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  • Research Article
  • Open Access

Image Quality Assessment Using the Joint Spatial/Spatial-Frequency Representation

EURASIP Journal on Advances in Signal Processing20062006:080537

  • Received: 9 December 2004
  • Accepted: 9 March 2006
  • Published:


This paper demonstrates the usefulness of spatial/spatial-frequency representations in image quality assessment by introducing a new image dissimilarity measure based on 2D Wigner-Ville distribution (WVD). The properties of 2D WVD are shortly reviewed, and the important issue of choosing the analytic image is emphasized. The WVD-based measure is shown to be correlated with subjective human evaluation, which is the premise towards an image quality assessor developed on this principle.


  • Analytic Image
  • Information Technology
  • Image Quality
  • Quality Assessment
  • Quantum Information

Authors’ Affiliations

L2TI-Institute Galilée, Université Paris 13, Villetaneuse, 93430, France
GE Healthcare Technologies, Buc, 78530, France


  1. Qian S, Chen D: Joint Time-Frequency Analysis: Methods and Applications. Prentice-Hall, Upper Saddle River, NJ, USA; 1994.Google Scholar
  2. Boashash B (Ed): Time-Frequency Signal Analysis and Processing: A Comprehensive Reference. Elsevier, Oxford, UK; 2003.Google Scholar
  3. Jacobson L, Wechsler H: The Wigner distribution as a tool for deriving an invariant representation of 2-D images. Proceedings of the International Conference on Pattern Recognition and Image Processing, June 1982, Las Vegas, Nev, USA 218–220.Google Scholar
  4. Reed TR, Wechsler H: Segmentation of textured images and Gestalt organization using spatial/spatial-frequency representations. IEEE Transactions on Pattern Analysis and Machine Intelligence 1990, 12(1):1–12. 10.1109/34.41379View ArticleGoogle Scholar
  5. Zhu YM, Goutte R, Amiel M: On the use of two-dimensional Wigner-Ville distribution for texture segmentation. Signal Processing 1993, 30(3):329–353. 10.1016/0165-1684(93)90016-4View ArticleGoogle Scholar
  6. Jacobson L, Wechsler H: Joint spatial/spatial-frequency representation. Signal Processing 1988, 14(1):37–68. 10.1016/0165-1684(88)90043-6View ArticleGoogle Scholar
  7. Cristóbal G, Hormigo J: Texture segmentation through eigen-analysis of the Pseudo-Wigner distribution. Pattern Recognition Letters 1999, 20(3):337–345. 10.1016/S0167-8655(99)00002-1View ArticleGoogle Scholar
  8. Stankovic S, Djurovic I, Pitas I: Watermarking in the space/spatial-frequency domain using two-dimensional Radon-Wigner distribution. IEEE Transactions on Image Processing 2001, 10(4):650–658. 10.1109/83.913599View ArticleGoogle Scholar
  9. Iordache R, Beghdadi A: A Wigner-Ville distribution-based image dissimilarity measure. Proceedings of the 6th International Symposium on Signal Processing and Its Applications (ISSPA '01), August 2001, Kuala Lumpur, Malaysia 2: 430–433.Google Scholar
  10. Gabarda S, Cristóbal G: On the use of a joint spatial-frequency representation for the fusion of multi-focus images. Pattern Recognition Letters 2005, 26(16):2572–2578. 10.1016/j.patrec.2005.06.003View ArticleGoogle Scholar
  11. Special issue on image quality assessment Signal Processing 1998., 70:Google Scholar
  12. García-Pérez MA, Sierra-Vázquez V: Visual processing in the joint spatial/spatial-frequency domain. In Visual Models for Target Detection and Recognition. Edited by: Peli E. World Scientific, Hackensack, NJ, USA; 1995:16–62.View ArticleGoogle Scholar
  13. Eskicioglu AM, Fisher PS: Image quality measures and their performance. IEEE Transactions on Communications 1995, 43(12):2959–2965. 10.1109/26.477498View ArticleGoogle Scholar
  14. ITU-R Recommendation BT.500-7 : Methodology for the Subjective Assessment of the Quality of Television Pictures. ITU, Geneva, Switzerland, 1995Google Scholar
  15. Wang Z, Lu L, Bovik AC: Video quality assessment based on structural distortion measurement. Signal Processing: Image Communication 2004, 19(2):121–132. special issue on "Objective video quality metrics" 10.1016/S0923-5965(03)00076-6Google Scholar
  16. Bülow T, Sommer G: A novel approach to the 2d analytic signal. Proceedings of the 8th International Conference on Computer Analysis of Images and Patterns (CAIP '99), September 1999, Ljubljana, Slovenia 25–32.View ArticleGoogle Scholar
  17. Hahn SL: Multidimensional complex signals with single-orthant spectra. Proceedings of the IEEE 1992, 80: 1287–1300. 10.1109/5.158601View ArticleGoogle Scholar
  18. Boashash B, Black PJ: An efficient real-time implementation of the Wigner-Ville distribution. IEEE Transactions on Acoustics, Speech, and Signal Processing 1987, 35(11):1611–1618. 10.1109/TASSP.1987.1165070View ArticleGoogle Scholar
  19. Homigo J, Cristobal G: High resolution spectral analysis of images using the pseudo-Wigner distribution. IEEE Transactions on Signal Processing 1998, 46(6):1757–1763. 10.1109/78.678519View ArticleGoogle Scholar
  20. Iordache R, Beghdadi A: Single-quadrant analytic images for 2-D discrete Wigner distribution. Proceedings of the 8th International Workshop on Systems, Signals and Image Processing (IWSSIP '01), June 2001, Bucharest, Romania 163–166.Google Scholar
  21. Malik J, Perona P: Preattentive texture discrimination with early vision mechanisms. Journal of the Optical Society of America 1990, 7(5):923–932. 10.1364/JOSAA.7.000923View ArticleGoogle Scholar
  22. Watson AB, Solomon JA: Model of visual contrast gain control and pattern masking. Journal of the Optical Society of America A: Optics and Image Science, and Vision 1997, 14(9):2379–2391. 10.1364/JOSAA.14.002379View ArticleGoogle Scholar
  23. Teo PC, Heeger DJ: Perceptual image distortion. Proceedings of the 1st IEEE International Conference on Image Processing, November 1994, Austin, Tex, USA 2: 982–986.View ArticleGoogle Scholar
  24. Beghdadi A, Pesquet-Popescu B: A new image distortion measure based on wavelet decomposition. Proceedings of the 6th International Symposium on Signal Processing and Its Applications (ISSPA '03), July 2003, Paris, France 1: 485–488.Google Scholar
  25. Sundaram RS, Prabhu KMM: Numerically stable algorithm for computing Wigner-Ville distribution. IEE Proceedings - Vision, Image, and Signal Processing 1997, 144(1):46–48. 10.1049/ip-vis:19970817View ArticleGoogle Scholar


© Beghdadi and Iordache 2006