Evaluation of Image Quality Metrics for Color Prints

  • Marius Pedersen
  • Yuanlin Zheng
  • Jon Yngve Hardeberg
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6688)

Abstract

New technology is continuously proposed in the printing technology, and as a result the need to perform quality assessment is increasing. Subjective assessment of quality is tiresome and expensive, the use of objective methods have therefore become more and more popular. One type of objective assessment that has been subject for extensive research is image quality metrics. However, so far no one has been able to propose a metric fully correlated with the percept. Pedersen et al. (J Elec Imag 19(1):011016, 2010) proposed a set of quality attributes with the intention of being used with image quality metrics. These quality attributes are the starting point for this work, where we evaluate image quality metrics for them, with the goal of proposing suitable metrics for each quality attribute. Experimental results show that suitable metrics are found for the sharpness, lightness, artifacts, and contrast attributes, while none of the evaluated metrics correlate with the percept for the color attribute.

Keywords

Image quality metrics print quality quality attributes color printing 

References

  1. 1.
    Ajagamelle, S.A., Pedersen, M., Simone, G.: Analysis of the difference of gaussians model in image difference metrics. In: 5th European Conference on Colour in Graphics, Imaging, and Vision (CGIV), pp. 489–496. IS&T, Joensuu (2010)Google Scholar
  2. 2.
    Baranczuk, Z., Zolliker, P., Giesen, J.: Image quality measures for evaluating gamut mapping. In: Color Imaging Conference, pp. 21–26. IS&T/SID, Albuquerque (2009)Google Scholar
  3. 3.
    Cao, G., Pedersen, M., Baranczuk, Z.: Saliency models as gamut-mapping artifact detectors. In: 5th European Conference on Colour in Graphics, Imaging, and Vision (CGIV), pp. 437–443. IS&T, Joensuu (2010)Google Scholar
  4. 4.
    Chandler, D., Hemami, S.: VSNR: A wavelet-based visual signal-to-noise ratio for natural images. IEEE Transactions on Image Processing 16(9), 2284–2298 (2007)MathSciNetCrossRefGoogle Scholar
  5. 5.
    CIE: Guidelines for the evaluation of gamut mapping algorithms. Tech. Rep., CIE TC8-03 (156:2004) ISBN: 3-901-906-26-6Google Scholar
  6. 6.
    CIE: Chromatic adaptation under mixed illumination condition when comparing softcopy and hardcopy images. Tech. Rep., CIE TC8-04 (162:2004) ISBN: 3-901-906-34-7Google Scholar
  7. 7.
    Crete, F., Dolmiere, T., Ladret, P., Nicolas, M.: The blur effect: perception and estimation with a new no-reference perceptual blur metric. In: Rogowitz, B.E., Pappas, T.N., Daly, S.J. (eds.) Proceedings of SPIE Human Vision and Electronic Imaging XII, vol. 6492, p. 64920I (March 2007)Google Scholar
  8. 8.
    Fedorovskaya, E.A., Blommaert, F., de Ridder, H.: Perceptual quality of color images of natural scenes transformed in CIELUV color space. In: Color Imaging Conference, pp. 37–40. IS&T/SID (1993)Google Scholar
  9. 9.
    Field, G.G.: Test image design guidelines for color quality evaluation. In: Color Imaging Conference, pp. 194–196. IS&T, Scottsdale (1999)Google Scholar
  10. 10.
    Green, P., MacDonald, L. (eds.): Colour Engineering: Achieving Device Independent Colour. John Wiley & Sons, Chichester (2002)Google Scholar
  11. 11.
    Hardeberg, J., Bando, E., Pedersen, M.: Evaluating colour image difference metrics for gamut-mapped images. Coloration Technology 124(4), 243–253 (2008)CrossRefGoogle Scholar
  12. 12.
    ISO: ISO 12640-2: Graphic technology - prepress digital data exchange - part 2: XYZ/sRGB encoded standard colour image data (XYZ/SCID) (2004)Google Scholar
  13. 13.
    ISO: ISO 12640-3 graphic technology - prepress digital data exchange - part 3: CIELAB standard colour image data (CIELAB/SCID) (2007)Google Scholar
