Miaou Shaou-Gang, Chen Shih-Tse
Multimedia Computing and Telecommunication Laboratory, Department of Electronic Engineering, Chung Yuan Christian University, 32023 Taiwan, R.O.C.
IEEE Trans Med Imaging. 2004 Nov;23(11):1417-29. doi: 10.1109/TMI.2004.835312.
The enormous data of volumetric medical images (VMI) bring a transmission and storage problem that can be solved by using a compression technique. For the lossy compression of a very long VMI sequence, automatically maintaining the diagnosis features in reconstructed images is essential. The proposed wavelet-based adaptive vector quantizer incorporates a distortion-constrained codebook replenishment (DCCR) mechanism to meet a user-defined quality demand in peak signal-to-noise ratio. Combining a codebook updating strategy and the well-known set partitioning in hierarchical trees (SPIHT) technique, the DCCR mechanism provides an excellent coding gain. Experimental results show that the proposed approach is superior to the pure SPIHT and the JPEG2000 algorithms in terms of coding performance. We also propose an iterative fast searching algorithm to find the desired signal quality along an energy-quality curve instead of a traditional rate-distortion curve. The algorithm performs the quality control quickly, smoothly, and reliably.
体积医学图像（VMI）的海量数据带来了一个可通过使用压缩技术解决的传输和存储问题。对于非常长的VMI序列的有损压缩，自动保持重建图像中的诊断特征至关重要。所提出的基于小波的自适应矢量量化器采用了失真约束码本补充（DCCR）机制，以满足用户定义的峰值信噪比质量要求。结合码本更新策略和著名的分层树集合划分（SPIHT）技术，DCCR机制提供了出色的编码增益。实验结果表明，所提出的方法在编码性能方面优于纯SPIHT和JPEG2000算法。我们还提出了一种迭代快速搜索算法，以沿着能量-质量曲线而不是传统的率失真曲线找到所需的信号质量。该算法能够快速、平稳且可靠地进行质量控制。
