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Recent and prospective decorrelation techniques for image processing

Techniques de dÉecorrÉlation rÉcentes et futures pour traitement d’image

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Abstract

This paper is devoted to the description of recent and prospective decorrelation techniques used in image processing. Two classes are taken into consideration : the universal or generic methods and the adaptive methods. The first class performs signal processing independently of its specificity. These methods only rely on statistical properties of the signal. They are principally based on orthogonal transforms and subband coding. In opposition, the adaptive techniques take the a posteriori contents of the images into account. Some of them adapt universal techniques to the signal content (i.e. adaptive subband coding or hybrid methods). Others are based on feature extraction processes which include more physical entities like edges and regions. For example, object approaches attempt to describe images as collections of regions characterized by their shape and their aspect.

Résumé

Ľarticle présente les techniques de décorrélation actuellement utilisées ou développées en traitement ďimages. Elles sont regroupées en deux classes : les méthodes universelles, encore appelées génériques et les méthodes adaptatives. Dans le premier cas, le signal est traité indépendamment de ses propres caractéristiques. Ces méthodes sont essentiellement constituées de transformations orthogonales et de décompositions en sous-bandes. Par contre, les techniques adaptatives considèrent davantage le contenu a posteriori de ľimage traitée. Certaines résultent ďune simple adaptation de techniques universelles au contenu du signal (i.e. le codage en sous-bandes auto-adaptatif ou les methodes hybrides). Ďautres reposent davantage sur ľextraction de caractéristiques propres, ou ďentités physiques comme des contours ou des régions. Par exemple, Ľapproche orientée objet essaie de décrire une image sous la forme ďune collection de régions définies par leur forme et leur contenu.

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References

  1. Ahmed (N.),Rao (K.). Orthogonal transforms for digital signal processing.Springer-Verlag (1975).

  2. Ang (P.), Ruetz (P.), Auld (D.). Video compression makes big gains.IEEE Spectrum (1991),28, n° 10, pp. 16–19.

    Article  Google Scholar 

  3. Antonini (M), Barlaud (M.), Matthmu (P.), Daubechies (I.). Image coding wavelet transform.IEEE Trans, on Image Processing (1992),1, n° 2, pp. 205–220.

    Article  Google Scholar 

  4. Ash (R.).Information theory.Dover (1990).

  5. Biggar (M.), Morris (O.), Constantinides (A.). Segmentedimage coding: performance comparison with the discrete cosine transform.IEE Proceedings F, Radar, and Signal Processing (1988),135, n° 2, pp. 121–132.

    Article  Google Scholar 

  6. Bovik (A.), Clark (M.), Geisler (W.). Multichannel texture analysis using localized spatial filters.IEEE Trans, on Pattern Analysis and Machine Intelligence (1990),12, n° 1, pp. 55–73.

    Article  Google Scholar 

  7. Burt (P.), Adelson (E.). The Laplacian pyramid as a compact image code.IEEE Trans, on Communications (1983),1, n° 4, pp. 532–540.

    Article  Google Scholar 

  8. Caselles (V.),et al. A geometric model for active contours in image processing. Cérémade (1992),Cahier de mathématique de la décision, N-9210.

  9. Chambolle (A.). Segmentation « optimale » de signaux.Problèmes non linéaires appliqués : modélisations mathématiques pour le traitement ďimages (1992), Ecole CEA-EDF-INRIA.

  10. Chaudhuri (B.), Chandrashekhar (S.). Neighboring direction runlength coding: an efficient contour coding scheme.IEEE Trans, on Systems, Man, and Cybernetics (1990),20, n° 4, pp. 916–921.

    Article  Google Scholar 

  11. Clark (R.). Relation between the Karhunen Loève and cosine transform.IEE Proceedings-F: Communications, radar and signal processing (1981),128, n° 6, pp. 359–360.

    Google Scholar 

  12. Crochiere (R.),Rabiner (L). Multirate digital signal processing.Prentice-Hall (1983).

  13. Daubechies (I.). Orthonormal bases of compactly supported wavelets.Comm. Pure and Appl. Math. (1988),41, pp. 909–996.

    Article  MATH  MathSciNet  Google Scholar 

  14. Daubechies (I.). Ten lectures on Wavelets.Siam (1992).

  15. Delsarte (P.), Macq (B.), Slock (D.). Signal-adapted multiresolution transform for image coding.IEEE Trans. on Information Theory (1992),38, n° 2, pp. 897–904.

