Hierarchical Oriented Predictions for Resolution Scalable Lossless and Near-Lossless Compression of CT and MRI Biomedical Images
Hierarchical Oriented Predictions for Resolution Scalable Lossless and Near-Lossless Compression of CT and MRI Biomedical Images
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DOI：
10.1109/tip.2012.2186147
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发表时间：
2012-05
影响因子：
10.6
通讯作者：
Jonathan Taquet;C. Labit
中科院分区：
文献类型：
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作者：
Jonathan Taquet;C. Labit
我们提出了一种新的分层方法来解决可伸缩的无损和近无损(NLS)压缩。它结合了DPCM方案的适应性和新的分层定向预测器，提供了比通常的分层内插预测器或小波变换更好的压缩性能和分辨率可伸缩性。由于所提出的分层定向预测(HOP)对平滑图像并不是很有效，我们还引入了新的预测器，这些预测器使用最小二乘准则进行动态优化。在大规模医学图像数据库上获得的无损压缩结果，在CT上比分辨率可伸缩JPEG2000(J2K)高4%以上，在磁共振成像上高9%，接近不可伸缩Calic。HOP算法也非常适合于NLS压缩，与JPEG-LS相比提供了一个有趣的率失真折衷，对于高比特率(原始)医学图像，提供了等效或更好的PSNR。
We propose a new hierarchical approach to resolution scalable lossless and near-lossless (NLS) compression. It combines the adaptability of DPCM schemes with new hierarchical oriented predictors to provide resolution scalability with better compression performances than the usual hierarchical interpolation predictor or the wavelet transform. Because the proposed hierarchical oriented prediction (HOP) is not really efficient on smooth images, we also introduce new predictors, which are dynamically optimized using a least-square criterion. Lossless compression results, which are obtained on a large-scale medical image database, are more than 4% better on CTs and 9% better on MRIs than resolution scalable JPEG-2000 (J2K) and close to nonscalable CALIC. The HOP algorithm is also well suited for NLS compression, providing an interesting rate-distortion tradeoff compared with JPEG-LS and equivalent or a better PSNR than J2K for a high bit rate on noisy (native) medical images.