Unsupervised Learning Approach to Adaptive Differential Pulse Code Modulation

Norman C. Griswold, Khalid Sayood

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

This research is concerned with investigating the problem of data compression utilizing an unsupervised estimation algorithm. This extends previous work utilizing a hybrid source coder which combines an orthogonal transformation with differential pulse code modulation (DPCM). The data compression is achieved in the DPCM loop, and it is the quantizer of this scheme which is approached from an unsupervised learning procedure. The distribution defining the quantizer is represented as a set of separable Laplacian mixture densities for two-dimensional images. The condition of idcntifiability is shown for the Laplacian case and a decision directed estimate of both the active distribution parameters and the mixing parameters are discussed in view of a Bayesian structure. The decision directed estimators, although not optimum, provide a realizable structure for estimating the parameters which define a distribution which has become active. These parameters are then used to scale the optimum (in the mean square error sense) Laplacian quantizer. The decision criteria is modified to prevent convergence to a single distribution which in effect is the default condition for a variance estimator. This investigation was applied to a test image and the resulting data demonstrate improvement over other techniques using fixed bit assignments and ideal channel conditions.

Original languageEnglish (US)
Pages (from-to)380-391
Number of pages12
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
VolumePAMI-4
Issue number4
DOIs
StatePublished - Jul 1982

Keywords

  • Adaptive differential pulse code modulation (DPCM)
  • adaptive quantization
  • bandwidth compression
  • decision directed estimates
  • unsupervised learning

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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