Pruned tree-structured vector quantization in image coding

Eve A. Riskin, Elizabeth M. Daly, Robert M. Gray

Research output: Contribution to journalConference articlepeer-review

14 Scopus citations

Abstract

A recently developed technique for variable-rate vector quantizer (VQ) design has been applied to both memoryless and predictive VQ of images. This technique, called pruned tree-structured vector quantization (PTSVQ), uses variable-depth encoders that are tree-structured and thus have very low design and search complexity. PTSVQ is applied to a series of medical images, and gains over full-search VQ of up to 3.78 dB in the signal-to-noise-ratio (SNR) are measured. On still images from the USC database, gains of up to 1.63 dB in the peak SNR are realized for predictive PSTVQ over predictive full search VQ, resulting in high image quality at 0.51 bits per pixel.

Original languageEnglish
Pages (from-to)1735-1738
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume3
StatePublished - 1989
Event1989 International Conference on Acoustics, Speech, and Signal Processing - Glasgow, Scotland
Duration: 23 May 198926 May 1989

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