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EURASIP Journal on Image and Video Processing

EURASIP Journal on Image and Video Processing Cover Image
Research Article
Open Access

An Edge-Sensing Predictor in Wavelet Lifting Structures for Lossless Image Coding

EURASIP Journal on Image and Video Processing20072007:019313

https://doi.org/10.1155/2007/19313

©  Ö.N. Gerek and A.E. Çetin 2007

Received: 25 August 2006

Accepted: 5 January 2007

Published: 19 February 2007

Abstract

The introduction of lifting implementations for image wavelet decomposition generated possibilities of several applications and several adaptive decomposition variations. The prediction step of a lifting stage constitutes the interesting part of the decomposition since it aims to reduce the energy of one of the decomposition bands by making predictions using the other decomposition band. In that aspect, more successful predictions yield better efficiency in terms of reduced energy in the lower band. In this work, we present a prediction filter whose prediction domain pixels are selected adaptively according to the local edge characteristics of the image. By judicuously selecting the prediction domain from pixels that are expected to have closer relation to the estimated pixel, the prediction error signal energy is reduced. In order to keep the adaptation rule symmetric for the encoder and the decoder sides, lossless compression applications are examined. Experimental results show that the proposed algorithm provides good compression results. Furthermore, the edge calculation is computationally inexpensive and comparable to the famous Daubechies 5/3 lifting implementation.

Keywords

Decomposition VariationImage CodeEdge CharacteristicLossless CompressionAdaptation Rule

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Authors’ Affiliations

(1)
Department of Electrical and Electronics Engineering, Anadolu University, Eskişehir, Turkey
(2)
Department of Electrical Engineering, Bilkent University, Bilkent, Turkey

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Copyright

© Ö.N. Gerek and A.E. Çetin 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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