Variable Length Coding for GPU-Based Direct Volume Rendering

2016 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON), 2016

In rendering, textures are usually consuming more graphics memory than the geometry. This is especially true when rendering regular sampled volume data as the geometry is a single box. In addition, volume rendering suffers from the curse of dimensionality. Every time the resolution doubles, the number of projected pixels is multiplied by four but the amount of data is multiplied by eight. Data compression is thus mandatory even with the increasing amount of memory available on today's GPUs. Existing compression schemes are either lossy or do not allow on-the-fly random access to the volume data while rendering. Both of these properties are, however, important for high quality direct volume rendering. In this paper, we propose a lossless compression and caching strategy that allows random access and decompression on the GPU using a compressed volume object.

2013

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, or 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 co...

Computer Graphics Forum, 2014

Figure 1: Frames from real-time rendering of animated supernova data set (432 3 × 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.

2006

Volume Rendering methods employing the GPU capabilities offer high performance on off-the-shelf hardware. In this article, we discuss the various bottlenecks found in the graphics hardware when performing GPU-based Volume Rendering. The specific properties of each bottleneck and the trade-offs between them are described. Further we present a novel strategy to balance the load on the identified bottlenecks, without compromising the image quality. Our strategy introduces a two-staged space-skipping, whereby the first stage applies bricking on a semi-regular grid, and the second stage uses octrees to reach a finer granularity. Additionally we apply early ray termination to the bricks. We demonstrate how the two stages address the individual bottlenecks, and how they can be tuned for a specific hardware pipeline. The described method takes into account that the rendered volume may exceed the available texture memory. Our approach further allows fast run-time changes of the transfer function.

The wide majority of current state-of-the-art compressed GPU volume renderers are based on block-transform coding, which is susceptible to blocking artifacts, particularly at low bit-rates. In this paper we address the problem for the first time, by introducing a specialized deferred filtering architecture working on block-compressed data and including a novel deblocking algorithm. The architecture efficiently performs high quality shading of massive datasets by closely coordinating visibility-and resolution-aware adaptive data loading with GPU-accelerated per-frame data decompression, deblocking, and rendering. A thorough evaluation including quantitative and qualitative measures demonstrates the performance of our approach on large static and dynamic datasets including a massive 512 4 turbulence simulation (256GB), which is aggressively compressed to less than 2 GB, so as to fully upload it on graphics board and to explore it in real-time during animation.

Proceedings Visualization 2000. VIS 2000 (Cat. No.00CH37145), 2000

Very large irregular-grid data sets are represented as tetrahedral mesh and may incur significant disk I/O access overhead in the rendering process. An effective way to alleviate the disk I/O overhead associated with rendering large tetrahedral mesh is to reduce the I/O bandwidth requirement through compression. Existing tetrahedral mesh compression algorithms focus only on compression efficiency and cannot be readily integrated into the mesh rendering process, and thus demand that a compressed tetrahedral mesh be decompressed before it can be rendered into a 2D image. This paper presents an integrated tetrahedral mesh compression and rendering algorithm called Gatun, which allows compressed tetrahedral meshes to be rendered incrementally as they are being decompressed, thus forming an efficient irregular grid rendering pipeline. Both compression and rendering algorithms in Gatun exploit the same local connectivity information among adjacent tetrahedra, and thus can be tightly integrated into a unified implementation framework. Our tetrahedral compression algorithm is specifically designed to facilitate the integration with irregular grid renderer without any compromise in compression efficiency. A unique performance advantage of Gatun is its ability to reduce the run-time memory footprint requirement by releasing memory allocated to tetrahedra as early as possible. As a result, Gatun is able to decrease rendering time by a factor of 2 for very large tetrahedral mesh whose size exceeds the amount of physical memory. At the same time, the smaller working set and better access locality of Gatun improve the rendering performance by up to 30%, even when the input tetrahedral mesh is entirely memory-resident.

IEEE Transactions on Visualization and Computer Graphics, 2013

We present a novel approach for GPU-based high-quality volume rendering of large out-of-core volume data. By focusing on the locations and costs of ray traversal, we are able to significantly reduce the rendering time over traditional algorithms. We store a volume in an octree (of bricks); in addition, every brick is further split into regular macrocells. Our solutions move the branch-intensive accelerating structure traversal out of the GPU raycasting loop and introduce an efficient empty-space culling method by rasterizing the proxy geometry of a view-dependent cut of the octree nodes. This rasterization pass can capture all of the bricks that the ray penetrates in a per-pixel list. Since the per-pixel list is captured in a front-to-back order, our raycasting pass needs only to cast rays inside the tighter ray segments. As a result, we achieve two levels of empty space skipping: the brick level and the macrocell level. During evaluation and testing, this technique achieved 2 to 4 times faster rendering speed than a current state-of-the-art algorithm across a variety of data sets.

Computers & Graphics, 2008

We present a point-cloud compression algorithm that allows fast parallel decompression on the GPU suitable for interactive applications. The algorithm is based on vector quantization of an atlas of height-fields that have been sampled over primitive shapes which approximate the geometry. We introduce novel vector quantization acceleration techniques to facilitate fast compression as well. We achieve bitrates of less than four bits per normal-equipped point. Our method enables hole-free level-of-detail point rendering. We also show that using only up to two bits per point, high-quality renderings can still be obtained if normals are estimated in image-space. Even lower bitrates are obtained for storage on disk if arithmetic coding is used.

Computer Graphics Forum, 2001

Volume data sets resulting from, e.g., computerized tomography (CT) or magnetic resonance (MR) imaging modalities require enormous storage capacity even at moderate resolution levels. Such large files may require compression for processing in CPU memory which, however, comes at the cost of decoding times and some loss in reconstruction quality with respect to the original data. For many typical volume visualization applications (rendering of volume slices, subvolumes of interest, or isosurfaces) only a part of the volume data needs to be decoded. Thus, efficient compression techniques are needed that provide random access and rapid decompression of arbitrary parts the volume data. We propose a technique which is block based and operates in the wavelet transformed domain. We report performance results which compare favorably with previously published methods yielding large reconstruction quality gains from about 6 to 12 dB in PSNR for a 512 3-volume extracted from the Visible Human data set. In terms of compression our algorithm compressed the data 6 times as much as the previous state-of-theart block based coder for a given PSNR quality.

2004

We describe a system for the texture-based direct volume visualization of large data sets on a PC cluster equipped with GPUs. The data is partitioned into volume bricks in object space, and the intermediate images are combined to a final picture in a sort-last approach. Hierarchical wavelet compression is applied to increase the effective size of volumes that can be handled. An adaptive rendering mechanism takes into account the viewing parameters and the properties of the data set to adjust the texture resolution and number of slices. We discuss the specific issues of this adaptive and hierarchical approach in the context of a distributed memory architecture and present solutions for these problems. Furthermore, our compositing scheme takes into account the footprints of volume bricks to minimize the costs for reading from framebuffer, network communication, and blending. A detailed performance analysis is provided and scaling characteristics of the parallel system are discussed. F...