Analyzing the Quality of Distorted Images by the Normalized Mutual Information Measure

Authors

  • Mariam E. Haroutunian Institute for Informatics and Automation Problems of NAS RA
  • David G. Asatryan Institute for Informatics and Automation Problems of NAS RA; Russian-Armenian University
  • Karen A. Mastoyan Institute for Informatics and Automation Problems of NAS RA

DOI:

https://doi.org/10.51408/1963-0111

Keywords:

Image quality, Distortion types, Evaluation metrics, Normalized mutual information

Abstract

This research explores how different types of distorting algorithms impact the Full-Reference image quality assessment, particularly when subjective quality evaluations are incorporated. We draw upon the TID2013 database, which contains 3000 images distorted by 24 distinct algorithms, in conjunction with Mean Opinion Scores (MOS) for quality ratings. We compare the results of Normalized Mutual Information (NMI) for image quality score with W2, based on Weibull distribution, the common PSNR similarity measure and MOS. We advocate for integrating of NMI into the repertoire of image quality assessment metrics.

References

A. George and S. J. Livingston, “A survey on full reference image quality assessment algorithms”, International Journal of Research in Engineering and Technology, vol.2, no.12, pp. 303-307, 2013.

Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity”, IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600-612, 2004.

T.-J. Liu, Y.-C. Lin, W. Lin and C.-C.J. Kuo, “Visual Quality Assessment: Recent Developments, Coding Applications and Future Trends”, APSIPA Trans. on Signal and Information Processing, vol. 2, 2013. DOI: https://doi.org/10.1017/ATSIP.2013.5

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

J. Wang, Z. Wang, L. Lu and A. C. Bovik, “Subjective Quality Assessment of Stereoscopic Omnidirectional Image”, Lecture Notes in Computer Science, in book Advances in Multimedia Information Processing PCM, pp. 589-599, 2018.

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 Journals of Scientific Technical Union of Mechanical Engineering ”Industry 4.0”, Mathematical Modeling, 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, Battisti F., et al. “Image database tid2013”, Signal Processing: Image Communication, vol. 30, pp. 57-77, 2015.

A. Kraskov, H. Stogbauer, R. G. Andrzejak and P. Grassberger, “Estimating mutual information”, Physical Review, E 69, 066138, 2004.

T. M. Cover and J. A Thomas, Elements of Information Theory, Second Edition, Wiley-Interscience, 2006.

F. E. Ruiz, P. S. P´erez and B. I. Bonev, “Information theory in computer vision and pattern recognition”, Springer Science and Business Media, 2009.

N. X. Vinh, J. Epps and J. Bailey, “Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance”, Journal of Machine Learning Research, vol. 11, pp. 2837-2854, 2010.

J. P. W. Pluim, J. B. A. Maintz and M. A. Viergever, “Mutual-information-based registration of medical images: A survey”, IEEE Transactions on Medical Imaging, vol. 22, no. 8, pp. 986-1004, Aug. 2003.

R. C. Gonzales and R. E. Woods, Digital Image Processing, 3rd edition, Pearson Education, 2008.

Z. Wang, E. P. Simoncelli and A. C. Bovik, “Multiscale structural similarity for image quality assessment”, In The Thrity-Seventh Asilomar Conference on Signals, Systems and Computers, vol. 2, pp. 1398-1402, 2003.

D. Asatryan, “Image blur estimation using gradient field analysis”, Computer Optics, vol. 41, no. 6, pp. 957-962, 2017. DOI: 10.18287/2412-6179-2017-41-6-957-962.

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Published

2024-06-01

How to Cite

Haroutunian, M. E., Asatryan, D. G., & Mastoyan, K. A. (2024). Analyzing the Quality of Distorted Images by the Normalized Mutual Information Measure. Mathematical Problems of Computer Science, 61, 7–14. https://doi.org/10.51408/1963-0111

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