Automated qualitative assessment of multi-modal distortions in digital images based on GLZ

  • Andrzej Głowacz
  • Michał Grega
  • Przemysław Gwiazda
  • Lucjan Janowski
  • Mikołaj Leszczuk
  • Piotr Romaniak
  • Simon Pietro Romano
Article

Abstract

This paper introduces a novel approach to a qualitative assessment of images affected by multi-modal distortions. The idea is to assess the image quality perceived by an end user in an automatic way in order to avoid the usual time-consuming, costly and non-repeatable method of collecting subjective scores during a psycho-physical experiment. This is achieved by computing quantitative image distortions and mapping results on qualitative scores. Useful mapping models have been proposed and constructed using the generalised linear model (GLZ), which is a generalisation of the least squares regression in statistics for ordinal data. Overall qualitative image distortion is computed based on partial quantitative distortions from component algorithms operating on specified image features. Seven such algorithms are applied to successfully analyse the seven image distortions in relation to the original image. A survey of over 12,000 subjective quality scores has been carried out in order to determine the influence of these features on the perceived image quality. The results of quantitative assessments are mapped on the surveyed scores to obtain an overall quality score of the image. The proposed models have been validated in order to prove that the above technique can be applied to automatic image quality assessment.

Keywords

Image quality Image distortion MOS Mean opinion score GLZ Generalised linear models Quality metrics 

Notes

Acknowledgements

The work presented in this paper was supported in part by Telekomunikacja Polska S.A. and the grants funded by EC (CONTENT FP6-0384239, INDECT FP7-218086) and Polish MNiSW (PBZ-MNiSW-02/II/2007 and N 517 4388 33).

References

  1. 1.
    Agresti A (2002) Categorical data analysis, 2nd edn. Wiley, New YorkMATHGoogle Scholar
  2. 2.
    Aguirre-Torres V, Rios-Curil A (1994) The effect and adjustment of complex surveys on chi-squared goodness of fit tests: some Montecarlo evidence. In: Proceedings of the survey research methods section, pp 602–607Google Scholar
  3. 3.
    Bierens HJ (2004) Introduction to the mathematical and statistical foundations of econometrics. Cambridge University Press, CambridgeMATHGoogle Scholar
  4. 4.
    Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8(6):679–698CrossRefGoogle Scholar
  5. 5.
    Farias MCQ, Mitra SK (2005) No-reference video quality metric based on artifact measurements. In: IEEE international conference on image processing, ICIP 2005, vol 3, III - 141–4Google Scholar
  6. 6.
    Hosaka K (1986) A new picture quality evaluation method. In: Proc international picture coding symposium, pp 17–18Google Scholar
  7. 7.
    Imme M (1991) A noise peak elimination filter, CVGIP: graph. Models Image Process 53(2):204–211CrossRefGoogle Scholar
  8. 8.
    ITU-T (1998) Methodology for the subjective assessment of the quality of television pictures. Recommendation ITU-R BT.500-11Google Scholar
  9. 9.
    ITU-T (1996) Methods for subjective determination of transmission quality. Recommendation ITU-T P.800Google Scholar
  10. 10.
    ITU-T (2008) Objective perceptual multimedia video quality measurement in the presence of a full reference. Recommendation ITU-T J.247Google Scholar
  11. 11.
    ITU-T (2004) Objective perceptual video quality measurement techniques for digital cable television in the presence of a full reference. Recommendation ITU-T J.144Google Scholar
  12. 12.
    ITU-T (1998) Standardized digitized image set. Recommendation ITU-T T.24Google Scholar
  13. 13.
    ITU-T (1999) Subjective video quality assessment methods for multimedia applications. Recommendation ITU-T P.910Google Scholar
  14. 14.
    Janowski L, Papir Z (2009) Modeling subjective tests of quality of experience with a generalized linear model. In: Proc QoMEX 2009Google Scholar
  15. 15.
    Miyahara M, Kotani K, Algazi VR (1998) Objective picture quality scale (PQS) for image coding. IEEE Trans Commun 46(9):1215–1226CrossRefGoogle Scholar
  16. 16.
    OPTICOM GmbH (2007) Perceptual evaluation of video quality. http://www.opticom.de/technology/pevq.html

Copyright information

© Institut TELECOM and Springer-Verlag 2009

Authors and Affiliations

  • Andrzej Głowacz
    • 1
  • Michał Grega
    • 1
  • Przemysław Gwiazda
    • 2
  • Lucjan Janowski
    • 1
  • Mikołaj Leszczuk
    • 1
  • Piotr Romaniak
    • 1
  • Simon Pietro Romano
    • 3
  1. 1.Department of TelecommunicationsAGH University of Science and TechnologyKrakowPoland
  2. 2.Telekomunikacja Polska R&DWarsawPoland
  3. 3.Computer Science DepartmentUniversita’ degli Studi di Napoli Federico IINaplesItaly

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