Using Free Energy Principle for Blind Image Quality Assessment

IEEE Transactions on Multimedia

Volumn 17, Issue 1, 2015, Pages 50-63

  Gu, Ke ^a   Zhai, Guangtao ^a   Yang, Xiaokang ^a   Zhang, Wenjun ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

Free energy; human visual system; image quality assessment (IQA); no reference (NR); structural degradation

Indexed keywords

FEATURE EXTRACTION; IMAGE PROCESSING; IMAGE QUALITY;

GENERALIZED GAUSSIAN DISTRIBUTIONS; HUMAN VISUAL SYSTEM; HUMAN VISUAL SYSTEMS; IMAGE QUALITY ASSESSMENT; IMAGE QUALITY ASSESSMENT (IQA); NO REFERENCES; STRUCTURAL DEGRADATION; STRUCTURAL INFORMATION;

FREE ENERGY;

EID: 84926456216     PISSN: 15209210     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMM.2014.2373812     Document Type: Article

References (63)

1. A. C. Bovik, "Automatic prediction of perceptual image and video quality," Proc. IEEE, vol. 101, no. 9, pp. 2008-2024, Sep. 2013.
2. G. Zhai, J. Cai, W. Lin, X. Yang, and W. Zhang, "Three dimensional scalable video adaptation via user-end perceptual quality assessment," IEEE Trans. Broadcast., vol. 54, no. 3, pp. 719-727, Sep. 2008.
3. G. Zhai, J. Cai, W. Lin, X. Yang, W. Zhang, and M. Etoh, "Cross-dimensional perceptual quality assessment for low bitrate videos," IEEE Trans. Multimedia, vol. 10, no. 7, pp. 1316-1324, Nov. 2008.
4. 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.
5. Z. Wang, E. P. Simoncelli, and A. C. Bovik, "Multi-scale structural similarity for image quality assessment," in Proc. IEEE Asilomar Conf. Signals, Syst., Comput., Nov. 2003, pp. 1398-1402.
6. A. K. Moorthy and A. C. Bovik, "Visual importance pooling for image quality assessment," IEEE J. Sel. Topics Signal Process., vol. 3, no. 2, pp. 193-201, Apr. 2009.
7. Z. Wang and Q. Li, "Information content weighting for perceptual image quality assessment," IEEE Trans. Image Process., vol. 20, no. 5, pp. 1185-1198, May 2011.
8. K. Gu, G. Zhai, X. Yang, and W. Zhang, "Self-adaptive scale transform for IQA metric," in Proc. IEEE Int. Symp. Circuits Syst., May 2013, pp. 2365-2368.
9. K. Gu, G. Zhai, X. Yang, W. Zhang, and M. Liu, "Structural similarity weighting for image quality assessment," in Proc. IEEE Int. Conf. Multimedia Expo Workshops, Jul. 2013, pp. 1-6.
10. K. Gu, G. Zhai, M. Liu, Q. Xu, X. Yang, and W. Zhang, "Adaptive high-frequency clipping for improved image quality assessment," in Proc. IEEE Vis. Commun. Image Process., Nov. 2013, pp. 1-5.
11. 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.
12. A. Liu, W. Lin, and M. Narwaria, "Image quality assessment based on gradient similarity," IEEE Trans. Image Process., vol. 21, no. 4, pp. 1500-1512, Apr. 2012.
13. J. Wu, W. Lin, G. Shi, and A. Liu, "Perceptual quality metric with internal generative mechanism," IEEE Trans. Image Process., vol. 22, no. 1, pp. 43-54, Jan. 2013.
14. K. Gu, G. Zhai, X. Yang, and W. Zhang, "A new psychovisual paradigm for image quality assessment: From differentiating distortion types to discriminating quality conditions," Signal, Image Video Process., vol. 7, no. 3, pp. 423-436, May 2013.
15. Z. Wang and A. C. Bovik, "Mean squared error: Love it or leave it?-A new look at signal fidelity measures," IEEE Signal Process. Mag., vol. 26, no. 1, pp. 98-117, Jan. 2009.
16. L. Ma, S. Li, F. Zhang, and K. N. Ngan, "Reduced-reference image quality assessment using reorganized DCT-based image representation," IEEE Trans. Multimedia, vol. 13, no. 4, pp. 824-829, Aug. 2011.
