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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 ArticleMATHGoogle 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 ArticleMATHGoogle 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.View ArticleGoogle 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