04-12-2012, 04:25 PM
An Image Multiresolution Representation for Lossless and Lossy Compression
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Abstract
We propose a new image multiresolution transform that is suited for both lossless
reversible and lossy compression The new transformation is similar to the subband
decomposition but can be computed with only integer addition and bitshift operations
During its calculation the number of bits required to represent the transformed image
is kept small through careful scaling and truncations Numerical results show that the
entropy obtained with the new transform is smaller than that obtained with predictive
coding of similar complexity In addition we propose entropycoding methods that exploit
the multiresolution structure and can eciently compress the transformed image for
progressive transmission up to exact recovery The lossless compression ratios are among
the best in the literature and simultaneously the rate vs distortion performance is
comparable to those of the most ecient lossy compression methods
Introduction
There are important image applications where some processing eg subtraction ltering
contrast enhancement etc should be applied to archived or transmitted images In those
cases lossy compression methods may destroy some of the information required during pro
cessing or add artifacts which lead to erroneous interpretations Quite frequently the user of
those applications wants to have total control of the precision in which the image pixels are
represented and prefers to have the image compressed with a lossless or reversible method