. Sabir, and A. C. Bovik, "A statistical evaluation of recent full reference image quality assessment algorithms," IEEE Trans. Image Process., vol. 15, no. 11, pp. 3440-3451, Nov. 2006.
15. H. R. Sheikh, Z. Wang, L. Cormack, and A. C. Bovik. (2005). LIVE Image Quality Assessment Database Release 2. [Online]. Available: http://live.ece.utexas.edu/research/quality/subjective.htm
16. N. Ponomarenko et al., "Color image database TID2013: Peculiarities and preliminary results," in Proc. 4th Eur. Workshop Vis. Inf. Process. (EUVIP), Jun. 2013, pp. 106-111.
17. E. C. Larson and D. M. Chandler, "Most apparent distortion: Full-reference image quality assessment and the role of strategy," J. Electron. Imag., vol. 19, no. 1, p. 011006, 2010.
18. D. Jayaraman, A. Mittal, A. K. Moorthy, and A. C. Bovik, "Objective quality assessment of multiply distorted images," in Proc. IEEE Conf. Rec. 46th Asilomar Conf. Signals, Syst., Comput. (ASILOMAR), Nov. 2012, pp. 1693-1697.
19. N. Ponomarenko, V. Lukin, A. Zelensky, K. Egiazarian, M. Carli, and F. Battisti, "TID2008 - A database for evaluation of full-reference visual quality assessment metrics," Adv. Modern Radioelectron., vol. 10, no. 4, pp. 30-45, 2009.
20. L. L. Thurstone, "A law of comparative judgment," Psychol. Rev., vol. 101, no. 2, pp. 266-270, 1994.
21. K. Tsukida and M. R. Gupta, "How to analyze paired comparison data," Dept. Elect. Eng., Univ. Washington, Seattle, WA, USA, Tech. Rep. UWEETR-2011-0004, 2011.
22. D. M. Rouse, R. Pépion, P. Le Callet, and S. S. Hemami, "Tradeoffs in subjective testing methods for image and video quality assessment," Proc. SPIE, Human Vis. Electron. Imag., vol. 7517, p. 75270F, Feb. 2010.
23. Methodology for the Subjective Assessment of the Quality of Television Pictures, document Rec. ITU-R BT.500-13, 2012.
24. H. de Ridder, "Current issues and new techniques in visual quality assessment," in Proc. IEEE Int. Conf. Image Process., Sep. 1996, pp. 869-872.
25. A. Parducci and D. H. Wedell, "The category effect with rating scales: Number of categories, number of stimuli, and method of presentation," J. Experim. Psychol., Human Perception Perform., vol. 12, no. 4, pp. 496-516, 1986.
26. W. Xue, L. Zhang, and X. Mou, "Learning without human scores for blind image quality assessment," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2013, pp. 995-1002.
27. A. Mittal, G. S. Muralidhar, J. Ghosh, and A. C. Bovik, "Blind image quality assessment without human training using latent quality factors," IEEE Signal Process. Lett., vol. 19, no. 2, pp. 75-78, Feb. 2012.
28. A. Mittal, R. Soundararajan, and A. C. Bovik, "Making a 'completely blind' image quality analyzer," IEEE Signal Process. Lett., vol. 20, no. 3, pp. 209-212, Mar. 2013.
29. R. A. Bradley and M. E. Terry, "Rank analysis of incomplete block designs: I. The method of paired comparisons," Biometrika, vol. 39, nos. 3-4, pp. 324-345, 1952.
30. R. Halonen, S. Westman, and P. Oittinen, "Naturalness and interestingness of test images for visual quality evaluation," Proc. SPIE, Image Qual. Syst. Perform. VIII, vol. 7867, pp. 78670Z-1-78670Z-12, Jan. 2011.
31. H. Li, "A short introduction to learning to rank," IEICE Trans. Inf. Syst., vol. E94-D, no. 10, pp. 1854-1862, 2011.
32. Z. Xu, R. Jin, H. Yang, I. King, and M. R. Lyu, "Simple and efficient multiple kernel learning by group lasso," in Proc. 27th Int. Conf. Mach. Learn. (ICML), 2010, pp. 1175-1182.
33. H. Chen, Y. Tang, L. Li, Y. Yuan, X. Li, and Y. Tang, "Error analysis of stochastic gradient descent ranking," IEEE Trans. Cybern., vol. 43, no. 3, pp. 898-909, Jun. 2013.
34. D. Tao, L. Jin, Y. Zhao, and X. Li, "Rank preserving sparse learning for Kinect based scene classification," IEEE Trans. Cybern., vol. 43, no. 5, pp. 1406-1417, Oct. 2013.
35. J. Yu, Y. Rui, and D. Tao, "Click rediction for web image reranking using multimodal sparse coding," IEEE Trans. Image Process., vol. 23, no. 5, pp. 2019-2032, May 2014.
36. J. Yu, D. Tao, and Y. Rui, "Learning to rank using user clicks and visual features for image retrieval," IEEE Trans. Cybern., preprinted, Jun. 2014. [Online]. Available: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6867349
37. Y. Pang, Z. Ji, P. Jing, and X. Li, "Ranking graph embedding for learning to rerank," IEEE Trans. Neural Netw. Learn. Syst., vol. 24, no. 8, pp. 1292-1303, Aug. 2013.
