Most apparent distortion: Full-reference image quality assessment and the role of strategy

Journal of Electronic Imaging

Volumn 19, Issue 1, 2010, Pages

  Larson, Eric C ^a   Chandler, Damon M ^b  

^a University of Washington   (United States)

^b Oklahoma State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPEARANCE BASED; CONTRAST MASKING; FREQUENCY COMPONENTS; HIGH QUALITY IMAGES; HUMAN VISUAL SYSTEMS; IMAGE QUALITY ASSESSMENT; LOCAL STATISTICS; LOW QUALITIES; MULTIPLE STRATEGY; QUALITY ASSESSMENT; REFERENCE IMAGE; SUBJECT MATTERS; SUBJECTIVE RATING;

IMAGE QUALITY;

EID: 76549113942     PISSN: 10179909     EISSN: 1560229X     Source Type: Journal    
DOI: 10.1117/1.3267105     Document Type: Article

References (69)

1. B. Moulden, F. A. A. Kingdom, and L. F. Gatley, "The standard deviation of luminance as a metric for contrast in random-dot images," Perception 19, 79-101 (1990).
2. J. L. Mannos and D. J. Sakrison, "The effects of a visual fidelity criterion on the encoding of image," IEEE Trans. Inf. Theory 20, 525-535 (1974).
3. F. Lukas and Z. Budrikis, "Picture quality prediction based on a visual model," IEEE Trans. Commun. 30(7), 1679-1692 (1982).
4. N. Nill, "A visual model weighted cosine transform for image compression and quality assessment," IEEE Trans. Commun. 33(6), 551-557 (1985).
5. S. Daly, "Visible differences predictor: an algorithm for the assessment of image fidelity," in Digital Images and Human Vision, A. B. Watson, Ed., pp. 179-206 (1993).
6. P. C. Teo and D. J. Heeger, "Perceptual image distortion," Proc. SPIE 2179, 127-141 (1994).
7. S. J. P. Westen, R. L. Lagendijk, and J. Biemond, "Perceptual image quality based on a multiple channel HVS model," Intl. Conf. Acoustics, Speech, Signal Process. 4, 2351-2354 (1995).
8. J. Lubin, "A visual discrimination model for imaging system designand evaluation," in Vision Models for Target Detection and Recognition, E. Peli, Ed., pp. 245-283, World Scientific, New York (1995).
9. C. J. van den Branden Lambrecht, "A working spatio-temporal model of the human visual system for image representation and quality assessment applications," in Proc. IEEE Intl. Conf. Acoustics, Speech, Signal Process., pp. 2291-2294 (May 1996).
10. Y. Lai and C. J. Kuo, "Image quality measurement using the haar wavelet," Proc. SPIE 3169, 127 (1997).
11. M. Miyahara, K. Kotani, and V. R. Algazi, "Objective picture quality scale (PQS( for image coding," IEEE Trans. Commun. 46(9), 1215-1226 (1998).
12. W. Osberger, N. Bergmann, and A. Maeder, "An automatic image quality assessment technique incorporating higher level perceptual factors," Proc. IEEE Intl. Conf. Image Process., 3, 414-418 (1998).
13. S. Winkler, "A perceptual distortion metric for digital color images," Proc. IEEE Intl. Conf. Image Process., 3, 399-403 (1998).
14. A. Bradley, "A wavelet visible difference predictor," IEEE Trans. Image Process. 8, 717-730 (May 1999).
15. S. Winkler, "Visual quality assessment using a contrast gain control model," in IEEE Signal Process. Soc. Workshop Multimedia Signal Process., pp. 527-532 (Sep. 1999).
16. J. Lubin, M. Brill, A. De Vries, and O. Finard, "Method and apparatus for assessing the visibility of differences between two image sequences," U.S. Patent 5,974,159 (1999).
17. "Jndmetrix technology," Sarnoff Corp., see http://www.sarnoff. com/
18. N. Damera-Venkata, T. D. Kite, W. S. Geisler, B. L. Evans, and A. C. Bovik, "Image quality assessment based on a degradation model," IEEE Trans. Image Process. 9, 636-650 (2000).
19. M. Carnec, P. L. Callet, and D. Barba, "An image quality assessment method based on perception of structural information," in ICIP 2003, 2, 185-188 (2003).
20. See http://foulard.ece.cornell.edu/VSNR.html.
21. Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, "Image quality assessment: from error visibility to structural similarity," IEEE Trans. Image Process. 13, 600-612 (2004).
22. H. R. Sheikh, A. C. Bovik, and G. de Veciana, "An information fidelity criterion for image quality assessment using natural scene statistics," IEEE Trans. Image Process. 14(12), 2117-2128 (2005).
23. G. Zhai, W. Zhang, X. Yang, and Y. Xu, "Image quality assessment metrics based on multi-scale edge presentation," IEEE Workshop Signal Process. Syst. Design Implement., pp. 331-336 (2005).
24. A. Shnayderman, A. Gusev, and A. M. Eskicioglu, "An SVD-based grayscale image quality measure for local and global assessment," IEEE Trans. Image Process. 15(2), 422-429 (2006).
25. R. L. DeValois and K. K. DeValois, Spatial Vision, Oxford University Press, Oxford, UK (1990).
26. J. Schulkin, Cognitive Adaptation, 1st ed., Cambridge University Press, Boston, MA (2008).
27. H. R. Sheikh, Z. Wang, A. C. Bovik, and L. K. Cormack, "Image and video quality assessment research at live," see http://live.ece.utexas.edu/ research/quality/
28. C. C. Taylor, Z. Pizlo, J. P. Allebach, and C. A. Bouman, "Image quality assessment with a gabor pyramid model of the human visual system," Proc. SPIE 3016(1), 58-69 (1997).
29. P. LeCallet, A. Saadane, and D. Barba, "Frequency and spatial pooling of visual differences for still image quality assessment," Proc. SPIE 3959, 595-603 (2000).
30. T. N. Pappas, T. A. Michel, and R. O. Hinds, "Supra-threshold perceptual image coding," Proc. ICIP, pp. 237-240 (1996).
31. M. P. Eckert and A. P. Bradley, "Perceptual quality metrics applied to still image compression," Signal Process. 70, 177-200 (1998).
32. A. Ninassi, O. L. Meur, P. L. Callet, and D. Barba, "Which semi-local visual masking model forwavelet based image quality metric?" in ICIP, pp. 1180-1183 (2008).
33. N. Graham, Visual Pattern Analyzers, Oxford University Press, New York (1989).
34. A. B. Watson and J. A. Solomon, "A model of visual contrast gain control and pattern masking," J. Opt. Soc. Am. A 14, 2378-2390 (1997).
35. W. Zeng, S. Daly, and S. Lei, "An overview of the visual optimization tools in jpeg 2000," Signal Process. Image Commun. 17, 85-104 (2001).
36. D. M. Chandler and S. S. Hemami, "Dynamic contrast-based quantization for lossy wavelet image compression," IEEE Trans. Image Process. 14(4), 397-410 (2005).
37. Z. Wang, E. P. Simoncelli, and A. C. Bovik, "Multi-scale structural similarity for image quality assessment," in Proc. Asilomar Conf. Signals, Syst. Computers, Vol.2, p. 1398 (Nov. 2003).
38. C. L. Yang, W. R. Gao, and L. M. Po, "Discrete wavelet transformbased structural similarity for image quality assessment," in ICIP, pp. 377-380 (2008).
39. M. Zhang and X. Mou, "A psychovisual image quality metric based on multi-scale structure similarity," in ICIP, pp. 381-384 (2008).
40. Z. Wang, L. Lu, and A. C. Bovik, "Video quality assessment based on structural distortion measurement," Signal Process. Image Commun. 19(2), pp. 121-132 (2004).
41. S. Ye, K. Su, and C. Xiao, "Video quality assessment based on edge structural similarity," Image Signal Process. Congress 3, 445-448 (2008).
42. U. Engelke and H. J. Zepernick, "Pareto optimalweighting of structural impairments for wireless imaging quality assessment," in ICIP, pp. 373-376 (2008).
43. H. R. Sheikh and A. C. Bovik, "Image information and visual quality," IEEE Trans. Image Process. 15(2), 430-444 (2006).
44. M. Liu and X. Yang, "A new image quality approach based on decision fusion," Fuzzy Syst. Knowledge Discovery, 4th Intl. Conf., 4, 10-14 (2008).
45. B. Girod, "What's wrong with mean-squared error?" in Digital Images and Human Vision, A. B. Watson, Ed., pp. 207-220 (1993).
46. M. Stokes, M. Anderson, S. Chandrasekar, and R. Motta, "A standard default color space for the internet-srgb," see http://www.w3.org/Graphics/ Color/sRGB (1996).
