Near-lossless and scalable compression for medical imaging using a new adaptive hierarchical oriented prediction

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.

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