38. Z. Cao, T. Qin, T.-Y. Liu, M.-F. Tsai, and H. Li, "Learning to rank: From pairwise approach to listwise approach," in Proc. 24th Int. Conf. Mach. Learn., 2007, pp. 129-136.
39. L. Rigutini, T. Papini, M. Maggini, and F. Scarselli, "SortNet: Learning to rank by a neural preference function," IEEE Trans. Neural Netw., vol. 22, no. 9, pp. 1368-1380, Sep. 2011.
40. O. Chapelle and S. S. Keerthi, "Efficient algorithms for ranking with SVMs," Inf. Retr., vol. 13, no. 3, pp. 201-215, 2010.
41. E. Hüllermeier, J. Fürnkranz, W. Cheng, and K. Brinker, "Label ranking by learning pairwise preferences," Artif. Intell., vol. 172, nos. 16-17, pp. 1897-1916, 2008.
42. R. Herbrich, T. Graepel, and K. Obermayer, "Large margin rank boundaries for ordinal regression," in Advances in Neural Information Processing Systems. Cambridge, MA, USA: MIT Press, 1999, pp. 115-132.
43. Z. Zheng, H. Zha, T. Zhang, O. Chapelle, K. Chen, and G. Sun, "A general boosting method and its application to learning ranking functions for web search," in Advances in Neural Information Processing Systems 20. Red Hook, NY, USA: Curran Associates, 2007, pp. 1697-1704.
44. A. Srivastava, A. B. Lee, E. P. Simoncelli, and S.-C. Zhu, "On advances in statistical modeling of natural images," J. Math. Imag. Vis., vol. 18, no. 1, pp. 17-33, 2003.
45. G. R. G. Lanckriet, T. De Bie, N. Cristianini, M. I. Jordan, and W. S. Noble, "A statistical framework for genomic data fusion," Bioinformatics, vol. 20, no. 16, pp. 2626-2635, 2004.
46. A. Rakotomamonjy, F. Bach, S. Canu, and Y. Grandvalet, "SimpleMKL," J. Mach. Learn. Res., vol. 9, pp. 2491-2521, Aug. 2008.
47. X. Xu, I. W. Tsang, and D. Xu, "Soft margin multiple kernel learning," IEEE Trans. Neural Netw. Learn. Syst., vol. 24, no. 5, pp. 749-761, May 2013.
48. C. Li, M. Georgiopoulos, and G. C. Anagnostopoulos, "A unifying framework for typical multitask multiple kernel learning problems," IEEE Trans. Neural Netw. Learn. Syst., vol. 25, no. 7, pp. 1287-1297, Jul. 2014.
49. M. Gönen and E. Alpaydin, "Multiple kernel learning algorithms," J. Mach. Learn. Res., vol. 12, pp. 2211-2268, Feb. 2011.
50. C.-C. Chang and C.-J. Lin, "LIBSVM: A library for support vector machines," ACM Trans. Intell. Syst. Technol., vol. 2, no. 3, pp. 1-27, 2011, Art. ID 27.
51. VQEG. (2000). Final Report From the Video Quality Experts Group on the Validation of Objective Models of Video Quality Assessment. [Online]. Available: http://www.vqeg.org/
52. H. R. Sheikh and A. C. Bovik, "Image information and visual quality," IEEE Trans. Image Process., vol. 15, no. 2, pp. 430-444, Feb. 2006.
53. N. Wang, D. Tao, X. Gao, X. Li, and J. Li, "A comprehensive survey to face hallucination," Int. J. Comput. Vis., vol. 106, no. 1, pp. 9-30, 2014.
54. N. Wang, D. Tao, X. Gao, X. Li, and J. Li, "Transductive face sketch-photo synthesis," IEEE Trans. Neural Netw. Learn. Syst., vol. 24, no. 9, pp. 1364-1376, Sep. 2013.
55. X. Li, L. Mou, and X. Lu, "Scene parsing from an MAP perspective," IEEE Trans. Cybern., vol. PP, no. 99, pp. 1-1, 2014. [Online]. Available: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6945874
56. L. Guo, Y. Xiong, Q. Huang, and X. Li, "Image esthetic assessment using both hand-crafting and semantic features," Neurocomputing, vol. 143, pp. 14-26, Nov. 2014.
57. S. Li, H. Qin, and A. Hao, "Multi-scale local features based on anisotropic heat diffusion and global eigen-structure," Sci. Chin. Inf. Sci., vol. 56, no. 11, pp. 110901-1-110901-10, 2013.
58. L. Zhou, D. Hu, and Z. Zhou, "Scene recognition combining structural and textural features," Sci. Chin. Inf. Sci., vol. 56, no. 7, pp. 078106-1-078106-14, 2013.
59. J. Yu, R. Hong, M. Wang, and J. You, "Image clustering based on sparse patch alignment framework," Pattern Recognit., vol. 47, no. 11, pp. 3512-3519, Nov. 2014.