Robust Quantization of ∊-Contaminated Data

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Abstract

The problem of robust quantization of data with uncertain statistical properties is considered. Uncertainty in the statistics of the data is modeled by assuming that the data have a probability density function of the ∊-contaminated form, and a minimax approach to robust design is adopted. An approximation is developed for the asymptotic worst-case distortion (over the ∊-contarninated class) produced by an arbitrary companded quantizer, and the quantizer design which minimizes this worst-case distortion is derived. The robustness of the resulting design is Verified numerically for the particular problem of quantizing ∊-contaminated Gaussian data.

Original languageEnglish (US)
Pages (from-to)218-222
Number of pages5
JournalIEEE Transactions on Communications
Volume33
Issue number3
DOIs
StatePublished - Jan 1 1985
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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