OSA's Digital Library

Journal of the Optical Society of America A

Journal of the Optical Society of America A

| OPTICS, IMAGE SCIENCE, AND VISION

  • Editor: Stephen A. Burns
  • Vol. 24, Iss. 12 — Dec. 1, 2007
  • pp: B42–B51

Blind image quality assessment through anisotropy

Salvador Gabarda and Gabriel Cristóbal  »View Author Affiliations


JOSA A, Vol. 24, Issue 12, pp. B42-B51 (2007)
http://dx.doi.org/10.1364/JOSAA.24.000B42


View Full Text Article

Enhanced HTML    Acrobat PDF (861 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

We describe an innovative methodology for determining the quality of digital images. The method is based on measuring the variance of the expected entropy of a given image upon a set of predefined directions. Entropy can be calculated on a local basis by using a spatial/spatial-frequency distribution as an approximation for a probability density function. The generalized Rényi entropy and the normalized pseudo-Wigner distribution (PWD) have been selected for this purpose. As a consequence, a pixel-by-pixel entropy value can be calculated, and therefore entropy histograms can be generated as well. The variance of the expected entropy is measured as a function of the directionality, and it has been taken as an anisotropy indicator. For this purpose, directional selectivity can be attained by using an oriented 1-D PWD implementation. Our main purpose is to show how such an anisotropy measure can be used as a metric to assess both the fidelity and quality of images. Experimental results show that an index such as this presents some desirable features that resemble those from an ideal image quality function, constituting a suitable quality index for natural images. Namely, in-focus, noise-free natural images have shown a maximum of this metric in comparison with other degraded, blurred, or noisy versions. This result provides a way of identifying in-focus, noise-free images from other degraded versions, allowing an automatic and nonreference classification of images according to their relative quality. It is also shown that the new measure is well correlated with classical reference metrics such as the peak signal-to-noise ratio.

© 2007 Optical Society of America

OCIS Codes
(100.2000) Image processing : Digital image processing
(100.6640) Image processing : Superresolution
(110.3000) Imaging systems : Image quality assessment
(330.6180) Vision, color, and visual optics : Spectral discrimination

History
Original Manuscript: February 26, 2007
Revised Manuscript: July 9, 2007
Manuscript Accepted: July 18, 2007
Published: September 26, 2007

Virtual Issues
Vol. 3, Iss. 1 Virtual Journal for Biomedical Optics

Citation
Salvador Gabarda and Gabriel Cristóbal, "Blind image quality assessment through anisotropy," J. Opt. Soc. Am. A 24, B42-B51 (2007)
http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-24-12-B42


