Compression for large-scale time-varying volume data using spatio-temporal features

Abstract

Data compression is always needed in large-scale time-varying volume visualization. In some recent application cases, the compression method is also required to provide a low-cost decompression process. In the present paper, we propose a compression scheme for large-scale time-varying volume data using the spatio-temporal features. With this compression scheme, we are able to provide a proper compression ratio to satisfy many system environments (even a low-spec environment) by setting proper compression parameters. After the compression, we can also provide a low-cost and fast decompression process for the compressed data. Furthermore, we implement a specialized particle-based volume rendering (PBVR) [2] to achieve an accelerated rendering process for the decompressed data. As a result, we confirm the effectiveness of our compression scheme by applying it to the large-scale timevarying turbulent combustion data. © Springer-Verlag Berlin Heidelberg 2013.