Great advancements in commodity graphics hardware have favoured graphics processing unit 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 LOD 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 LOD 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 independent decompression. In this report, we review the existing compressed GPU volume rendering approaches, covering sampling grid layouts, compact representation models, compression techniques, GPU rendering architectures and fast decoding techniques.

State-of-the-Art in Compressed GPU-Based Direct Volume Rendering

Data in volume form consumes an extraordinary amount of storage space. For efficient storage and transmission of such data, compression algorithms are imperative. However, most volumetric datasets are used in biomedicine and other scientific applications where lossy compression is unacceptable. We present a lossless data-compression algorithm which, being oriented specifically for volume data, achieves greater compression performance than generic compression algorithms that are typically available on modern computer systems. Our algorithm is a combination of differential pulse-code modulation (DPCM) and Huffman coding and results in compression of around 50% for a set of volume data files.

Lossless Compression of Volume Data.

Large scale and structurally complex volume datasets from high-resolution 3D imaging devices or computational simulations pose a number of technical challenges for interactive visual analysis. In this paper, we present the first integration of a multiscale volume representation based on tensor approximation within a GPU-accelerated out-of-core multiresolution rendering framework. Specific contributions include (a) a hierarchical brick-tensor decomposition approach for pre-processing large volume data, (b) a GPU accelerated tensor reconstruction implementation exploiting CUDA capabilities, and (c) an effective tensor-specific quantization strategy for reducing data transfer bandwidth and out-of-core memory footprint. Our multiscale representation allows for the extraction, analysis and display of structural features at variable spatial scales, while adaptive level-of-detail rendering methods make it possible to interactively explore large datasets within a constrained memory footprint. The quality and performance of our prototype system is evaluated on large structurally complex datasets, including gigabyte-sized micro-tomographic volumes.

/pdf/interactive-multiscale-tensor-reconstruction-for-k9el20kg42.pdf

Interactive Multiscale Tensor Reconstruction for Multiresolution Volume Visualization

The authors would like to warmly thank Peter Lindstrom (ZFP), Mar Treib (CC), and Sheng Di, Dingwen Tao, Xin Liang (SZ) for making their ompression odes availableDatasets ISO, HBDT and CHAN are ourtesy of the Johns Hopkins Turbulene Database (JHTDB) initiative. Dataset RT is ourtesy of LLNL. We also aknowledge the ontribution of Sardinian Regional Authorities (projets VIGECLAB and TDM) and of King Abdullah University of Siene and Tehnology (KAUST).

/pdf/a-framework-for-gpu-accelerated-exploration-of-massive-time-1g0x6r85b9.pdf

A framework for GPU-accelerated exploration of massive time-varying rectilinear scalar volumes

Quality assessment of volume compression approaches using isovalue clustering

We present a parallel system capable of rendering multi-gigabyte data sets on a multi-megapixel display wall at interactive rates. The system is based on Residual Vector Quantization which allows us to render extremely large data sets out of the graphics memory. At 0.75 bits per voxel, such large data sets can even be kept on a consumerlevel graphics card. As an example we compress the whole full color “Visible Human Female” data set, approximately 21GByte in size, down to 700MByte. Taking advantage of the fixed code length and the extremely simple decompression scheme of RVQ, all decompression is done on the GPU at very high rates. For each frame the data set is decompressed into small subvolumes which are rendered front to back. Classification and shading can be moved into the decompression step, speeding up the rendering pass.

/pdf/giga-voxel-rendering-from-compressed-data-on-a-display-wall-2ymaykyztn.pdf

Giga-voxel rendering from compressed data on a display wall

We propose a new method for finding initial codevectors for vector quantization. It is based on Principal Component Analysis and uses error-directed subdivision of the eigenspace in reduced dimensionality. Additionally, however, we include shape-directed split decisions based on eigenvalue ratios to improve the visual appearance. The method achieves about the same image quality as the well-known k-means++ method, while providing some global control over compression priorities.

PCA-based Seeding for Improved Vector Quantization.

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Giga-voxel rendering from compressed data on a display wall

PCA-based Seeding for Improved Vector Quantization.