Adaptive Entropy-Coded Predictive Vector Quantization of Images

James W. Modestino, Yong Han Kim

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


We consider 2-D predictive vector quantization (PVQ) of images subject to an entropy constraint and demonstrate the substantial performance improvements over existing unconstrained approaches. Furthermore, we describe a simple adaptive buffer-instrumented implementation of this 2-D entropy-coded PVQ scheme which can accommodate the associated variable-length entropy coding while completely eliminating buffer overflow/underflow problems at the expense of only a slight degradation in performance. This scheme, called 2-D PVQ/AECQ, is shown to result in excellent rate-distortion performance and impressive quality reconstructions on real-world images. Indeed, the real-world coding results shown here demonstrate little distortion at rates as low as 0.5 b /pixel.

Original languageEnglish
Pages (from-to)633-644
Number of pages12
JournalIEEE Transactions on Signal Processing
Issue number3
StatePublished - Mar 1992


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