通过熵编码的 DPCM 实现医学图像的近无损压缩。

Near-lossless compression of medical images through entropy-coded DPCM.

机构信息

Dept. of Electr. Eng. & Comput. Eng., Iowa State Univ., Ames, IA.

出版信息

IEEE Trans Med Imaging. 1994;13(3):538-48. doi: 10.1109/42.310885.

Abstract

The near-lossless, i.e., lossy but high-fidelity, compression of medical Images using the entropy-coded DPCM method is investigated. A source model with multiple contexts and arithmetic coding are used to enhance the compression performance of the method. In implementing the method, two different quantizers each with a large number of quantization levels are considered. Experiments involving several MR (magnetic resonance) and US (ultrasound) images show that the entropy-coded DPCM method can provide compression in the range from 4 to 10 with a peak SNR of about 50 dB for 8-bit medical images. The use of multiple contexts is found to improve the compression performance by about 25% to 30% for MR images and 30% to 35% for US images. A comparison with the JPEG standard reveals that the entropy-coded DPCM method can provide about 7 to 8 dB higher SNR for the same compression performance.

摘要

使用熵编码差分脉冲编码调制（DPCM）方法对医学图像进行近无损（即有损但高保真）压缩的研究。使用具有多个上下文的源模型和算术编码来提高该方法的压缩性能。在实现该方法时，考虑了两种不同的具有大量量化级别的量化器。涉及多个磁共振（MR）和超声（US）图像的实验表明，熵编码 DPCM 方法可以提供 4 到 10 的压缩比，对于 8 位医学图像，峰值信噪比（SNR）约为 50dB。使用多个上下文可使 MR 图像的压缩性能提高约 25%至 30%，US 图像的压缩性能提高约 30%至 35%。与 JPEG 标准的比较表明，对于相同的压缩性能，熵编码 DPCM 方法可以提供约 7 至 8dB 更高的 SNR。