    Article  Google Scholar 

  16. Du Buf (J.). Abstract processes in texture discrimination.Spatial Vision (1992),6, n° 3, pp. 221–242.

    Article  Google Scholar 

  17. Duda (R.),Hart (P.). Pattern classification and scene analysis.Wiley-Interscience (1973).

  18. Eden (M.), Kocher (M.). On the performance of a contour coding algorithm in the context of image coding; part I: Contour segment coding.Signal Processing (1985),8, n° 4, pp. 381–386.

    Article  Google Scholar 

  19. Farelle (P.). Recursive block coding for image data compression.Springer-Verlag (1991).

  20. Feauveau (J.). Analyse multiresolution par ondelettes non orthogonales et bancs de filtres numeriques.Ph.D. Thesis (1990), Universite Paris Sud.

  21. Freeman (H.). On the encoding of arbitrary geometric configurations.IRE Trans. on Electron. Comput. (1961),10, pp. 260–268.

    MathSciNet  Google Scholar 

  22. Froment (J.),et al. Compression ďimages et ondelettes.Problemes non linéaires appliqués : ondelettes et paquets ďondelettes (1991), Ecole CEA-EDF-INRIA.

  23. Gallager (R. G.). Variations on a theme by Huffmann.IEEE Trans, on Information Theory (1978),24, n° 6, pp. 668–674.

    Article  MATH  MathSciNet  Google Scholar 

  24. Gilge (M.), Engelhardt (T.), Mehlan (R.). Coding of arbitrarily shaped image segments based on a generalized orthogonal transform.Signal Processing: Image Communications (1989),1, n° 2, pp. 152–180.

    Google Scholar 

  25. Guillemot (C.),Cetin (E.),Ansari (JR.). M-channel nonrectangular wavelet representation for 2-D signal : Basis for quincunx sampled signals.Int. Conf. on Acoustics, Speech and Signal Processing (1991), pp. 2813–2816.

  26. Haralick (R.), Sternberg (S.), Zhuang (X.). Image analysis using mathematical morphology.IEEE Trans, on Pattern Analysis and Machine Intelligence (1987),9, n° 4, pp. 532–550.

    Article  Google Scholar 

  27. Holschneider (M.),et al. Pointwise analysis of Riemannés non differentiable function.Inventiones Math., Springer Verlag (1991).

  28. Huang (T.). Coding of two-tone images.IEEE Trans, on Communications (1977),25, n° 11, pp. 1406–1424.

    Article  MATH  Google Scholar 

  29. Hurt (N.).Phase retrieval and zero crossings.Kluwer (1989).

  30. Habibi (A.). Hybrid coding of pictorial data.IEEE Trans. on Communications (1974),22, n° 5, pp. 614–624.

    Article  Google Scholar 

  31. Jain (A.). Image data compression: a review.Proc. of the IEEE (1981),69, n° 3, pp. 349–389.

    Article  Google Scholar 

  32. Jerri (A.). The Shannon sampling theorem, its various extensions and applications: a tutorial review.Proc. of the IEEE (1977),65, n° 11, pp. 1565–1596.

    Article  MATH  Google Scholar 

  33. Johnston (J.). A filter family designed for use in quadrature mirror filter banks.Int. Conf. on Acoustics, Speech and Signal Processing (1987), pp. 191–195.

  34. Jurgen (R.). Digital video.IEEE Spectrum (1992),29, n° 3, pp. 24–30.

    Article  Google Scholar 

  35. Karlsson (G.), Vetterli (M.). Theory of two-dimensional multirate filter banks.IEEE Trans, on Acoustics, Speech, and Signal Processing (1990),8, n° 6, pp. 925–937.

    Article  Google Scholar 

  36. Koepfler (G.). Texturediscrimination : a review of the theory and its algorithms.Problèmes non linéaires appliqués : modélisations mathématiques pour le traitement ďimages (1992), Ecole CEA-EDF-INRIA.

  37. Kovacevic (J.), Vetterli (M.). Nonseparable multidimensional perfect reconstruction filter banks and wavelet bases for Rn.IEEE Trans, on Information Theory (1992),38, n° 2, pp. 533–555.

    Article  MathSciNet  Google Scholar 

  38. Kunt (M.). Traitementnumerique du signal.Dunod (1981).

  39. Kunt (M.), Benard (M.), Leonardi (JR.). Recent results in high-compression image coding.IEEE Trans, on Circuits and Systems (1987),34, n° 11, pp. 1306–1336.

    Article  Google Scholar 

  40. Leou (F.-C), Chen (Y.-C). A contour-based image coding technique with its texture information reconstructed by polyline representation.Signal Processing (1991),5, n° 1, pp. 81–89.

    Article  Google Scholar 

  41. Linde (Y.), Buzo (A.), Gray (R.). An algorithm for vector quantizer design.IEEE Trans, on Communications (1980),28, n° 1, pp. 84–95.

