## No-reference image quality assessment through the von Mises distribution |

JOSA A, Vol. 29, Issue 10, pp. 2058-2066 (2012)

http://dx.doi.org/10.1364/JOSAA.29.002058

Enhanced HTML Acrobat PDF (771 KB)

### Abstract

An innovative way of calculating the von Mises distribution of image entropy is introduced in this paper. The von Mises distribution’s concentration parameter and some fitness parameter that will be defined later have been analyzed in the experimental part for determining their suitability as an image quality assessment measure in some particular distortions such as Gaussian blur or additive Gaussian noise. To achieve such measure, the local Rényi entropy is calculated in four equally spaced orientations and used to determine the parameters of the von Mises distribution of the image entropy. Considering contextual images, experimental results after applying this model show that the best-in-focus noise-free images are associated with the highest values for the von Mises distribution concentration parameter and the highest approximation of image data to the von Mises distribution model. Our defined von Mises fitness parameter experimentally appears also as a suitable no-reference image quality assessment indicator for no-contextual images.

© 2012 Optical Society of America

**OCIS Codes**

(100.2000) Image processing : Digital image processing

(110.3000) Imaging systems : Image quality assessment

(180.0180) Microscopy : Microscopy

(330.6180) Vision, color, and visual optics : Spectral discrimination

**ToC Category:**

Imaging Systems

**History**

Original Manuscript: February 17, 2012

Revised Manuscript: June 28, 2012

Manuscript Accepted: July 24, 2012

Published: September 6, 2012

**Citation**

Salvador Gabarda and Gabriel Cristóbal, "No-reference image quality assessment through the von Mises distribution," J. Opt. Soc. Am. A **29**, 2058-2066 (2012)

