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Pattern Recognition Letters
Volume 24, Issue 15, November 2003, Pages 2767-2776
 
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doi:10.1016/S0167-8655(03)00120-X    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2003 Published by Elsevier Science B.V.

Feature preserving image compression*1

Kameswara Rao NamuduriCorresponding Author Contact Information, E-mail The Corresponding Author, a and Veeru N. Ramaswamyb

a ECE Department, Wichita State University, 1845 Fairmount Avenue, Kansas, KS 67260, USA b Broadband Engineering, Comcast IP Services, Comcast Corporation, 3 Executive Campus, Fifth Floor, Cherry Hill, NJ 08002, USA

Received 27 November 2002; 
revised 7 April 2003. 
Available online 25 June 2003.

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Abstract

Image details appear as wavelet coefficients with large magnitude in the wavelet transform domain. Image compression methods such as the embedded zerotree wavelet encoding and the set partitioning in hierarchical trees select wavelet coefficients in the order of their significance (magnitude) and encode them generating an embedded bit stream. In existing wavelet based image compression techniques, the significance of a wavelet coefficient is solely defined by its magnitude.

In this paper, we describe a flexible scheme to prioritize wavelet coefficients based on the features they exhibit. The proposed scheme combines tree based wavelet coefficient representation with the implicit transmission of data about image features that need to be emphasized. The experimental results presented in this paper demonstrate that it is possible to enhance the image features in the reconstructed images by embedding locally adaptive image processing techniques in the compression algorithm. The main advantage of the proposed technique over the existing methods is that it exploits the embedded zerotree data structure to eliminate the need to send side (additional) information to the decoder regarding the feature selection process.

Article Outline

1. Introduction
2. Related work
2.1. Image compression framework
3. Zerotree framework and terminology
3.1. Zerotree data structure
3.2. Terminology
4. Prioritizing wavelet coefficients
4.1. Selection of wavelet coefficients
4.2. Implementation of the prioritization scheme
5. Experiments
5.1. Feature based selection
6. Summary and conclusions
References






Pattern Recognition Letters
Volume 24, Issue 15, November 2003, Pages 2767-2776
 
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