LAMDA Methodology Applied to Image Vector Quantization

  • E. Guzmán
  • J. G. Zambrano
  • I. García
  • Oleksiy Pogrebnyak
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 95)


In this paper we present a novel approach to image vector quantization (VQ) based on Learning Algorithm for Multivariate Data Analysis (LAMDA methodology). The proposed algorithm, named VQ-LAMDA, employs a codebook generated by LBG algorithm, which must be normalized to obtain a new codebook representation, named LAMDA-codebook. The classification phase of the LAMDA methodology uses the fuzzy binomial distribution to determine marginal adequacy degrees between input vectors and LAMDA-codebook. Then, to obtain global adequacy degrees, are used the operators min-max and product and a linear convex function. Finally, using the Maximum Adequacy rule are calculate a set of indices of the codewords, to which every input vector belongs. The computer simulation results demonstrate that the proposed algorithm provides better performance than other VQ methods based on the LGB algorithm, at the peak signal-to-noise ratio (PSNR) parameter.


Input Vector Codebook Size Index Vector Quantization Codebook Generation Vector Quantization Method 
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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© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • E. Guzmán
    • 1
  • J. G. Zambrano
    • 1
  • I. García
    • 1
  • Oleksiy Pogrebnyak
    • 2
  1. 1.Universidad Tecnológica de la MixtecaHuajuapan de LeónMéxico
  2. 2.Centro de Investigación en Computación del IPNCd. de México

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