Share Email Print

Proceedings Paper

Image coding based on classified lapped orthogonal transform vector quantization
Author(s): Suresh Venkatraman; Jae Yeal Nam; K. R. Rao
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Classified transform coding of images using vector quantization has proved to be an efficient technique. Transform vector quantization combines the energy compaction properties of transform coding and the superior performance of vector quantization. Classification improves the reconstructed image quality considerably because of adaptive bit allocation. Block transform coding of images, traditionally using DCT, produces an undesirable effect called the blocking effect. In this paper a classified transform vector quantization technique using the lapped orthogonal transform (LOT/VQ) is presented. Image blocks are transformed using the LOT and are classified into four classes based on their structural properties. These are further divided adaptively into subvectors depending on the LOT coefficient statistics as this allows efficient distribution of bits. These subvectors are then vector quantized. The LOT/VQ is an efficient image coding algorithm which also reduces the blocking effect significantly. Coding tests using computer simulation show the effectiveness of this technique.

Paper Details

Date Published: 1 November 1992
PDF: 12 pages
Proc. SPIE 1818, Visual Communications and Image Processing '92, (1 November 1992); doi: 10.1117/12.131467
Show Author Affiliations
Suresh Venkatraman, Univ. of Texas/Arlington (United States)
Jae Yeal Nam, Video Communications (South Korea)
K. R. Rao, Univ. of Texas/Arlington (United States)

Published in SPIE Proceedings Vol. 1818:
Visual Communications and Image Processing '92
Petros Maragos, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?