Hierarchical Oriented Predictions for Resolution Scalable Lossless and Near-Lossless Compression of CT and MRI Biomedical Images - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles IEEE Transactions on Image Processing Year : 2012

Hierarchical Oriented Predictions for Resolution Scalable Lossless and Near-Lossless Compression of CT and MRI Biomedical Images

Abstract

We propose a new hierarchical approach for lossless and near-lossless resolution scalable compression. It combines the adaptability of DPCM schemes with new hierarchical oriented predictors in order to provide resolution scalability with better compression performances than usual hierarchical interpolation predictor or wavelet transform. Because the proposed hierarchical oriented prediction (HOP) is not really efficient on smooth images, we also introduce new predictors, dynamically optimized using a least-square criterion. Lossless compression results, obtained on a large-scale medical images database, are more than 4% better on CTs and 9% better on MRIs than resolution scalable JPEG-2000 and close to non-scalable CALIC. 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 JPEG-2000 for high bit-rate on noisy (native) medical images.
No file

Dates and versions

hal-00755741 , version 1 (21-11-2012)

Identifiers

  • HAL Id : hal-00755741 , version 1

Cite

Jonathan Taquet, Claude Labit. Hierarchical Oriented Predictions for Resolution Scalable Lossless and Near-Lossless Compression of CT and MRI Biomedical Images. IEEE Transactions on Image Processing, 2012, 21 (5), pp.2641-2652. ⟨hal-00755741⟩
145 View
0 Download

Share

Gmail Facebook X LinkedIn More