A Complexity-Based Image Analysis to Investigate Interference Between Distortions and Image Contents in Image Quality Assessment

  • Gianluigi Ciocca
  • Silvia Corchs
  • Francesca Gasparini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10213)


In this paper we investigate how distortion and image content interfere within image quality assessment. To this end we analyze how full reference metrics behave within three different groups of images. Given a dataset of images, these are first classified as high, medium or low complexity and the FR methods are applied within each group separately. We consider images from LIVE, CSIQ and LIVE multi-distorted databases. We evaluate 17 full reference quality metrics available in the literature on each of these the high, medium and low complexity groups. We observe that within these groups the metrics better correlate subjective data. In particular, the signal based metrics are the ones that show the highest improvements. Moreover for the LIVE multi-distorted database the gain in performance is evident for all the metrics considered.


Image complexity Image Quality Assessment Full Reference metrics 

Supplementary material


  1. 1.
    Chandler, D.M., Alam, M.M., Phan, T.D.: Seven challenges for image quality research. In: Proceedings of IS&T/SPIE Electronic Imaging, p. 901402. International Society for Optics and Photonics (2014)Google Scholar
  2. 2.
    Larson, E.C., Chandler, D.M.: Most apparent distortion: full-reference image quality assessment and the role of strategy. J. Electron. Imaging 19(1), 011006 (2010)CrossRefGoogle Scholar
  3. 3.
    Moorthy, A.K., Bovik, A.C.: Visual quality assessment algorithms: what does the future hold? Multimedia Tools Appl. 51(2), 675–696 (2011)CrossRefGoogle Scholar
  4. 4.
    Triantaphillidou, S., Allen, E., Jacobson, R.E.: Image quality of JPEG vs JPEG2000: part 2: scene dependency, scene analysis and classification. J. Imaging Sci. Technol. 51, 259–270 (2007)CrossRefGoogle Scholar
  5. 5.
    Oh, K.H., Triantaphillidou, S., Jacobson, R.E.: Scene classification with respect to image quality measurements. In: Proceedings of IS&T/SPIE Electronic Imaging, p. 752908. International Society for Optics and Photonics (2010)Google Scholar
  6. 6.
    Bondzulic, B., Pavlovic, B., Petrovic, V., Andric, M.: Performance of peak signal-to-noise ratio quality assessment in video streaming with packet losses. Electron. Lett. 52(6), 454–456 (2016)CrossRefGoogle Scholar
  7. 7.
    Bianco, S., Ciocca, G., Marini, F., Schettini, R.: Image quality assessment by preprocessing and full reference model combination. In: Proceedings of IS&T/SPIE Electronic Imaging, p. 72420O. International Society for Optics and Photonics (2009)Google Scholar
  8. 8.
    Liu, H., Engelke, U., Wang, J., Le Callet, P., Heynderickx, I.: How does image content affect the added value of visual attention in objective image quality assessment? IEEE Signal Process. Lett. 20, 355–358 (2013)CrossRefGoogle Scholar
  9. 9.
    Corchs, S., Gasparini, F., Schettini, R.: Grouping strategies to improve the correlation between subjective and objective image quality data. In: Proceddings of IS&T/SPIE Electronic Imaging, p. 86530D. International Society for Optics and Photonics (2013)Google Scholar
  10. 10.
    Jayaraman, D., Mittal, A., Moorthy, A.M., Bovik, A.: Objective quality assessment of multiply distorted images. In: Proceedings of the Asilomar Conference on Signals, Systems and Computers (2012)Google Scholar
  11. 11.
    Sheik, H., Wang, Z., Cormakc, L., Bovik, A.: LIVE image quality assessment database release 2.
  12. 12.
    Larson, E., Chandler, D.: CSIQ: categorical subjective image quality CSIQ database (2009).
  13. 13.
    Birkhoff, G.D.: Collected Mathematical Papers. Dover, New York (1950)zbMATHGoogle Scholar
  14. 14.
    Oliva, A., Mack, M.L., Shrestha, M.: Identifying the perceptual dimensions of visual complexity of scenes. In: Proceedings of the 26th Annual Meeting of the Cognitive Science Society (2004)Google Scholar
  15. 15.
    Mario, I., Chacon, M., Alma, D., Corral, S.: Image complexity measure: a human criterion free approach. In: Annual Meeting of the North American Fuzzy Information Processing Society, 2005. NAFIPS 2005, pp. 241–246. IEEE (2005)Google Scholar
  16. 16.
    Rigau, J., Feixas, M., Sbert, M.: An information-theoretic framework for image complexity. In: Proceedings of the First Eurographics Conference on Computational Aesthetics in Graphics, Visualization and Imaging, pp. 177–184. Eurographics Association (2005)Google Scholar
  17. 17.
    Perkiö, J., Hyvärinen, A.: Modelling image complexity by independent component analysis, with application to content-based image retrieval. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds.) ICANN 2009. LNCS, vol. 5769, pp. 704–714. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-04277-5_71 CrossRefGoogle Scholar
  18. 18.
