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Multi-class Image Coding via EM-KLT Algorithm

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Neural Nets (WIRN 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2859))

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Abstract

This paper presents a new image coding method in which the image blocks are assigned to different classes learned by the EM algorithm. Each class is matched to a multidimensional Gaussian density function and the Karhunen-Loeve Transform (KLT), followed by optimal quantization and coding, is applied to each one of them. The performance of this Class-KLT coder is compared to the classical KLT coder (one class) showing appreciable improvement in image quality.

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References

  1. Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society B39, 1–38 (1977)

    MathSciNet  Google Scholar 

  2. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, Chichester (2001)

    MATH  Google Scholar 

  3. Jain, A.K.: Fundamentals of Digital Image Processing. Prentice-Hall, Englewood Cliffs (1989)

    MATH  Google Scholar 

  4. Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Wiley, Chichester (1994)

    Google Scholar 

  5. Gray, R.M.: Gauss mixture vector quantization. In: Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (2001)

    Google Scholar 

  6. Ortega, A., Vetterli, M.: Adaptive Scalar quantization without side information. IEEE Trans. on Image Processing 6, 665–676 (1997)

    Article  Google Scholar 

  7. Subramaniam, A.D., Rao, B.D.: PDF optimized parametric vector quantization of speech line spectral frequencies. In: Proc. IEEE Workshop on Speech Coding (2000)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Dip. di Ingegneria dell’Informazione, Seconda Università di Napoli, via Roma, 29, 81031, Aversa, CE, Italy

    Alessandra Budillon & Francesco Palmieri

Authors
  1. Alessandra Budillon
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  2. Francesco Palmieri
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Editors and Affiliations

  1. Dipartimentimento di Scienze dell’Informazione, via Comelico 39/41, 20135, Milano, Italy

    Bruno Apolloni

  2. Dipartimento di Fisica “E.R. Caianiello”, Università degli Studi di Salerno, Via S. Allende, 84081, Baronissi, (SA), Italy

    Maria Marinaro

  3. Department of Mathematics and Informatics, University of Salerno, Via Ponte Don Melillo, 84084, Fisciano, (SA), Italy

    Roberto Tagliaferri

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© 2003 Springer-Verlag Berlin Heidelberg

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Budillon, A., Palmieri, F. (2003). Multi-class Image Coding via EM-KLT Algorithm. In: Apolloni, B., Marinaro, M., Tagliaferri, R. (eds) Neural Nets. WIRN 2003. Lecture Notes in Computer Science, vol 2859. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45216-4_11

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  • DOI: https://doi.org/10.1007/978-3-540-45216-4_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20227-1

  • Online ISBN: 978-3-540-45216-4

  • eBook Packages: Springer Book Archive

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