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Proceedings Paper

Classified residue vector quantization by visual patterns
Author(s): K. W. Chan; Kwok-Leung Chan
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Paper Abstract

A new classified residue vector quantization (CRVQ) by visual pattern (VP) without any side information was developed. The original image was first decomposed into a low frequency component (LFC), which was highly correlated with the original, and a residue. The residue was classified not by itself nor the original, but by the LFC. With 15 VPs and 4 variance classes, the visual quality was enhanced from 0.5 to nearly 1.5 dB, without any penalty in bit rate.

Paper Details

Date Published: 27 February 1996
PDF: 9 pages
Proc. SPIE 2727, Visual Communications and Image Processing '96, (27 February 1996); doi: 10.1117/12.233240
Show Author Affiliations
K. W. Chan, City Univ. of Hong Kong (Hong Kong)
Kwok-Leung Chan, City Univ. of Hong Kong (Hong Kong)


Published in SPIE Proceedings Vol. 2727:
Visual Communications and Image Processing '96
Rashid Ansari; Mark J. T. Smith, Editor(s)

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