    Rosenholtz, R., Li, Y., Nakano, L.: Measuring visual clutter. J. Vis. 7(2), 17 (2007)CrossRefGoogle Scholar
  19. 19.
    Mack, M., Oliva, A.: Computational estimation of visual complexity. In: The 12th Annual Object, Perception, Attention, and Memory Conference (2004)Google Scholar
  20. 20.
    Corchs, S.E., Ciocca, G., Bricolo, E., Gasparini, F.: Predicting complexity perception of real world images. PloS One 11(6), e0157986 (2016)CrossRefGoogle Scholar
  21. 21.
    Ciocca, G., Corchs, S., Gasparini, F.: Genetic programming approach to evaluate complexity of texture images. J. Electron. Imaging 25(6), 061408 (2016)CrossRefGoogle Scholar
  22. 22.
    Allen, E., Triantaphillidou, S., Jacobson, R.: Image quality comparison between JPEG and JPEG2000. I: psychophysical investigation. J. Imaging Sci. Technol. 51, 248–258 (2007)CrossRefGoogle Scholar
  23. 23.
    Chacón, M., Aguilar, L.: A fuzzy approach to edge level detection. In: The 10th IEEE International Conference on Fuzzy Systems, 2001, vol. 2, pp. 809–812. IEEE (2001)Google Scholar
  24. 24.
    Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers, London (1981)CrossRefzbMATHGoogle Scholar
  25. 25.
    Balasko, B., Abonyi, J., Feil, B.: Fuzzy clustering and data analysis toolbox. Department of Process Engineering, University of Veszprem, Veszprem (2005)Google Scholar
  26. 26.
    Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Procedings of the 8th International Conference on Computer Vision, vol. 2, pp. 416–423, July 2001Google Scholar
  27. 27.
    Bezdek, J.C., Dunn, J.C.: Optimal fuzzy partitions: a heuristic for estimating the parameters in a mixture of normal distributions. IEEE Trans. Comput. 100(8), 835–838 (1975)CrossRefzbMATHGoogle Scholar
  28. 28.
    Winkler, S.: Analysis of public image and video databases for quality assessment. IEEE J. Sel. Top. Sig. Proces. 6, 616–625 (2012)CrossRefGoogle Scholar
  29. 29.
    Sheikh, H., Sabir, M., Bovik, A.: A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Trans. Image Process. 15, 3440–3451 (2006)CrossRefGoogle Scholar
  30. 30.
    Wang, Z., Bovik, A.: A universal image quality index. IEEE Signal Process. Lett. 9, 81–84 (2002)CrossRefGoogle Scholar
  31. 31.
    Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600–612 (2004)CrossRefGoogle Scholar
  32. 32.
    Wang, Z., Simoncelli, E., Bovik, A.: Multi-scale structural similarity for image quality assessment. In: 37th IEEE Asilomar Conference on Signals, Systems and Computers (2003)Google Scholar
  33. 33.
    Chandler, D., Hemami, S.: VSNR: a wavelet-based visual signal-to-noise ratio for natural images. IEEE Trans. Image Process. 16, 2284–2298 (2007)MathSciNetCrossRefGoogle Scholar
  34. 34.
    Sheikh, H., Bovik, A., de Veciana, G.: An information fidelity criterion for image quality assessment using natural scene statistics. IEEE Trans. Image Process. 14, 2117–2128 (2005)CrossRefGoogle Scholar
  35. 35.
    Sheikh, H., Bovik, A.: Image information and visual quality. IEEE Trans. Image Process. 15, 430–444 (2006)CrossRefGoogle Scholar
  36. 36.
    Egiazarian, K., Astola, J., Ponomarenko, N., Lukin, V., Battisti, F., Carli, M.: New full-reference quality metrics based on hvs. In: CD-ROM proceedings of the Second International Workshop on Video Processing and Quality Metrics, Scottsdale, USA (2006)Google Scholar
  37. 37.
    Ponomarenko, N., Silvestri, F., Egiazarian, K., Carli, M., Astola, J., Lukin, V.: On between-coefficient contrast masking of dct basis functions. In: Proceedings of the Third International Workshop on Video Processing and Quality Metrics, vol. 4 (2007)Google Scholar
  38. 38.
    Wang, Z., Li, Q.: Information content weighting for perceptual image quality assessment. IEEE Trans. Image Process. 20(5), 1185–1198 (2011)MathSciNetCrossRefGoogle Scholar
  39. 39.
    Zhang, L., Zhang, D., Mou, X., Zhang, D.: FSIM: a feature similarity index for image quality assessment. IEEE Trans. Image Process. 20(8), 2378–2386 (2011)MathSciNetCrossRefGoogle Scholar
  40. 40.
    Xue, W., Zhang, L., Mou, X., Bovik, A.: Gradient magnitude similarity deviation: a highly efficient perceptual image quality index. IEEE Trans. Image Process. 23(2), 684–695 (2014)MathSciNetCrossRefGoogle Scholar
  41. 41.
    Laparra, V., Muñoz-Marí, J., Malo, J.: Divisive normalization image quality metric revisited. JOSA A 27(4), 852–864 (2010)CrossRefGoogle Scholar
  42. 42.
    Jayaraman, D., Mittal, A., Moorthy, A.K., Bovik, A.C.: Objective image quality assessment of multiply distorted images. In: Proceedings of Asilomar Conference on Signals, Systems and Computers (2012)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Gianluigi Ciocca
    • 1
    • 2
  • Silvia Corchs
    • 1
    • 2
  • Francesca Gasparini
    • 1
    • 2
  1. 1.Dipartimento di Informatica, Sistemistica e ComunicazioneUniversity of Milano-BicoccaMilanItaly
  2. 2.NeuroMi - Milan Center for NeuroscienceMilanItaly

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