47. S. J. Daly, "Subroutine for the generation of a human visual contrast sensitivity function," Eastman Kodak Tech. Report 233203y (1987).
48. T. Mitsa and K. Varkur, "Evaluation of contrast sensitivity functions for the formulation of quality measures incorporated in halftoning algorithms," IEEE Intl. Conf. Acoustics, Speech, Signal Process., pp. 301-304 (1993).
49. D. M. Chandler and S. S. Hemami, "Effects of natural images on the detectability of simple and compound wavelet subband quantization distortions," J. Opt. Soc. Am. A 20, pp. 1167-1180 (July 2003).
50. A. K. Jain and F. Farrokhnia, "Unsupervised texture segmentation using gabor filters," Pattern Recogn. 24, 1167-1186 (May 1991).
51. D. J. Field, "Relations between the statistics of natural images and the response properties of cortical cells," J. Opt. Soc. Am. A 4, 2379-2394 (1987).
52. B. A. Olshausen and D. J. Field, "Sparse coding with an overcomplete basis set: a strategy employed by v1?" Vision Res. 37, 3311-3325 (1997).
53. F. A. A. Kingdom, A. Hayes, and D. J. Field, "Sensitivity to contrast histogram differences in synthetic wavelet-textures," Vision Res. 41, 585-598 (1995).
54. D. M. Chandler and S. S. Hemai, "Vsnr: a wavelet-based visual signal-to-noise ratio for natural images," IEEE Trans. Image Process. 16(9), 2284-2298 (2007).
55. N. Ponomarenko, F. Battisti, K. Egiazarian, J. Astola, and V. Lukin, "Metrics performance comparison for color image database," in 4th Intl. Workshop Video Process. Quality Metrics Consumer Electron., 6, p. 6 (Jan. 2009).
56. H. R. Sheikh, Z. Wang, A. C. Bovik, and L. K. Cormack, "Image and video quality assessment research at LIVE," see http://live.ece.utexas.edu/ research/quality/.
57. Y. Horita, K. Shibata, and Z. M. Parvez Saddad, "Subjective quality assessment toyama database," see http://mict.eng.u-toyama.ac.jp/mict/ (2008).
58. See http://vision.okstate.edu/csiq/.
59. E. C. Larson and D. M. Chandler, "Most apparent distortion: a dual strategy for full-reference image quality assessment," Proc. SPIE 7242, 72420S (2009).
60. VQEG, "Final report from the video quality experts group on the validation of objective models of video quality assessment, phase ii," see http://www.vqeg.org (Aug. 2003).
61. H. Sheikh, M. Sabir, and A. Bovik, "A statistical evaluation of recent full reference image quality assessment algorithms," IEEE Trans. Image Process. 15, 1349-1364 (2006).
62. Z. Wang, E. P. Simoncelli, and A. C. Bovik, "Multi-scale structural similarity for image quality assessment," 37th IEEE Asilomar Conf. Signals, Syst. Computers, Vol.2, pp. 1398 (2003).
63. M. D. Gaubatz, D. M. Rouse, and S. S. Hemami, "MeTriX MuX," http://foulard.ece.cornell.edu/gaubatz/metrix-mux/.
64. A. K. Bera and C. M. Jarque, "Efficient tests for normality, homoscedasticity and serial independence of regression residuals," Econ. Lett. 6, 255-259 (1980).
65. E. Peli, L. E. Arend, G. M. Young, and R. B. Goldstein, "Contrast sensitivity to patch stimuli: effects of spatial bandwidth and temporal presentation," Spatial Vis. 7, 1-14 (1993).
66. A. B. Watson, G. Y. Tangand, J. A. Solomon, and J. Villasenor, "Visibility of wavelet quantization noise," IEEE Trans. Image Process. 6, 1164-1175 (1997).
67. D. M. Chandler, M. D. Gaubatz, and S. S. Hemami, "A patch based structural masking model with an application to compression," in EURASIP J. Image Video Process. 2009, 649316 (2009).
68. S. S. Hemami, D. M. Chandler, B. C. Chern, and J. A. Moses, "Suprathreshold visual psychophysics and structure-based visual masking," in Proc. SPIE 6077, 60770O (2006).
69. D. M. Chandler and S. S. Hemami, "Suprathreshold image compression based on contrast allocation and global precedence," Proc. SPIE 5007, 73-86 (2003).