Volume xx (200y), Number z, pp. 1–25 State-of-the-art in Compressed GPU-Based Direct Volume Rendering

Figure 1: Frames from real-time rendering of animated supernova data set (4323×60, float- 18GB), compressed using sparse coding of voxel blocks [GIM12] (block size 6, sparsity 4) at 0.10 bps, PSNR 46.57 dB. The full compressed dataset (184MB) fits in GPU memory and is available for low-latency local and transient decoding during rendering. Data made available by Dr. John Blondin at the North Carolina State University through US DoE’s SciDAC Institute for Ultrascale Visualization. Great advancements in commodity graphics hardware have favored GPU-based volume rendering as the main adopted solution for interactive exploration of rectilinear scalar volumes on commodity platforms. Nevertheless, long data transfer times and GPU memory size limitations are often the main limiting factors, especially for massive, time-varying or multi-volume visualization, as well as for networked visualization on the emerging mobile devices. To address this issue, a variety of level-of-detail data representations and compression techniques have been introduced. In order to improve capabilities and performance over the entire storage, distribution and rendering pipeline, the encoding/decoding process is typically highly asymmetric, and systems should ideally compress at data production time and decompress on demand at rendering time. Compression and level-of-detail pre-computation does not have to adhere to real-time constraints and can be performed off-line for high quality results. In contrast, adaptive real-time rendering from compressed representations requires fast, transient, and spatially independen