  14. 14.
    Keelan, B.W.: Handbook of Image Quality: Characterization and Prediction. Marcel Dekker, New York (2002)CrossRefGoogle Scholar
  15. 15.
    Kendall, M.G., Stuart, A., Ord, J.K.: Kendall’s Advanced Theory of Statistics: Classical inference and relationship, 5th edn., vol. 2. A Hodder Arnold Publication (1991)Google Scholar
  16. 16.
    Kolpatzik, B., Bouman, C.: Optimized error diffusion for high-quality image display. Journal of Electronic Imaging 1(3), 277–292 (1992)CrossRefGoogle Scholar
  17. 17.
    Kolpatzik, B., Bouman, C.: Optimal universal color palette design for error diffusion. Journal of Electronic Imaging 4(2), 131–143 (1995)CrossRefGoogle Scholar
  18. 18.
    Larson, E.C., Chandler, D.M.: Unveiling relationships between regions of interest and image fidelity metrics. In: Pearlman, W.A., Woods, J.W., Lu, L. (eds.) Visual Communications and Image Processing, SPIE Proceedings, vol. 6822, pp. 68222A–68222A–16. SPIE, San Jose (January 2008)Google Scholar
  19. 19.
    Lindberg, S.: Perceptual determinants of print quality. Ph.D. thesis, Stockholm University (2004)Google Scholar
  20. 20.
    Norberg, O., Westin, P., Lindberg, S., Klaman, M., Eidenvall, L.: A comparison of print quality between digital, offset and flexographic printing presses performed on different paper qualities. In: International Conference on Digital Production Printing and Industrial Applications, pp. 380–385. IS&Ts (May 2001)Google Scholar
  21. 21.
    Orfanidou, M., Triantaphillidou, S., Allen, E.: Predicting image quality using a modular image difference model. In: Farnand, S.P., Gaykema, F. (eds.) Image Quality and System Performance V. SPIE Proceedings, vol. 6808, p. 12. SPIE/IS&T, San Jose, USA (January 2008)CrossRefGoogle Scholar
  22. 22.
    Pedersen, M., Amirshahi, S.: A modified framework the evaluation of color prints using image quality metrics. In: 5th European Conference on Colour in Graphics, Imaging, and Vision (CGIV), pp. 75–82. IS&T, Joensuu (2010)Google Scholar
  23. 23.
    Pedersen, M., Bonnier, N., Hardeberg, J.Y., Albregtsen, F.: Attributes of a new image quality model for color prints. In: Color Imaging Conference, pp. 204–209. IS&T, Albuquerque (2009)Google Scholar
  24. 24.
    Pedersen, M., Bonnier, N., Hardeberg, J.Y., Albregtsen, F.: Attributes of image quality for color prints. Journal of Electronic Imaging 19(1), 011016–1–13 (2010)Google Scholar
  25. 25.
    Pedersen, M., Bonnier, N., Hardeberg, J.Y., Albregtsen, F.: Estimating print quality attributes by image quality metrics. In: Color and Imaging Conference, pp. 68–73. IS&T/SID, San Antonio (2010)Google Scholar
  26. 26.
    Pedersen, M., Bonnier, N., Hardeberg, J.Y., Albregtsen, F.: Validation of quality attributes for evaluation of color prints. In: Color and Imaging Conference, pp. 74–79. IS&T/SID, San Antonio (2010)Google Scholar
  27. 27.
    Pedersen, M., Bonnier, N., Hardeberg, J.Y., Albregtsen, F.: Image quality metrics for the evaluation of print quality. In: Gaykema, F., Farnand, S. (eds.) Image Qualtiy and System Performance. Proceedings of SPIE. SPIE, San Francisco (2011)Google Scholar
  28. 28.
    Pedersen, M., Hardeberg, J.Y.: Rank order and image difference metrics. In: 4th European Conference on Colour in Graphics, Imaging, and Vision (CGIV), pp. 120–125. IS&T, Terrassa (2008)Google Scholar
  29. 29.
    Pedersen, M., Hardeberg, J.Y.: A new spatial hue angle metric for perceptual image difference. In: Trémeau, A., Schettini, R., Tominaga, S. (eds.) CCIW 2009. LNCS, vol. 5646, pp. 81–90. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  30. 30.
    Pedersen, M., Hardeberg, J.Y., Nussbaum, P.: Using gaze information to improve image difference metrics. In: Rogowitz, B., Pappas, T. (eds.) Human Vision and Electronic Imaging VIII, SPIE Proceedings, San Jose, CA, USA, vol. 6806, p. 680611 (January 2008)Google Scholar