    Article  Google Scholar 

  42. Lu (C.-C), Dunham (J.). Highly efficient coding schemes for contour lines based on chain code representations.IEEE Trans, on Communications (1991),59, n° 10, pp. 1511–1514.

    Article  Google Scholar 

  43. Lu (Y.), Jain (R.). Reasoning about edges in scale space.IEEE Trans, on Pattern Analysis and Machine Intelligence (1992),14, n° 4, pp. 450–468

    Article  Google Scholar 

  44. Macq (B.). Perceptual transforms and universal entropy coding for an integrated approach to picture coding.Ph.D. Thesis (1989), Universite Cafholique de Louvain.

  45. Maes (S.). Hints on the relationship between subband coding, wavelet and vaguelette decompositions and cosine sine alterned transformations.European Signal Processing Conference, Brussels, EURASIP (1992), pp. 1041–1044.

  46. Maes (S.). Adaptive optimal decompositions with hybrid dct-subband coding.Int. Conf. on Signal Processing Time-Frequency and Time-Scale Analysis (1992), pp. 479–482.

  47. Maes (S.), Van Droogenbroeck (M.). Edge characterization in hierarchical subband coding schemes.Int. Conf. on Image Processing and its Applications (1992), pp. 598–601.

  48. Maes (S.). The H-point theorem applied in signal processing.Int. Conf. on Signal Processing Time-Frequency and Time-Scale Analysis (1992), pp. 467–470.

  49. Mallat (S.). A theory for multiresolution signal decomposition: the wavelet representation.IEEE Trans. on Pattern Analysis and Machine Intelligence (1989),11, n° 7, pp. 674–693.

    Article  MATH  Google Scholar 

  50. Mallat (S.),et al. Wavelet maxima representation.Wavelets and Applications, Masson/Springer-Verlag (1991).

  51. Mallat (S.),Zhang (Z.). Adaptive time-frequency decomposition with matching pursuits.Int. Conf. on Signal Processing Time-Frequency and Time-Scale Analysis (1992), pp. 7–10.

  52. Malvar (H.), Staelin (D.). The lot: transform coding without blocking effects.IEEE Trans, on Acoustics, Speech, and Signal Processing (1989),37, n° 4, pp. 553–559.

    Article  Google Scholar 

  53. Malvar (H.). Lapped transforms for efficient transform/subband coding.IEEE Trans, on Acoustics, Speech, and Signal Processing (1990),38, n° 6, pp. 969–978.

    Article  Google Scholar 

  54. Maragos (P.), Schafer (R.). Morphological skeleton representation and coding of binary images.IEEE Trans, on Acoustics, Speech and Signal Processing (1986),34, n° 5, pp. 1228–1244.

    Article  Google Scholar 

  55. Marr (D.), Hildreth (E.). Theory of edge detection.Proc. of Royal Soc. of London (1980),207, pp. 187–217.

    Google Scholar 

  56. Martens (J.), Majoor (G.). The perceptual relevance of scale-space image coding.Signal Processing (1989),17, n° 4, pp. 353–364.

    Article  Google Scholar 

  57. Mead (C). Analog vlsi and neural systems.Addison Wesley (1989).

  58. Mead (C.), Ismail (M.). Analog vlsi implementation of neural systems.Kluwer Academia (1989).

  59. Meyer (F.), Beucher (S.). Morphological segmentation.Journal of Visual Communication and Image Representation (1990),1, n° 1, pp. 21–46.

    Article  Google Scholar 

  60. Minami (T.), Shinohara (K.). Encoding of line drawings with a multiple grid chain code.IEEE Trans, on Pattern Analysis and Machine Intelligence (1986),8, n° 2, pp. 269–276.

    Google Scholar 

  61. Mussmann (G.), Ptrsch (P.), Grallert (H.-J.). Advances in picture coding.Proc. of the IEEE (1985),73, n° 4, pp. 523–548.

    Article  Google Scholar 

  62. Nasrabadi (N.), King (R.). Image coding using vector quantization: a review.IEEE Trans, on Communications (1988),36, n° 8, pp. 957–971.

    Article  Google Scholar 

  63. Netravali (A.),Haskell (B.). Digital pictures, representations and compression.Plenum (1988).

  64. Olzak (L.),Thomas (J.). Seeing spatial patterns, chapter 7 ofHandbook of perception and human performance, vol. 1. Sensory processes and perception.John Wiley and Sons (1986).

  65. Papoulis (A.). Probability, random variable and stochastics processes.McGraw-Hill (1984).

  66. Pavlidis (T). Structural pattern recognition.Springer-Verlag (1977).

  67. Perantonis (S.), Lisboa (P.). Translation, rotation and scale invariant pattern recognition of high order neural networks and moment classifiers.IEEE Trans, on Neural Networks (1992),3, n° 2, pp. 241–251.

