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Proceedings Paper

Adaptive boxcar/wavelet transform
Author(s): Osman G. Sezer; Yucel Altunbasak
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Paper Abstract

This paper presents a new adaptive Boxcar/Wavelet transform for image compression. Boxcar/Wavelet decomposition emphasizes the idea of average-interpolation representation which uses dyadic averages and their interpolation to explain a special case of biorthogonal wavelet transforms (BWT). This perspective for image compression together with lifting scheme offers the ability to train an optimum 2-D filter set for nonlinear prediction (interpolation) that will adapt to the context around the low-pass wavelet coefficients for reducing energy in the high-pass bands. Moreover, the filters obtained after training is observed to posses directional information with some textural clues that can provide better prediction performance. This work addresses a firrst step towards obtaining this new set of training-based fillters in the context of Boxcar/Wavelet transform. Initial experimental results show better subjective quality performance compared to popular 9/7-tap and 5/3-tap BWTs with comparable results in objective quality.

Paper Details

Date Published: 19 January 2009
PDF: 6 pages
Proc. SPIE 7257, Visual Communications and Image Processing 2009, 725719 (19 January 2009); doi: 10.1117/12.806166
Show Author Affiliations
Osman G. Sezer, Georgia Institute of Technology (United States)
Yucel Altunbasak, Georgia Institute of Technology (United States)

Published in SPIE Proceedings Vol. 7257:
Visual Communications and Image Processing 2009
Majid Rabbani; Robert L. Stevenson, Editor(s)

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