Towards the new concept of linear image quality assessment. Review of phase correlation formulas

• Autorzy: Skurowski, P.

Warianty tytułu

PL

Nowa koncepcja liniowej metody oceny jakości obrazów. Analiza formuł korelacji faz

• Języki publikacji: EN

Abstrakty

EN

The article reports the progress in a development of an image quality assessment (IQA) method based on a new concept – phase correlation. The primary idea stems from the classical observation that structural information of the media/image information is stored within the phase part of the Fourier spectra. The paper describes the review and selection process of a correlation formula for future full reference IQA method. The results were verified with reference database against the human visual quality responses given in mean opinion score (MOS) scale, whereas the key goal was to keep the linearity of the results as it was observed in preliminary results. There were tested correlation models linear and circular raw and weighted in using various weighting schemes.

PL

Artykuł opisuje prace związane z rozwojem metody oceny jakości obrazów, bazującej na nowej koncepcji – korelacji faz. Podstawowy pomysł wynika z klasycznej obserwacji, że informacja strukturalna w mediach/obrazach reprezentowana jest przez część fazową widma Fourierowskiego. Artykuł opisuje przegląd metod pomiaru korelacji i ich ocenę w celu dobrania formuły korelacyjnej dla przyszłej metody oceny obrazów. Wyniki zostały zweryfikowane względem ludzkich ocen, podanych w skalach MOS, zawartych w referencyjnych bazach danych, gdzie jako główny cel badawczy postawiono poszukiwanie liniowej zależności, tak jak zaobserwowano to we wstępnych wynikach. Przetestowano modele korelacji liniowej i cyklicznej w postaci prostej i ważonej z wykorzystaniem różnych schematów doboru wag.

Słowa kluczowe

PL

jakość obrazów; korelacja faz; korelacja cykliczna

EN

image quality; phase correlation; circular correlation

• Czasopismo: Studia Informatica
• Rocznik: 2014
• Tom: Vol. 35, nr 4
• Strony: 111--131
• Opis fizyczny: Bibliogr. 25 poz.

Twórcy

autor

Skurowski, P.

• Silesian University of Technology, Institute of Informatics,

Bibliografia

• 1. Wang, Z., Bovik, A.C., Lu, L.: Why is image quality assessment so difficult? [in:] 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2002, vol. 4, p. IV–3313÷IV–3316.
• 2. Wang, Z., Bovik, A.C.: Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures. IEEE Signal Process. Mag., vol. 26, no. 1, 2009, p. 98÷117.
• 3. Oppenheim, A.V., Lim, J.S.: The importance of phase in signals. Proc. IEEE, vol. 69, no. 5, 1981, p. 529÷541.
• 4. Campbell, F.W.: The human eye as an optical filter. Proc. IEEE, vol. 56, no. 6, 1968, p. 1009÷1014.
• 5. Mantiuk, R., Daly, S.J., Myszkowski, K., Seidel, H.-P.: Predicting visible differences in high dynamic range images: model and its calibration. [in:] Proc. SPIE 5666, Human Vision and Electronic Imaging X, 2005, vol. 5666, p. 204÷214.
• 6. Zhang, X., Wandell, B.A.: A spatial extension of CIELAB for digital color-image reproduction. J. Soc. Inf. Disp., vol. 5, no. 1, 1997, p. 61.
• 7. Wang, Z., Simoncelli, E.P.: Reduced-reference image quality assessment using a wavelet-domain natural image statistic model. [in:] Proc. SPIE 5666, Human Vision and Electronic Imaging X, 2005, vol. 5666, p. 149÷159.
• 8. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process., vol. 13, no. 4, 2004, p. 600÷612.
• 9. Liu, Z., Laganiere, R.: On the Use of Phase Congruency to Evaluate Image Similarity. [in:] 2006 IEEE International Conference on Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings, 2006, vol. 2, p. II 937 ÷ II 940.
• 10. Shao, X., Zhong, C.: Phase congruency assesses hyperspectral image quality. [in:] Proc. SPIE 8514, Satellite Data Compression, Communications, and Processing VIII, 2012, vol. 8514, p. 851408÷851408÷11.
• 11. Zhang, L., Zhang, L., Mou, X., Zhang, D.: FSIM: A Feature Similarity Index for Image Quality Assessment. IEEE Trans. Image Process., vol. 20, no. 8, Aug. 2011, p. 2378÷2386.
• 12. Narwaria, M., Lin, W., McLoughlin, I.V., et al.: Fourier Transform-Based Scalable Image Quality Measure. IEEE Trans. Image Process., vol. 21, no. 8, Aug. 2012, p. 3364÷3377.
• 13. Skurowski, P., Gruca, A.: Image Quality Assessment Using Phase Spectrum Correlation, [in:] Proceedings of the International Conference on Computer Vision and Graphics: Revised Papers, Lecture Notes in Computer Science, vol. 5337, Springer-Verlag, Berlin, Heidelberg 2009, p. 80÷89.
• 14. Bracewell, R.N.: The Fourier transform and its applications. 3rd ed., McGraw Hill, Boston 2000.
• 15. Rice, J.A.: Mathematical statistics and data analysis. 3rd ed., Thomson/Brooks/Cole, Belmont, CA, 2007.
• 16. Carrasco, J.L., Jover, L.: Estimating the Generalized Concordance Correlation Coefficient through Variance Components. Biometrics, vol. 59, no. 4, Dec. 2003, p. 849÷858.
• 17. Lin, L.I.-K.: A Concordance Correlation Coefficient to Evaluate Reproducibility. Biometrics, vol. 45, no. 1, Mar. 1989, p. 255.
• 18. Jammalamadaka, S.R.: Topics in circular statistics. World Scientific, River Edge N.J 2001.
• 19. Fisher, N.I., Lee, A.J.: A correlation coefficient for circular data. Biometrika, vol. 70, no. 2, Jan. 1983, p. 327÷332.
• 20. Sheikh, H.R., Wang, Z., Cormack, L., Bovik, A.C.: LIVE Image Quality Assessment Database Release 2. [Online]. Available: http://live.ece.utexas.edu/research/quality.
• 21. Ponomarenko, N., Lukin, V., Zelensky, A., et al.: TID2008 – A Database for Evaluation of Full Reference Visual Quality Assessment Metrics. Adv. Mod. Radioelectron., vol. 10, no. 4, 2009, p. 30÷45.
• 22. Winkler, S.: Analysis of Public Image and Video Databases for Quality Assessment. IEEE J. Sel. Top. Signal Process., vol. 6, no. 6, 2012, p. 616÷625.
• 23. Gaubatz, M.: MeTriX MuX Visual Quality Assessment Package. [Online]. Available: http://foulard.ece.cornell.edu/gaubatz/metrix_mux/.
• 24. Ponomarenko, N., Battisti, F., Egiazarian, K., et al.: Metrics performance comparison for color image database. [in:] 4th International Workshop on Video Processing and Quality Metrics for Consumer Electronics, Scottsdale, 2009, p. 14÷16.
• 25. Wang, Z., Bovik, A.C.: A universal image quality index. IEEE Signal Process. Lett., vol. 9, no. 3, 2002, p. 81÷84.