Image Compression by Vector Quantization with Recurrent Discrete Networks

  • Domingo López-Rodríguez
  • Enrique Mérida-Casermeiro
  • Juan M. Ortiz-de-Lazcano-Lobato
  • Ezequiel López-rubio
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)


In this work we propose a recurrent multivalued network, generalizing Hopfield’s model, which can be interpreted as a vector quantifier. We explain the model and establish a relation between vector quantization and sum-of-squares clustering. To test the efficiency of this model as vector quantifier, we apply this new technique to image compression. Two well-known images are used as benchmark, allowing us to compare our model to standard competitive learning. In our simulations, our new technique clearly outperforms the classical algorithm for vector quantization, achieving not only a better distortion rate, but even reducing drastically the computational time.


Mean Square Error Image Compression Distortion Function Lossless Compression Competitive Learning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Domingo López-Rodríguez
    • 1
  • Enrique Mérida-Casermeiro
    • 1
  • Juan M. Ortiz-de-Lazcano-Lobato
    • 2
  • Ezequiel López-rubio
    • 2
  1. 1.Department of Applied MathematicsUniversity of MálagaMálagaSpain
  2. 2.Department of Computer Science and Artificial IntelligenceUniversity of MálagaMálagaSpain

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