An Improved HVQ Algorithm for Compression and Rendering of Space Environment Volume Data with Multi-correlated Variables

Volume visualization can not only illustrate overall distribution but also inner structure and it is an important approach for space environment research. Space environment simulation can produce several correlated variables at the same time. However, existing compressed volume rendering methods only consider reducing the redundant information in a single volume of a specific variable, not dealing with the redundant information among these variables. For space environment volume data with multi-correlated variables, based on the HVQ-1d method we propose a further improved HVQ method by compositing variable-specific levels to reduce the redundant information among these variables. The volume data associated with each variable is divided into disjoint blocks of size 4³ initially. The blocks are represented as two levels, a mean level and a detail level. The variable-specific mean levels and detail levels are combined respectively to form a larger global mean level and a larger global detail level. To both global levels, a splitting based on a principal component analysis is applied to compute initial codebooks. Then, LBG algorithm is conducted for codebook refinement and quantization. We further take advantage of progressive rendering based on GPU for real-time interactive visualization. Our method has been tested along with HVQ and HVQ-1d on high-energy proton flux volume data, including > 5, > 10, > 30 and > 50 MeV integrated proton flux. The results of our experiments prove that the method proposed in this paper pays the least cost of quality at compression, achieves a higher decompression and rendering speed compared with HVQ and provides satisficed fidelity while ensuring interactive rendering speed.