17. G. Zhai, X. Wu, X. Yang, W. Lin, and W. Zhang, "A psychovisual quality metric in free-energy principle," IEEE Trans. Image Process., vol. 21, no. 1, pp. 41-52, Jan. 2012.
18. A. Rehman and Z. Wang, "Reduced-reference image quality assessment by structural similarity estimation," IEEE Trans. Image Process., vol. 21, no. 8, pp. 3378-3389, Aug. 2012.
19. K. Gu, G. Zhai, X. Yang, and W. Zhang, "A new reduced-reference image quality assessment using structural degradation model," in Proc. IEEE Int. Symp. Circuits Syst., May 2013, pp. 1095-1098.
20. K. Gu, G. Zhai, X. Yang, W. Zhang, and M. Liu, "Subjective and objective quality assessment for images with contrast change," in Proc. IEEE Int. Conf. Image Process., Sep. 2013, pp. 383-387.
21. Z. Wang, H. R. Sheikh, and A. C. Bovik, "No-reference perceptual quality assessment of JPEG compressed images," in Proc. IEEE Int. Conf. Image Process., Sep. 2002, pp. 477-480.
22. P. Marziliano, F. Dufaux, S. Winkler, and T. Ebrahimi, "A no-reference perceptual blur metric," in Proc. IEEE Int. Conf. Image Process., Sep. 2002, pp. 57-60.
23. H. R. Sheikh, A. C. Bovik, and L. K. Cormack, "No-reference quality assessment using natural scene statistics: JPEG2000," IEEE Trans. Image Process., vol. 14, no. 12, pp. 1918-1927, Dec. 2005.
24. D. Zoran and Y. Weiss, "Scale invariance and noise in natural images," in Proc. IEEE Int. Conf. Comput. Vis., Sep. 2009, pp. 2209-2216.
25. G. Zhai and X. Wu, "Noise estimation using statistics of natural images," in Proc. IEEE Int. Conf. Image Process., Sep. 2011, pp. 1857-1860.
26. R. Ferzli and L. 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.
27. N. D. Narvekar and L. J. Karam, "A no-reference image blur metric based on the cumulative probability of blur detection (CPBD)," IEEE Trans. Image Process., vol. 20, no. 9, pp. 2678-2683, Sep. 2011.
28. K. Gu, G. Zhai, M. Liu, X. Yang, and W. Zhang, "Details preservation inspired blind quality metric of tone mapping methods," in Proc. IEEE Int. Symp. Circuits Syst., Jun. 2014, pp. 518-521.
29. K. Friston, J. Kilner, and L. Harrison, "A free energy principle for the brain," J. Physiol. Paris, vol. 100, pp. 70-87, 2006.
30. K. Friston, "The free-energy principle: A unified brain theory," Nature Rev. Neurosci., vol. 11, pp. 127-138, 2010.
31. 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.
32. 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.
33. 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.
34. K. Gu, G. Zhai, X. Yang, W. Zhang, and L. Liang, "No-reference image quality assessment metric by combining free energy theory and structural degradation model," in Proc. IEEE Int. Conf. Multimedia Expo, Jul. 2013, pp. 1-6.
35. A. Mittal, R. Soundararajan, and A. C. Bovik, "Making a 'completely blind' image quality analyzer," IEEE Signal Process. Lett., vol. 22, no. 3, pp. 209-212, Mar. 2013.
36. W. Xue, L. Zhang, and X. Mou, "Learning without human scores for blind image quality assessment," in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recog., Jun. 2013, pp. 995-1002.
37. 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.
38. K. Gu, G. Zhai, X. Yang, and W. Zhang, "Hybrid no-reference quality metric for singly and multiply distorted images," IEEE Trans. Broadcast., vol. 60, no. 3, pp. 555-567, Sep. 2014.
39. H. R. Sheikh, Z. Wang, L. Cormack, and A. C. Bovik, LIVE image quality assessment database release 2, 2006 [Online]. Available: http:// live.ece.utexas.edu/research/quality
