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Proceedings Paper

Classified wavelet transform coding of images using vector quantization
Author(s): Young Huh; J. J. Hwang; C. K. Choi; Ricardo L. de Queiroz; K. R. Rao
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Paper Abstract

The discrete wavelet transform (DWT) has recently emerged as a powerful technique for image compression in conjunction with a variety of quantization schemes. In this paper, a new image coding scheme--classified wavelet transform/vector quantization (DWT/CVQ)--is proposed to efficiently exploit correlation among different DWT layers aiming to improve its performance. In this scheme, DWT coefficients are rearranged to form the small blocks, which are composed of the corresponding coefficients from all the subbands. The block matrices are classified into four classes depending on the directional activities, i.e., energy distribution along each direction. These are further divided adaptively into subvectors depending on the DWT coefficient statistics as this allows efficient distribution of bits. The subvectors are then vector quantized. Simulation results show that under this technique the reconstruction images preserve the detail and structure in a subjective sense compared to other approaches at a bit rate of 0.3 bit/pel.

Paper Details

Date Published: 16 September 1994
PDF: 11 pages
Proc. SPIE 2308, Visual Communications and Image Processing '94, (16 September 1994); doi: 10.1117/12.185953
Show Author Affiliations
Young Huh, Univ. of Texas/Arlington (United States)
J. J. Hwang, Univ. of Texas/Arlington (United States)
C. K. Choi, Univ. of Texas/Arlington (United States)
Ricardo L. de Queiroz, Univ. of Texas/Arlington (United States)
K. R. Rao, Univ. of Texas/Arlington (United States)


Published in SPIE Proceedings Vol. 2308:
Visual Communications and Image Processing '94
Aggelos K. Katsaggelos, Editor(s)

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