Universal blind image quality assessment metrics via natural scene statistics and multiple kernel learning

IEEE Transactions on Neural Networks and Learning Systems

Volumn 24, Issue 12, 2013, Pages 2013-2026

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

^a XIDIAN UNIVERSITY   (China)

^b UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

^c INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

Author keywords

Exponential decay characteristic (EDC); image quality assessment (IQA); multiple kernel learning (MKL); natural scene statistics (NSS)

Indexed keywords

DISCRETE COSINE TRANSFORM COEFFICIENTS; EXPONENTIAL DECAYS; GAUSSIAN SCALE MIXTURE MODELS; GENERALIZED GAUSSIAN DENSITY; IMAGE QUALITY ASSESSMENT (IQA); MULTIPLE KERNEL LEARNING; NATURAL SCENE STATISTICS; NON-GAUSSIAN MARGINAL DISTRIBUTION;

ALGORITHMS; FORECASTING; WAVELET TRANSFORMS;

IMAGE QUALITY;

EID: 84887908176     PISSN: 2162237X     EISSN: 21622388     Source Type: Journal    
DOI: 10.1109/TNNLS.2013.2271356     Document Type: Article

References (55)

1. Z. Wang and A. C. Bovik, Modern Image Quality Assessment. New York, NY, USA: Morgan & Claypool, 2006.
2. H. R. Sheikh, A. C. Bovik, and L. Cormack, "No-reference quality assessment using natural scene statistics: JPEG2000," IEEE Trans. Image Process., vol. 14, no. 11, pp. 1918-1927, Nov. 2005. (Pubitemid 41646081)
3. P. Marziliano, F. Dufaux, S. Winkler, and T. Ebrahimi, "Perceptual blur and ringing metrics: Application to JPEG2000," Signal Process., Image Commun., vol. 19, no. 2, pp. 163-172, Feb. 2004.
4. S. Suresh, R. V. Babu, H. J. Kim, "No-reference image quality assessment using modified extreme learning machine classifier," Appl. Soft Comput., vol. 9, no. 2, pp. 541-552, Mar. 2009.
5. S. Suthaharan, "No-reference visually significant blocking artifact metric for natural scene images," Signal Process., vol. 89, no. 8, pp. 1647-1652, Aug. 2009.
6. R. Ferzli and L. J. Karam, "A no-reference objective image sharpness metric based on the notion of just noticeable blur (JNB)," IEEE Trans. Image Process., vol. 18, no. 4, pp. 717-728, Apr. 2009.
7. S. Wu,W. Lin, S. Xie, Z. Lu, E. P. Ong, and S. Yao, "Blind blur assessment for vision-based applications," J. Vis. Commun. Image Represent., vol. 20, no. 4, pp. 231-241, May 2009.
8. A. Ciancio, A. L. N. Targino da Costa, E. A. B. da Silva, A. Said, R. Samadani, and P. Obrador, "No-reference blur assessment of digital pictures based on multifeature classifiers," IEEE Trans. Image Process., vol. 20, no. 1, pp. 64-75, Jan. 2011.
9. S. Gabarda and G. Cristóbal, "Blind image quality assessment through anisotropy," J. Opt. Soc. Amer. A, Opt. Image Sci. Vis., vol. 24, no. 12, pp. B42-B51, Dec. 2007. (Pubitemid 351325123)
10. W. Lu, K. Zeng, D. Tao, Y. Yuan, and X. Gao, "No-reference image quality assessment in contourlet domain," Neurocomputing, vol. 73, nos. 4-6, pp. 784-794, Jan. 2010.
11. J. Shen, Q. Li, and G. Erlebacher, "Hybrid no-reference natural image quality assessment of noisy, blurry, JPEG2000, and JPEG images," IEEE Trans. Image Process., vol. 20, no. 8, pp. 2089-2098, Aug. 2011.
12. F. Gao, X. Gao, W. Lu, D. Tao, and X. Li, "An image quality assessment metric with no reference using hidden Markov tree model," Proc. SPIE, vol. 7744, pp. 774410-1-774410-7, Jul. 2010.
13. M. A. Saad, A. C. Bovik, and C. Charrier, "A DCT statistics-based blind image quality index," IEEE Signal Process. Lett., vol. 17, no. 6, pp. 583-586, Jun. 2010.
