基于图的补偿小波提升动态医学数据可分级无损编码
Graph-Based Compensated Wavelet Lifting for Scalable Lossless Coding of Dynamic Medical Data
摘要：
动态2D+t和3D+t医学数据的无损压缩是具有挑战性的，因为数据量巨大，固有噪声的特性，以及高比特深度。除此之外，在远程医疗应用中通常需要可扩展的表示。运动补偿时间滤波适用于医学体积数据的无损压缩，并且另外提供时间、空间和质量可伸缩性特征。为了实现高质量的低通子带，这将被用作原始数据的降尺度代表，基于图形的运动补偿最近被引入到这个框架中。然而，编码的运动信息，这是存储在邻接矩阵，是没有得到很好的研究到目前为止。本文的工作重点是对这些邻接矩阵进行编码，以使基于图的运动补偿对数据压缩是可行的。我们提出了一种新的编码方案的基础上构建所谓的运动地图。这使得第一次比较基于图形的运动补偿的性能，传统的块和网格为基础的方法。对于高质量的低通子带，我们的方法是能够优于块和网格为基础的方法，通过增加视觉质量方面的PSNR由0.53 dB和0.28 dB的CT数据，以及1.04 dB和1.90 dB的MR数据，分别，而比特率在同一时间降低。
英文摘要：
Lossless compression of dynamic 2D+t and 3D+t medical data is challenging regarding the huge amount of data, the characteristics of the inherent noise, and the high bit depth. Beyond that, a scalable representation is often required in telemedicine applications. Motion Compensated Temporal Filtering works well for lossless compression of medical volume data and additionally provides temporal, spatial, and quality scalability features. To achieve a high quality lowpass subband, which shall be used as a downscaled representative of the original data, graph-based motion compensation was recently introduced to this framework. However, encoding the motion information, which is stored in adjacency matrices, is not well investigated so far. This work focuses on coding these adjacency matrices to make the graph-based motion compensation feasible for data compression. We propose a novel coding scheme based on constructing so-called motion maps. This allows for the first time to compare the performance of graph-based motion compensation to traditional block- and mesh-based approaches. For high quality lowpass subbands our method is able to outperform the block- and mesh-based approaches by increasing the visual quality in terms of PSNR by 0.53dB and 0.28dB for CT data, as well as 1.04dB and 1.90dB for MR data, respectively, while the bit rate is reduced at the same time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
