Pruned tree-structured vector quantization in image coding

Author

Riskin, Evan A. ; Daly, Elizabeth M. ; Gray, R.M.

Author_Institution

Inf. Syst. Lab., Stanford Univ., CA, USA

fYear

1989

Firstpage

1735

Abstract

A recently developed technique for variable-rate vector quantizer (VQ) design by P.A. Chou et al. (see IEEE Trans. Inf. Theory, vol.35, no.2, p.299-315, 1989) 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 PTSVQ over predictive full search VQ, resulting in high image quality at 0.51 bits per pixel

Keywords

data compression; encoding; filtering and prediction theory; picture processing; PTSVQ; USC database; image coding; medical images; memoryless vector quantisation; predictive vector quantisation; pruned tree-structured vector quantization; signal-to-noise-ratio; variable-depth encoders; variable-rate vector quantizer; Algorithm design and analysis; Biomedical imaging; Code standards; Image coding; Image databases; Information systems; Laboratories; PSNR; Pixel; Vector quantization;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1989. ICASSP-89., 1989 International Conference on

Conference_Location

Glasgow

ISSN

1520-6149

Type

conf

DOI

10.1109/ICASSP.1989.266784

Filename

266784