Adaptive entropy-coded predictive vector quantization of images

J. W. Modestino, Y. H. Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Summary form only given, as follows. The authors describe a new approach to image coding based on adaptive entropy-coded 2-D predictive vector quantization (PVQ) ideas. PVQ is a straightforward vector extension of ordinary scaler predictive quantization schemes, such as DPCM, where the vector quantizer (PVQ) is now embedded in the predictive feedback loop. Prediction is then performed on a vector or block basis using previously encoded blocks, with the prediction error blocks subsequently applied, on a block-by-block basis, to the VQ. Although PVQ is not new, previous applications have not attempted to exploit the further compressibility of the VQ output through use of variable-length entropy coding. The authors consider 2-D PVQ of images subject to an entropy constraint and demonstrate the substantial performance improvements over existing 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, 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 are rather striking and demonstrate almost imperceptible distortions at rates as low as 0.5 b/pixel.

Original languageEnglish
Title of host publicationUnknown Host Publication Title
Place of PublicationPiscataway, NJ, United States
PublisherPubl by IEEE
StatePublished - Dec 1 1990
Event1990 IEEE International Symposium on Information Theory - San Diego, CA, USA
Duration: Jan 14 1990Jan 19 1990

Other

Other1990 IEEE International Symposium on Information Theory
CitySan Diego, CA, USA
Period1/14/901/19/90

ASJC Scopus subject areas

  • Engineering(all)

Fingerprint Dive into the research topics of 'Adaptive entropy-coded predictive vector quantization of images'. Together they form a unique fingerprint.

  • Cite this

    Modestino, J. W., & Kim, Y. H. (1990). Adaptive entropy-coded predictive vector quantization of images. In Unknown Host Publication Title Publ by IEEE.