一种大规模体数据压缩体绘制策略

Compressed Volume Rendering for Large-scale Volume Data

针对基于GPU的大规模体数据直接体绘制过程中遇到的显存不足的问题,提出了一种大规模体数据的压缩绘制策略.该策略结合小波变换和分类矢量量化进行数据压缩,采用基于GPU的光线投射算法进行绘制,在绘制时,只解压变换当前绘制所需要的极少数数据,并结合多分辨率绘制,实现实时交互.基于CUDA的实验表明:该压缩绘制策略有效解决了显存不足问题,并且在保证交互绘制帧率的前提下获得了较好的成像质量.

In order to solve the problem of video memory limitation during GPU-based visualization of large scale volume data, a compressing and rendering strategy was proposed. Volume data was compressed by combining wavelet transform with vector quantization algorithm, and then transferred to GPU totally. When rendering, a GPU-based ray casting algorithm was adopted and only few current needed data was decompressed and transformed. In order to realize real-time interactive visualization, a multi-resolution rendering method was designed. Experimental results based on CUDA show that this strategy can decrease the video memory consumption effectively and obtain better image quality while ensuring interactive rendering frame rate.