  31. 31.
    Pedersen, M., Hardeberg, J.: Survey of full-reference image quality metrics. Høgskolen i Gjøviks rapportserie 5, The Norwegian Color Research Laboratory (Gjøvik University College) (June 2009) ISSN: 1890-520X Google Scholar
  32. 32.
    Ponomarenko, N., Silvestri, F., Egiazarian, K., Carli, M., Astola, J., Lukin, V.: On between-coefficient contrast masking of DCT basis functions. In: Third International Workshop on Video Processing and Quality Metrics for Consumer Electronics VPQM 2007, Scottsdale, Arizona, USA, pp. 1–4 (January 2007)Google Scholar
  33. 33.
    Sheikh, H.R., Bovik, A.C.: Image information and visual quality. IEEE Transactions on Image Processing 15(2), 430–444 (2006)CrossRefGoogle Scholar
  34. 34.
    Shnayderman, A., Gusev, A., Eskicioglu, A.M.: An SVD-based grayscale image quality measure for local and global assessment. IEEE Transactions On Image Processing 15(2), 422–429 (2006)CrossRefGoogle Scholar
  35. 35.
    Simone, G., Oleari, C., Farup, I.: Performance of the euclidean color-difference formula in log-compressed OSA-UCS space applied to modified-image-difference metrics. In: 11th Congress of the International Colour Association (AIC), Sydney, Australia (October 2009)Google Scholar
  36. 36.
    Simone, G., Pedersen, M., Hardeberg, J.Y., Rizzi, A.: Measuring perceptual contrast in a multilevel framework. In: Rogowitz, B.E., Pappas, T.N. (eds.) Human Vision and Electronic Imaging XIV, vol. 7240. SPIE, San Jose (2009)CrossRefGoogle Scholar
  37. 37.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing 13(4), 600–612 (2004)CrossRefGoogle Scholar
  38. 38.
    Wang, Z., Hardeberg, J.Y.: An adaptive bilateral filter for predicting color image difference. In: Color Imaging Conference, pp. 27–31. IS&T/SID, Albuquerque, NM, USA (2009)Google Scholar
  39. 39.
    Wang, Z., Li, Q.: Information content weighting for perceptual image quality assessment. IEEE Transactions on Image Processing (2010)Google Scholar
  40. 40.
    Wang, Z., Simoncelli, E.: Translation insensitive image similarity in complex wavelet domain. In: IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 2, pp. 573–576 (2005)Google Scholar
  41. 41.
    Wang, Z., Simoncelli, E.P.: Reduced-reference image quality assessment using a wavelet-domain natural image statistic model. In: Human Vision and Electronic Imaging X. Proceedings of SPIE, vol. 5666, pp. 149–159. SPIE, San Jose (January 2005)CrossRefGoogle Scholar
  42. 42.
    Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multi-scale structural similarity for image quality assessment. In: Proceedings of the 37th IEEE Asilomar Conference on Signals, Systems and Computers, pp. 1398–1402 (November 2003)Google Scholar
  43. 43.
    Wang, Z., Bovik, A.C., Lu, L.: Wavelet-based foveated image quality measurement for region of interest image coding. In: International Conference on Image Processing, pp. 89–92. IEEE, Los Alamitos (2001)Google Scholar
  44. 44.
    Zhang, L., Zhang, L., Mou, X.: RFSIM: A feature based image quality assessment metric using riesz transforms. In: Internatonal Conference on Image Processing, Hong Kong, pp. 321–324 (September 2010)Google Scholar
  45. 45.
    Zhang, X., Farrell, J., Wandell, B.: Application of a spatial extension to CIELAB. In: Proceedings of SPIE Very high Resolution and Quality Imaging II, San Jose, CA, USA, vol. 3025, pp. 154–157 (February 1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Marius Pedersen
    • 1
    • 2
  • Yuanlin Zheng
    • 1
    • 3
  • Jon Yngve Hardeberg
    • 1
  1. 1.Gjøvik University CollegeGjøvikNorway
  2. 2.Océ Print Logic Technologies S.A.CreteilFrance
  3. 3.Xi’an University of TechnologyXi’anChina

Personalised recommendations