Sort:  Year  |  Journal  |  Reset  

References

  1. Z. Wang and A. Bovik, "Why is image quality assessment so difficult?" IEEE International Conference on Acoustics Speech and Signal Processing (IEEE, 2002), pp. 3313-3316.
  2. Z. Zhang and R. S. Blum, "A categorization of multiscale-decomposition-based image fusion schemes with a performance study for a digital camera application," Proc. IEEE 87, 1315-1328 (1999). [CrossRef]
  3. Z. Wang and A. C. Bovik, "A universal image quality index," IEEE Signal Process. Lett. 9, 81-84 (2002). [CrossRef]
  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. 13, 600-612 (2004). [CrossRef]
  5. H. R. Sheikh, A. C. Bovik, and G. DeVeciana, "An information fidelity criterion for image quality assessment using natural scene statistics," IEEE Trans. Image Process. 14, 2117-2128 (2005). [CrossRef]
  6. H. R. Sheikh and A. C. Bovik, "Image information and visual quality," IEEE Trans. Image Process. 15, 430-444 (2006). [CrossRef]
  7. H. R. Sheikh, A. C. Bovik, and L. K. Cormack, "No-reference quality assessment using natural scene statistics: JPEG2000," IEEE Trans. Image Process. 14, 1918-1927 (2005). [CrossRef]
  8. C. E. Shannon and W. Weaver, The Mathematical Theory of Communication (University of Illinois Press, 1949).
  9. E. N. Kirsanova and M. G. Sadovsky, "Entropy approach in the analysis of anisotropy of digital images," Open Syst. Inf. Dyn. 9, 239-250 (2002). [CrossRef]
  10. W. J. Williams, M. L. Brown, and A. O. Hero, "Uncertainity, information and time-frequency distributions," Proc. SPIE 1566, 144-156 (1991). [CrossRef]
  11. P. Marziliano, F. Dufaux, S. Winkler, and T. Ebrahimi, "Perceptual blur and ringing metrics: application to JPEG2000," Signal Process. 19, 163-172 (2004).
  12. N. Cvejic, C. N. Canagarajah, and D. R. Bull, "Image fusion metric based on mutual information and Tsallis entropy," Electron. Lett. 42, 626-627 (2006). [CrossRef]
  13. C. S. Xydeas and V. Petkovic, "Objective image fusion performance measure," Electron. Lett. 36, 308-309 (2000). [CrossRef]
  14. G. Qu, D. Zhang, and P. Yang, "Information measure for performance of image fusion," Electron. Lett. 38, 313-315 (2002). [CrossRef]
  15. R. Danserau and W. Kinsner, "New relative multifractal dimension measures," in IEEE International Conference on Acoustics Speech and Signal Processing (IEEE, 2001), pp. 1741-1744.
  16. L. Stankovic, "A measure of some time-frequency distributions concentration," Signal Process. 81, 621-631 (2001). [CrossRef]
  17. N. Wiener, Cybernetics (Wiley, 1948).
  18. A. Rényi, "Some fundamental questions of information theory," in Selected Papers of Alfréd Rényi, PálTurán, ed. (Akadémiai Kiadó, 1976), Vol. 3, pp. 526-552 A. Rényi,[Originally in Magy. Tud. Akad. III Oszt. Kózl. 10, 251-282 (1960)].
  19. T. H. Sang and W. J. Williams, "Rényi information and signal dependent optimal kernel design," in IEEE International Conference on Acoustics Speech and Signal Processing (IEEE, 1995), Vol. 2, pp. 997-1000.
  20. P. Flandrin, R. G. Baraniuk, and O. Michel, "Time-frequency complexity and information," in IEEE International Conference on Acoustics Speech and Signal Processing (IEEE, 1994), Vol. 3, pp. 329-332.
  21. R. Eisberg and R. Resnick, Quantum Physics (Wiley, 1974).
  22. L. D. Jacobson and H. Wechsler, "Joint spatial/spatial-frequency representation," Signal Process. 14, 37-68 (1988). [CrossRef]
  23. E. Wigner, "On the quantum correction for thermodynamic equilibrium," Phys. Rev. 40, 749-759 (1932). [CrossRef]
  24. T. A. C. M. Claasen and W. F. G. Mecklenbräuker, "The Wigner distribution--a tool for time frequency analysis, Parts I-III," Philips J. Res. 35, 217-250, 276-300, 372-389 (1980).
  25. K. H. Brenner, "A discrete version of the Wigner distribution function," in Proceedings of EURASIP, Signal Processing II: Theories and Applications (North Holland, 1983), pp. 307-309.
  26. B. Li, M. R. Peterson, and R. D. Freeman, "Oblique effect: a neural bias in the visual cortex," J. Neurophysiol. 90, 204-217 (2003). [CrossRef] [PubMed]
  27. E. Switkes, M. J. Mayer, and J. A. Sloan, "Spatial frequency analysis of the visual environment: anisotropy and the carpentered environment hypothesis," Vision Res. 18, 1393-1399 (1978). [CrossRef] [PubMed]
  28. R. J. Baddeley and P. J. B. Hancock, "A statistical analysis of natural images matches psychophysically derived orientation tuning curves," Proc. R. Soc. London, Ser. B 246, 219-223 (1991). [CrossRef]
  29. P. J. B. Hancock, R. J. Baddeley, and L. S. Smith, "The principal components of natural images," Network Comput. Neural Syst. 3, 61-70 (1992). [CrossRef]
  30. J. Haung and D. Mumford, "Statistics of natural images and models," in Proceedings of the ICCV, 1, 541-547 (1999).
  31. B. C. Hansen and E. A. Essock, "A horizontal bias in human visual processing of orientation and its correspondence to the structural components of natural scenes," J. Vision 4, 1044-1060 (2004). [CrossRef]
  32. M. S. Keil and G. Cristóbal, "Separating the chaff from the wheat: possible origins of the oblique effect," J. Opt. Soc. Am. A 17, 697-710 (2000). [CrossRef]
  33. R. Román, J. J. Quesada, and J. Martínez, "Multiresolution-information analysis for images," Signal Process. 24, 77-91 (1991). [CrossRef]
  34. Y. Qu, Z. Pu, H. Zhao, and Y. Zhao, "Comparison of different quality assessment functions in autoregulative illumination intensity algorithms," Opt. Eng. (Bellingham) 45, 117-201 (2006). [CrossRef]
  35. S. Fischer, F. Sroubek, L. Perrinet, R. Redondo, and G. Cristóbal, "Self-invertible 2D Gabor wavelets," Int. J. Comput. Vis. available at http://www.springerlink.com/content/07q411454q407047/fulltext.pdf.
  36. F. Sroubek, G. Cristóbal, and J. Flusser, "Combined superresolution and blind deconvolution," in Information Optics: 5th International Workshop (American Institute of Physics, 2006), paper CP860, pp. 15-26.
  37. H. R. Sheikh, Z. Wang, L. Cormack, and A. C. Bovik, "LIVE image quality assessment database," Release 2, 2005 [Online]. Available at http://live.ece.utexas.edu/research/quality.
  38. 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. 15, 3440-3451 (2006). [CrossRef] [PubMed]
  39. M. Y. Shen and C. C. Jay Kuo, "Review of postprocessing techniques for compression artifact removal," J. Visual Commun. Image Represent 9, 2-14 (1998). [CrossRef]

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited