DCRVQ: a new strategy for efficient entropy coding of vector-quantized images

Author

De Natale, Francesco G B ; Fioravanti, Stefano ; Giusto, Daniele D.

Author_Institution

Dept. of Biophys. & Electr. Eng., Genova Univ., Italy

Volume

44

Issue

6

fYear

1996

fDate

6/1/1996 12:00:00 AM

Firstpage

696

Lastpage

706

Abstract

This paper presents a novel predictive coding scheme for image-data compression by vector quantization (VQ). On the basis of a prediction, further compression is achieved by using a dynamic codebook-reordering strategy that allows a more efficient Huffman encoding of vector addresses. The proposed method is lossless, for it increases the compression performances of a baseline vector quantization scheme, without causing any further image degradation. Results are presented and a comparison with Cache-VQ is made

Keywords

Huffman codes; entropy codes; image coding; prediction theory; vector quantisation; Cache-VQ; DCRVQ; Huffman encoding; compression performance; dynamic codebook reordering VQ; entropy coding; image data compression; lossless coding method; neural approach; predictive coding; vector addresses; vector quantized images; Data compression; Degradation; Encoding; Entropy coding; Image coding; Mean square error methods; Multidimensional systems; Pixel; Predictive coding; Vector quantization;

fLanguage

English

Journal_Title

Communications, IEEE Transactions on

Publisher

ieee

ISSN

0090-6778

Type

jour

DOI

10.1109/26.506386

Filename

506386