Full-Reference Image Quality Assessment Procedure Based on Rice Distribution Model

Authors

  • David G. Asatryan Institute for Informatics and Automation Problems of NAS RA; Russian-Armenian University
  • Liana K. Andreasyan National Polytechnic University of Armenia
  • Grigor S. Sazhumyan Russian-Armenian University

Keywords:

Gradient magnitude, Weibull distribution, Rice distribution, Parameter estimation, Image similarity, MOS

Abstract

The problem of full-reference image quality assessment is considered based on the application of the mathematical model of the Rice distribution. The gradient field of an image is adequately described by the Weibull distribution, which allows one to effectively analyze image properties, evaluate their similarity, classify them by quality, etc. In this paper, an attempt is made to solve similar problems using the above-mentioned model, relying, in particular, on additional properties of the Rice distribution associated with the normal approximation of the latter. It is shown that the structural similarity measure used in different problems is also applicable to the case of the Rice gradient field model. In particular, images from the TID2013 database are experimentally studied. The modeling results obtained from both the Weibull and Rice distribution models were compared using the mean square and structural similarity measures, as well as the Mean Opinion Score (MOS) values. It is shown that the types of distortions in these indicators are in complete agreement, while for some other types, the Rice distribution model shows better results.

References

G. Zhai and X. Min, “Perceptual image quality assessment: a survey”, Sci. China Inf. Sci., vol. 63, no.11, 211301, 2020. doi:10.1007/s11432-019-2757-1

D. Asatryan and K. Egiazarian, “Quality assessment measure based on image structural properties”, 2009 International Workshop on Local and Non-Local Approximation in Image Processing, Tuusula, Finland, pp. 70-73, 2009. doi:10.1109/lnla.2009.5278400

D. Asatryan, “Gradient-based technique for image structural analysis and applications”, Computer Optics, vol.43, no. 2,pp.245-250, 2019. doi: 10.18287/2412-6179-2019-43-2-245-250.

D. Asatryan, M. Haroutunian, G. Sazhumyan and G. Hakobyan, “Procedure for analyzing the quality, structure and subjective rating of distorted images by the Full- Reference technique”, Intern. Scientific Journal Mathematical Modeling, 2022, vol. 6, no.4, pp. 100-102, 2022.

N. Ponomarenko, L. Jin, O. Ieremeiev, V. Lukin, K. Egiazarian, J. Astola, B. Vozel, K. Chehdi, M. Carli, F. Battisti and C.-C. Jay Kuo, “Image database TID2013: Peculiarities, results and perspectives”, Signal Processing: Image Communication, vol. 30, pp. 57-77, 2015.

T.Yakovleva, “Peculiarities of the rice statistical distribution: mathematical substantiation”, Applied and Computational Mathematics, Science Publishing Group, vol.7, no. 4, pp. 188-196, 2018. doi: 10.11648/j.acm.20180704.12.

Б. Левин, “Теоретические основы статистической радиотехники”, М.: Радио и связь, 656 с., 1989.

Downloads

Published

2025-06-01

How to Cite

Asatryan, D. G., Andreasyan, L. K., & Sazhumyan, G. S. (2025). Full-Reference Image Quality Assessment Procedure Based on Rice Distribution Model. Mathematical Problems of Computer Science, 63, 7–13. Retrieved from https://mpcs.sci.am/index.php/mpcs/article/view/879

Most read articles by the same author(s)