http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-29-10-2058

Sort: Year | Journal | Reset

### References

- N. Ponomarenko, V. Lukin, A. Zelensky, K. Egiazarian, M. Carli, and F. Battisti, “A database for evaluation of full-reference visual quality assessment metrics,” Adv. Mod. Radioelectron. 10, pp. 30–45 (2009).
- S. Yendrikhovskij, “Image quality and colour categorisation,” in W. Lindsay, M. MacDonald, and L. Ronnier, eds., Colour Image Science: Exploiting Digital Media (Wiley, 2002), pp. 393–420.
- G. Ciocca, F. Marini, and R. Schettini, “Image quality assessment in multimedia applications,” Proc. SPIE 7255, 72550A (2009). [CrossRef]
- Z. Wang and A. Bovik, “Modern image quality assessment,” in Synthesis Lectures on Image, Video, and Multimedia Processing (Morgan & Claypool, 2006), Vol. 2, pp. 1–156.
- 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. 18, 717–728 (2009). [CrossRef]
- X. Zhu and P. Milanfar, “Automatic parameter selection for denoising algorithms using a no-reference measure of image content,” IEEE Trans. Image Process. 19, 3116–3132 (2010). [CrossRef]
- N. D. Narvekar and L. J. Karam, “An improved no-reference sharpness metric based on the probability of blur detection,” in Proceedings of International Workshop on Video Processing and Quality Metrics for Consumer Electronics (VPQM), http://www.vpqm.org [pdf] [Software] (2010).
- H. R. Sheikh, A. C. Bovik, L. Cormack, and Z. Wang, “LIVE image quality assessment database,” http://live.ece.utexas.edu/research/quality (2003).
- S. Gabarda and G. Cristóbal, “Blind image quality assessment through anisotropy,” J. Opt. Soc. Am. A 24, B42–51 (2007). [CrossRef]
- http://www.iv.optica.csic.es/page49/styled-4/page62.html.
- C. E. Shannon and W. Weaver, The Mathematical Theory of Communication (University of Illinois, 1949).
- A. Rényi, “Some fundamental questions of information theory,” in Selected Papers of Alfred Rényi (Akadémiai Kiadó, Budapest), Vol. 3, pp. 526–552. (Originally: MTA III. Oszt. Közl., 10, 1960, pp. 251-282) (1976).
- E. Wigner, “On the quantum correction for thermodynamic equilibrium,” Phys. Rev. 40, 749–759 (1932). [CrossRef]
- L. Cohen, “Generalized phase-space distribution functions,” J. Math. Phys. 7, 781–786 (1966). [CrossRef]
- L. D. Jacobson and H. Wechsler, “Joint spatial/spatial-frequency representation,” Signal Process. 14, 37–68 (1988). [CrossRef]
- 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 (1980).
- K. H. Brenner, “A discrete version of the Wigner distribution function,” in Proceedings of EURASIP, Signal Processing II: Theories and Applications (1983), pp. 307–309.
- T. H. Sang and W. J. Williams, “Rényi information and signal dependent optimal kernel design,” in Proceedings of the ICASSP (1995), Vol. 2, pp. 997–1000.
- W. J. Williams, M. L. Brown, and A. O. Hero, “Uncertainty, information and time-frequency distributions,” Adv. Signal Process. 1566, 144–156 (1991).
- P. Flandrin, R. G. Baraniuk, and O. Michel, “Time-frequency complexity and information,” in Proceedings of the ICASSP (1994), Vol. 3, pp. 329–332.
- J. Pitton, P. Loughlin, and L. Atlas, “Positive time-frequency distributions via maximum entropy deconvolution of the evolutionary spectrum,” in Proceedings of ICASSP IV (1993), pp. 436–439.
- L. Stankovic, “A measure of some time-frequency distributions concentration,” Signal Process. 81, 621–631 (2001). [CrossRef]
- K. Conrad, “Probability distributions and maximum entropy,” Entropy 6, 1–10 (2004). [CrossRef]
- K. Zyczkowski, “Rényi extrapolation of Shannon entropy,” Open Syst. Inf. Dyn. 10, 297–310 (2003). [CrossRef]
- R. von Mises “Uber die ’Ganzzahligkeit’ der Atomgewicht und verwandte Fragen,” Physikalische Z. 19, 490–500 (1918).
- M. Abramowitz and I. A. Stegun, Handbook of Mathematical Functions (Dover, 1965).
- A. P. N. Vo and S. Oraintara, “Statistical image modeling using von Mises distribution in the complex directional wavelet domain,” in Proceedings of IEEE International Symposium on Circuits and Systems (IEEE, 2008), pp. 2885–2888..
- A. R. Palacios, C. Rodríguez, and C. Vejarano, “Circular processing of the hue variable a particular trait of colour image processing,” in 2nd International Conference on Computer Vision Theory and Applications (VISAPP 2007) (2007), pp. 69–78.
- X. Feng, “The analysis and approaches to image local orientation estimation,” Master’s thesis (University of California, 2003).
- C. Grana, D. Borghesani, and R. Cucchiara, “Describing texture directions with von Mises distributions,” in Proceedings of ICPR (2008), pp. 1–4.
- M. A. Stephens, “Techniques for directional data,” Technical report no. 150 (Department of Statistics, Stanford University, 1969).
- S. R. Jammalamadaka and A. SenGupta, Topics in Circular Statistics (World Scientific, 2001).
- J. Bentley, “Modelling circular data using a mixture of Von Mises and uniform distributions” (Department of Statistics and Actuarial Science, Simon Fraser University, 2006).
- I. S. Dhillon and S. Sra, “Modeling data using directional distributions,” Technical Report # TR-03-06 (Department of Computer Sciences, The University of Texas at Austin, 2003).
- http://www.mathworks.com/matlabcentral/fileexchange/authors/127745.
- C. R. Stephens and J. Mora Vargas, “Effective fitness as an alternative paradigm for evolutionary computation I: general formalism,” Genet. Program. Evolvable Mach. 1, 363–378(2000).
- A. G. Valdecasas, D. Marshall, J. M. Becerra, and J. J. Terrero, “On the extended depth of focus algorithms for bright field microscopy,” Micron 32, 559–569 (2001). [CrossRef]
- E. Snelson, C. E. Rasmussen, and Z. Ghahramani, “Warped Gaussian processes,” Adv. Neural Inform. Process. Syst. 16, 337–344 (2004).
- J. Redi, R. Zunino, H. Liu, H. Alers, and I. Heynderickx, “Comparing subjective image quality measurement methods for the creation of public databases,” Proc. SPIE 7529, 752903 (2010). [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.