NEAR-LOSSLESS AND SCALABLE COMPRESSION FOR MEDICAL IMAGING USING A NEW ADAPTIVE HIERARCHICAL ORIENTED PREDICTION

Jonathan Taquet 1 Claude Labit 1
1 TEMICS - Digital image processing, modeling and communication
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : A new adaptive approach for lossless and near-lossless scalable compression of medical images is presented. It combines the adaptivity of DPCM schemes with hierarchical oriented prediction (HOP) in order to provide resolution scalability with better compression performances. We obtain lossless results which are about 4% better than resolution scalable JPEG2000 and close to non scalable CALIC on a large scale database. The HOP algorithm is also well suited for near-lossless compression, providing interesting rate-distortion trade-off compared to JPEG-LS and equivalent or better PSNR than JPEG2000 for high bit-rate on noisy (native) medical images.
Type de document :
Communication dans un congrès
2010 International Conference on Image Processing (ICIP 2010), Sep 2010, HONG KONG, China. 2010
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https://hal.inria.fr/inria-00538794
Contributeur : Jonathan Taquet <>
Soumis le : mardi 23 novembre 2010 - 12:33:09
Dernière modification le : mercredi 11 avril 2018 - 01:54:12

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  • HAL Id : inria-00538794, version 1

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Jonathan Taquet, Claude Labit. NEAR-LOSSLESS AND SCALABLE COMPRESSION FOR MEDICAL IMAGING USING A NEW ADAPTIVE HIERARCHICAL ORIENTED PREDICTION. 2010 International Conference on Image Processing (ICIP 2010), Sep 2010, HONG KONG, China. 2010. 〈inria-00538794〉

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