Adaptive entropy-coded predictive vector quantization of images

The authors consider 2-D predictive vector quantization (PVQ) of images subject to an entropy constraint and demonstrate the substantial performance improvements over existing unconstrained approaches. They describe a simple adaptive buffer-instrumented implementation of this 2-D entropy-coded PVQ s...

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Bibliographic Details
Published inIEEE transactions on signal processing Vol. 40; no. 3; pp. 633 - 644
Main Authors Modestino, J.W., Kim, Y.H.
Format Journal Article
LanguageEnglish
Published IEEE 01.03.1992
Subjects
Degradation
Encoding
Entropy coding
Image coding
Image reconstruction
Pixel
Rate-distortion
Statistics
Vector quantization
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ISSN1053-587X
DOI10.1109/78.120806

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Summary:The authors consider 2-D predictive vector quantization (PVQ) of images subject to an entropy constraint and demonstrate the substantial performance improvements over existing unconstrained approaches. They 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 (adaptive entropy-coded quantization), is shown to result in excellent rate-distortion performance and impressive quality reconstructions of real-world images. Indeed, the real-world coding results shown demonstrate little distortion at rates as low as 0.5 b/pixel.< >
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ISSN:1053-587X
DOI:10.1109/78.120806