Optimum Quantizer Performance for a Class of Non-Gaussian Memoryless Sources

Nariman Farvardin, James W. Modestino

Research output: Contribution to journalArticlepeer-review

217 Scopus citations


The performance of optimum quantizers subject to an entropy constraint is studied for a wide class of memoryless sources. For a general distortion criterion, necessary conditions are developed for optimality and a recursive algorithm is described for obtaining the optimum quantizer. Under a mean-square error criterion, the performance of entropy encoded uniform quantization of memoryless Gaussian sources is well-known to be within 0. 255 bits/sample of the rate-distortion bound at relatively high rates. Despite claims to the contrary, it is demonstrated that similar performance can be expected for a wide range of memoryless sources. Indeed, for the cases considered, the worst case performance is observed to be less than 0. 3 bits/sample from the rate-distortion bound, and in most cases this disparity is less at low rates.

Original languageEnglish (US)
Pages (from-to)485-497
Number of pages13
JournalIEEE Transactions on Information Theory
Issue number3
StatePublished - May 1984

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences


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