Vector quantization for entropy coding of image subbands

Author

Senoo, Takanori ; Girod, Bernd

Author_Institution

Media Lab., MIT, Cambridge, MA, USA

Volume

1

Issue

4

fYear

1992

fDate

10/1/1992 12:00:00 AM

Firstpage

526

Lastpage

533

Abstract

Vector quantization for entropy coding of image subbands is investigated. Rate distortion curves are computed with mean square error as a distortion criterion. The authors show that full-search entropy-constrained vector quantization of image subbands results in the best performance, but is computationally expensive. Lattice quantizers yield a coding efficiency almost indistinguishable from optimum full-search entropy-constrained vector quantization. Orthogonal lattice quantizers were found to perform almost as well as lattice quantizers derived from dense sphere packings. An optimum bit allocation rule based on a Lagrange multiplier formulation is applied to subband coding. Coding results are shown for a still image

Keywords

entropy; image coding; vector quantisation; Lagrange multiplier formulation; entropy coding; full-search entropy-constrained vector quantization; image coding; image subbands; mean square error; optimum bit allocation rule; orthogonal lattice quantisers; rate distortion curves; still image; subband coding; Bit rate; Books; Encoding; Entropy coding; Image coding; Image storage; Iterative algorithms; Lattices; Rate-distortion; Vector quantization;

fLanguage

English

Journal_Title

Image Processing, IEEE Transactions on

Publisher

ieee

ISSN

1057-7149

Type

jour

DOI

10.1109/83.199923

Filename

199923