14. M. A. Saad, A. C. Bovik, and C. Charrier, "DCT statistics modelbased blind image quality assessment," in Proc. IEEE Int. Conf. Image Process., Sep. 2011, pp. 3093-3096.
15. 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.
16. A. K. Moorthy and A. C. Bovik, "A two-step framework for constructing blind image quality indices," IEEE Signal Process. Lett., vol. 17, no. 5, pp. 513-516, May 2010.
17. A. K. Moorthy and A. C. Bovik, "Blind image quality assessment: From scene statistics to perceptual quality," IEEE Trans. Image Process., vol. 20, no. 12, pp. 3350-3364, Dec. 2011.
18. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: From error visibility to structural similarity," IEEE Trans. Image Process., vol. 13, no. 4, pp. 600-612, Apr. 2004.
19. 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. (Pubitemid 43126168)
20. K. Okarma, "Colour image quality assessment using structural similarity index and singular value decomposition," in Computer Vision and Graphics (Lecture Notes in Computer Science). New York, NY, USA: Springer-Verlag, 2009, pp. 55-65.
21. L. Zhang, L. Zhang, X. Mou, and D. Zhang, "FSIM: A feature similarity index for image quality assessment," IEEE Trans. Image Process., vol. 20, no. 8, pp. 2378-2386, Aug. 2011.
22. S. Wang, D. Zheng, J. Zhao, W. J. Tam, and F. Speranza, "An image quality evaluation method based on digital watermarking," IEEE Trans. Circuits Syst. Video Technol., vol. 17, no. 1, pp. 98-105, Jan. 2007. (Pubitemid 46113454)
23. X. Gao, W. Lu, D. Tao, and X. Li, "Image quality assessment based on multiscale geometric analysis," IEEE Trans. Image Process., vol. 18, no. 7, pp. 1409-1423, Jul. 2009.
24. D. Tao, X. Li, W. Lu, X. Gao, "Reduced-reference IQA in contourlet domain," IEEE Trans. Syst., Man, Cybern., Part B, Cybern., vol. 39, no. 6, pp. 1623-1627, Dec. 2009.
25. A. Srivastava, A. Lee, E. Simoncelli, and S. Zhu, "On advances in statistical modeling of natural images," J. Math. Imaging Vis., vol. 18, no. 1, pp. 17-33, Jan. 2003.
26. M. J. Wainwright and E. P. Simoncelli, "Scale mixtures of Gaussians and the statistics of natural images," Adv. Neural Inf. Process. Syst., vol. 12, no. 1, pp. 855-861, 2000.
27. K. Sharifi and A. Leon-Garcia, "Estimation of shape parameter for generalized Gaussian distributions in subband decompositions of video," IEEE Trans. Circuits Syst. Video Technol., vol. 5, no. 1, pp. 52-56, Feb. 1995.
28. H. A. Chipman, E. D. Kolaczyk, and R. E. McCulloch, "Adaptive Bayesian wavelet shrinkage," J. Amer. Stat. Assoc., vol. 92, no. 440, pp. 1413-1421, Dec. 1997.
29. R. M. Dufour and E. L. Miller, "Statistical signal restoration with 1/f wavelet domain prior models," Signal Process., vol. 78, no. 3, pp. 209-307, Nov. 1998.
30. H. Yang, Z. Xu, J. Ye, I. King, and M. R. Lyu, "Efficient sparse generalized multiple kernel learning," IEEE Trans. Neural Netw., vol. 20, no. 5, pp. 827-839, May 2009.
31. D. J. Field, "Relationship between the statistics of natural images and the response properties of cortical cells," J. Opt. Soc. Amer., vol. 4, no. 12, pp. 2379-2394, Dec. 1987.
32. Y. Petrov and L. Zhaoping, "Local correlations, information redundancy, and sufficient pixel depth in natural images," J. Opt. Soc. Amer. A, Opt., Image Sci. Vis., vol. 20, no. 1, pp. 56-66, Jan. 2003.
33. J. K. Romberg, H. Choi, and R. G. Baraniuk, "Bayesian tree structured image modeling using wavelet domain hidden Markov models," IEEE Trans. Image Process., vol. 10, no. 7, pp. 1056-1068, Jul. 2001. (Pubitemid 32650882)
