Efficient Scalable Analysis and Coding of Hypervolume Data

三维及多维体数据记录出现在自然科学和工程科学的许多领域。磁共振断层扫描（MRT）和计算机断层扫描（CT）的动态三维体在医学图像处理中变得越来越重要。目前，本课题主要致力于利用补偿小波提升技术改进高维（超）体积数据的分析。为了得到一种改进的可伸缩表示，提出了可行的补偿方法。将小波系数直接加入到提升结构中，从根本上改变了小波系数的结构和特征，使得现有的编码方法不能再以最优的方式运行。在这个应用中，开发了新的更有效的可扩展编码方法。因此，使用了基于图的方法。搁置一种特定编码器的使用，以获得更高的编码效率。在申请人和文献目前的研究范围内，通过结合混合编码和专门的残差编码方法来获得视频数据的可伸缩无损编码，获得了可观的编码增益。因此，基于小波和混合方法的结合可以实现更灵活的分解以及对（超大）体积数据的更高编码效率。

Volumetric data records with three and more dimensions appear in many sectors of natural and engineering science. Dynamic 3-D volumes from magnetic resonance tomography (MRT) and computed tomography (CT) become more and more important in medical image processing. So far, this project focused on improving the analysis of high dimensional (hyper-) volume data by using compensated wavelet lifting. The goal is to obtain an improved scalable representation and feasible compensation methods were developed therefore. By their incorporation directly into the lifting structure, the structures and characteristics of the wavelet coefficients are modified fundamentally so existing methods for coding cannot operate in an optimum way anymore.In this application, novel more efficient methods for scalable coding are developed. Therefor, graph-based approaches are used. The usage of one specific coder is set aside to obtain a higher coding efficiency. Within the scope of the present research of the applicant and the literature, considerable coding gains were achieved by combining hybrid coding and specialized residual coding methods for obtaining scalable lossless coding of video data. Thus, the combination of wavelet-based and hybrid approaches is addressed to enable a more flexible Decomposition as well as a higher coding efficiency for (hyper-) volume data.