    Article  Google Scholar 

  68. Pitas (I.), Anastasios (N.), Venetsanopoulos (A.). Morphological shape decomposition.IEEE Trans. on Acoustics, Speech, and Signal Processing (1990),12, n° 1, pp. 38–45.

    Google Scholar 

  69. Rioul (O.), Vetterli (M.). Wavelets and signal processing.IEEE Signal Processing Magazine (1991),8, n° 4, pp. 14–38.

    Article  Google Scholar 

  70. Ronse (C.). On idempotence and related requirements in edge detection.Philips Research Laboratory (1990).

  71. Schalkoff (R.). Digital image processing and computer vision.Wiley (1989).

  72. Serra (J.). Image analysis and mathematical morphology.Academic Press (1982).

  73. Serra (J.), Vincent (L.). An overview of morphological filtering.Circuits Systems and Signal Process (1992),11, n° 1, pp. 47–108.

    Article  MATH  MathSciNet  Google Scholar 

  74. Shannon (C). A mathematical theory of communication.Bell Syst. Tech. J. (1974),27, pp. 379–423 and 623-656.

    MathSciNet  Google Scholar 

  75. Simoncelli (E.), Adelson (E.). Non-separable extensions of quadrature mirror filters to multiple dimensions.Proc. of the IEEE (1990),78, n° 4, pp. 652–664.

    Article  Google Scholar 

  76. Soryani (M.), Clarke (R.). Segmented coding of digital image sequences.IEE Proceedings I, Communications, Speech and Vision (1992),139, n° 2, pp. 212–218.

    Google Scholar 

  77. Vatdyanathan (P.). Multirate digital filters, filter banks, polyphase networks, and applications: a tutorial.Proc. of the IEEE (1990),78, n° 1, pp. 56–93.

    Article  Google Scholar 

  78. Vandendorpe (L.), Macq (B.). Optimum quality and progressive resolution of video signals.Ann. Telecommunic. (1990),45, n° 9–10, pp. 487-502.

    Google Scholar 

  79. Vandendorpe (L.). Optimized quantization for image subband coding.Signal Processing (1991),4, n° 1, pp. 65–79.

    Google Scholar 

  80. Vandendorpe (L.). Hierarchical coding of digital moving picture.Ph.D. Thesis (1991), Université Catholique de Louvain.

  81. Van Droogenbroeck (M.), Vandendorpe (L.). Integration of the selective deconvolution algorithm into a multiresolution scheme.Image Processing: Algorithms and Techniques HI (1992),1657, pp. 225–235, San Jose, SPIE.

    Google Scholar 

  82. Venkatesh (S.),Owens (R.). Implementation detail of a feature detection algorithm.Technical report 89/12 (1989), Univ. Western Australia, Dpt. Comp. Sc.

  83. Vetterli (M.), Le Gall (D.). Perfect reconstruction fir filter banks: some properties and factorizations.IEEE Trans. on Acoustics, Speech, and Signal Processing (1989),37, n° 7, pp. 1057–1071.

    Article  Google Scholar 

  84. Viscito (E.), Allebach (J.P.). The analysis and design of multidimensional fir perfect reconstruction filter banks for arbitrary sampling lattices.IEEE Trans. on Circuits and Systems (1991),38, n° 1, pp. 29–41.

    Article  Google Scholar 

  85. Wickerhauser (M.). Picture compression by best-basis subband coding, Preprint (Yale University, 1990).

  86. Wickerhauser (M.). Talk presented at the symposium on wavelets and signal processing.CNRS-LMA, Marseille (5–9 nov 1990).

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Authors and Affiliations

  1. National Fund for Scientific Research, Belgium (FNRS)

    Serge Comes & Stéephane Maes

  2. Laboratoire de télécommunications et télédétection, Université catholique de Louvain, 3, place du Levant, B-1348, Louvain-la-Neuve, Belgium

    Marc Van Droogenbroeck

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  1. Serge Comes
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  2. Stéephane Maes
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S. Maes is also member of FYMA (Unité de Physique Théorique et Mathématique, UCL) and of the Math dpt. and CAIP (Center for Computer Aids for Industrial Productivity), at Rutgers University and M. Van Droogenbroeck is under an IRSIA (Institut pour ľEncouragement de la Recherche Scientifique dans ľlndustrie et ľAgriculture, Belgium) Grant.

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Comes, S., Maes, S. & Van Droogenbroeck, M. Recent and prospective decorrelation techniques for image processing. Ann. Télécommun. 48, 390–403 (1993). https://doi.org/10.1007/BF02995465

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