基于权重的层次矢量量化体压缩算法及其在空间环境中的应用

空间环境数据可视化是空间环境预报和服务的重要手段.目前基于矢量量化的压缩体绘制算法均只考虑了数据特点和压缩效果，并未结合具体应用需求.为适应空间环境体数据可视化具体应用需求，提出了一种应用驱动的压缩体绘制算法——基于权重的层次矢量量化算法（WHVQ）.算法将体数据划分为4³的数据块，并为数据块设置权重，重点关注区域的数据块赋予相对较大的权重值.然后，对各数据块用三层结构表示.最后，对次高层和最高层分别采用基于权重的主成分分析分裂法产生初始码书，并采用基于权重的LBG算法进行码书优化和量化.实验结果表明，该算法能够在保证整体压缩效果的同时，提升局部重点关注区域的重构质量.

A Weight Based Hierarchical Vector Quantization Algorithm for Space Environment Volume Data

Visualization has been widely applied in space environment domain. However, compressed volume rendering algorithms based on VQ are concerned on fidelity and compression rate, not combined with specific application. To fulfill the specific visualization requirements for space environment volume data, an application-driven compression and rendering algorithm is proposed, which is Weight Based Hierarchical Vector Quantization (WHVQ). The volume data is initially partitioned into disjoint 43 blocks. Weights are assigned to the blocks according to their importance. The blocks are then decomposed into a three level hierarchical representation and each block is represented by a mean value and two detail vectors. To the top two levels, a splitting based on principal component analysis and weight is adopted to form their initial codebooks. Then, LBG algorithm based on weight is conducted for codebook refinement and quantization. The experimental results show that WHVQ is able to improve the quality of reconstruction in interested area on the premise of the good overall fidelity.