40. 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," Advances Modern Radioelectron., vol. 10, pp. 30-45, 2009.
41. E. C. Larson and D. M. Chandler, Categorical image quality (CSIQ) database, 2009 [Online]. Available: http://vision.okstate.edu/csiq
42. A. Ninassi, P. Le Callet, and F. Autrusseau, Subjective quality assess-ment-IVC database, 2008 [Online]. Available: http://www2.irccyn.ec-nantes.fr/ivcdb
43. Y. Horita, K. Shibata, Y. Kawayoke, and Z. M. P. Sazzad, MICT image quality evaluation database, 2008 [Online]. Available: http://mict.eng.u-toyama.ac.jp/mict/index2.html
44. B. Schökopf, A. J. Smola, R. C. Williamson, and P. L. Bartlett, "New support vector algorithms," Neural Comput., vol. 12, no. 5, pp. 1207-1245, 2000.
45. D. C. Knill and A. Pouget, "The Bayesian brain: The role of uncertainty in neural coding and computation," Trends Neurosci., vol. 27, no. 12, pp. 712-719, 2004.
46. J. Rissanen, Jr. and G. G. Langdon, "Universal modeling and coding," IEEE Trans. Inf. Theory, vol. 27, no. 1, pp. 12-23, Jan. 1981.
47. H. Barlow, , W. Rosenblith, Ed., Cognitive Psychology. Cambridge, MA, USA: MIT Press, 1961.
48. R. Linsker, "Perceptual neural organisation: Some approaches based on network models and information theory," Annu. Rev. Neurosci., vol. 13, pp. 257-281, 1990.
49. H. Attias, "A variational Bayesian framework for graphical models," Proc. Adv. Neural Inf. Process. Syst., vol. 12, pp. 209-215, 2000.
50. X. Wu, G. Zhai, X. Yang, and W. Zhang, "Adaptive sequential prediction of multidimensional signals with applications to lossless image coding," IEEE Trans. Image Process., vol. 20, no. 1, pp. 36-42, Jan. 2011.
51. D. Martin, C. Fowlkes, D. Tal, and J. Malik, "A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics," in Proc. IEEE Int. Conf. Comput. Vis., 2001, vol. 2, pp. 416-423.
52. M. C. Morrone, J. Ross, D. C. Burr, and R. Owens, "Mach bands are phase dependent," Nature, vol. 324, pp. 250-253, Nov. 1986.
53. P. Kovesi, "Image features from phase congruency," Videre: J. Comp. Vis. Res., vol. 1, no. 3, pp. 1-26, 1999.
54. B. Jöhne, H. Haubecker, and P. Geibler, Handbook of Computer Vision and Applications. New York, NY, USA: Academic, 1999.
55. D. L. Ruderman, "The statistics of natural images," Netw. Comput. Neural Syst., vol. 5, no. 4, pp. 517-548, 1994.
56. M. Carandini, D. J. Heeger, and J. A. Movshon, "Linearity and normalization in simple cells of the macaque primary visual cortex," J. Neurosci., vol. 17, no. 21, pp. 8621-8644, 1997.
57. 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.
58. C.-C. Chang and C.-J. Lin, "LIBSVM: A library for support vector machines," ACM Trans. Intelligent Symp. Technol., vol. 2, no. 3, 2011 [Online]. Available: http://www.csie.ntu.edu.tw/~cjlin/libsvm
59. VQEG. Boulder, CO, USA, "Final report from the video quality experts group on the validation of objective models of video quality assessment," Mar. 2000 [Online]. Available: http://www.vqeg.org/
60. D. Sheskin, Handbook of Parametric and Nonparametric Statistical Procedures. London, U.K.: Chapman & Hall, 2004.
61. K. Gu, G. Zhai, X. Yang, and W. Zhang, "No-reference stereoscopic IQA approach: From nonlinear effect to parallax compensation," J. Electr. Comput. Eng., vol. 2012, no. 436031, pp. 1-13, Sep. 2012.
62. D. Mackay, "Ensemble learning and evidence maximization," Proc. Adv. Neural Inf. Process. Syst, 1995.
63. S. Roberts and W. Penny, "Variational bayes for generalised autoregressive models," IEEE Trans. Signal Process., vol. 50, no. 9, pp. 2245-2257, Sep. 2002.