A near lossless compression domain volume rendering algorithm for floating-point time-varying volume data
Compressing floating-point time-varying volume data and achieving both high compression rate and near lossless are challenging. This paper proposes a compression domain volume rendering (CDVR) approach based on hierarchical vector quantization (HVQ) and perfect spatial hashing (PSH) techniques. First, a HVQ process is applied to the first frame to obtain codebooks and index volumes. Then, a sparse residual volume (SRV) is computed by differencing the first frame and the recovery volume, which is reconstructed by utilizing the codebooks and the index volumes. Difference volumes are calculated by differencing the adjacent frame pairs of the time-series. Thereafter, both the SRV and the difference volumes are compressed by means of PSH technique. To render the time-series, the codebooks, the index volumes and the results of PSH are decompressed on-the-fly in constant time in GPU. In addition, a high compression rate is achieved by HVQ and PSH, and the compression is near lossless. The results on varied datasets verify that the proposed method can achieve the high compression rate and near lossless compression quality for floating-point time-varying volume data, as well as high efficient CDVR.
near lossless compression compression domain volume rendering time-varying vector quantization perfect spatial hashing