34. E. P. Simoncelli and E. H. Adelson, "Noise removal via Bayesian wavelet coring," in Proc. 3rd Int. Conf. Image Process., Sep. 1996, pp. 379-383.
35. R. L. Joshi and T. R. Fischer, "Image subband coding using arithmetic and trellis coded quantization," IEEE Trans. Circuits Syst. Video Technol., vol. 5, no. 6, pp. 515-523, Dec. 1995.
36. M. Vasconcelos and N. Vasconcelos, "Natural image statistics and low complexity feature selection," IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 2, pp. 228-244, Feb. 2009.
37. H. R. Sheikh, Z. Wang, L. Cormack, and A. C. Bovik. (2003). LIVE Image Quality Assessment Database
38. [Online]. Available: http://live.ece.utexas. edu/research/quality
39. 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. (Pubitemid 44615139)
40. L. Paninski, "Estimation of entropy and mutual information," Neural Comput., vol. 15, no. 6, pp. 1191-1253, Jun. 2003. (Pubitemid 37049793)
41. K.-R. Müller, S. Mika, G. Rätsch, K. Tsuda, and B. Schölkopf, "An introduction to kernel-based learning algorithms," IEEE Trans. Neural Netw., vol. 12, no. 2, pp. 181-201, Mar. 2001. (Pubitemid 32371478)
42. P. Bouboulis, K. Slavakis, and S. Theodoridis, "Adaptive learning in complex reproducing kernel Hilbert spaces employing Wirtinger's subgradients," IEEE Trans. Neural Netw. Learn. Syst., vol. 23, no. 3, pp. 425-438, Mar. 2012.
43. S. Liwicki, S. Zafeiriou, G. Tzimiropoulos, and M. Pantic, "Efficient online subspace learning with an indefinite kernel for visual tracking and recognition," IEEE Trans. Neural Netw. Learn. Syst., vol. 23, no. 10, pp. 1624-1636, Oct. 2012.
44. G. Lanckriet, T. De Bie, N. Cristianini, M. Jordan, and W. Noble, "A statistical framework for genomic data fusion," Bioinformatics, vol. 20, no. 16, pp. 2626-2635, 2004. (Pubitemid 39530149)
45. A. Rakotomamonjy, F. R. Bach, S. Canu, and Y. Grandvalet, "SimpleMKL," J. Mach. Learn. Res., vol. 9, pp. 2491-2521, Sep. 2008.
46. 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.
47. G. Lanckriet, N. Cristianini, L. ElGhaoui, P. Bartlett, and M. Jordan, "Learning the kernel matrix with semi-definite programming," J. Mach. Learn. Res., vol. 5, pp. 27-72, Jan. 2004.
48. G. Fung, M. Dundar, J. Bi, and B. Rao, "A fast iterative algorithm for Fisher discriminant using heterogeneous kernels," in Proc. 21th Int. Conf. Mach. Learn., 2004, p. 40.
49. S.-J. Kim, A. Magnani, and S. Boyd, "Optimal kernel selection in kernel Fisher discriminant analysis," in Proc. 23th ICML, 2006, pp. 465-472.
50. C.-C. Chang and C.-J. Lin, "LIBSVM: A library for support vector machines," ACM Trans. Intell. Syst. Technol., vol. 2, no. 27, pp. 1-27, 2011.
51. (2000, Jun.). 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. T. J. Liu, W. Lin, and C.-C. J. Kuo, "Image quality assessment using multi-method fusion," IEEE Trans. Image Process., vol. 22, no. 5, pp. 1793-807, May 2013.
53. N. Wang, J. Li, D. Tao, X. Li, and X. Gao, "Heterogeneous image transformation," Pattern Recognit. Lett., vol. 34, no. 1, pp. 77-84, Jan. 2013.
54. X. Gao, N. Wang, D. Tao, and X. Li, "Face Sketch-Photo synthesis and retrieval using sparse representation," IEEE Trans. Circuits Syst. Video Technol., vol. 22, no. 8, pp. 1213-1226, Aug. 2012.
55. X. Gao, K. Zhang, D. Tao, and X. Li, "Joint learning for single-image super-resolution via a coupled constraint," IEEE Trans. Image Process., vol. 21, no. 2, pp. 469-480, Feb. 2012.