Rapid High Quality Compression of Volume Data for Visualization

Nguyen, Ky Giang; Saupe, Dietmar

doi:10.1111/1467-8659.00497

Cited by 57 publications

(29 citation statements)

References 16 publications

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“…The remaining coefficients are typically further compressed using an entropy coding scheme such as Huffman or arithmetic coding. The most commonly used transforms are the discrete cosine transform (DCT) and the discrete wavelet transform (DWT), both of which have been applied to volume rendering [28,45], [15,16,33,43]. Woodring et al [44] analyze the application of JPEG 2000 compression to a large climate simulation.…”

Section: Related Workmentioning

confidence: 99%

“…Lossy compression schemes based on the discrete wavelet transform, in combination with coefficient quantization and entropy coding, are well known to achieve very high compression rates at high fidelity [38]. Compression schemes based on transform coding also have a long tradition in visualization, for instance to reduce memory and bandwidth limitations in volume visualization [16,33,43,45]. However, only with the possibility to perform the entire compression pipeline on the GPU [39]-including encoding and decoding-can the full potential of wavelet-based compression be employed for large data visualization.…”

Section: Lossy Compressionmentioning

confidence: 99%

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Turbulence Visualization at the Terascale on Desktop PCs

Treib

Burger

Reichl

et al. 2012

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. Visualizations of structures in 1024 3 turbulence data sets on 1024 × 1024 viewports, directly from the turbulent motion field. Left: Close-up of iso-surfaces of the ∆ Chong invariant with direct volume rendering of vorticity direction inside the vortex tubes. Middle: Direct volume rendering of color-coded vorticity direction. Right: Close-up of direct volume rendering of R S . The visualizations are generated by our system in less than 5 seconds on a desktop PC equipped with 12 GB of main memory and an NVIDIA GeForce GTX 580 graphics card with 1.5 GB of video memory.Abstract-Despite the ongoing efforts in turbulence research, the universal properties of the turbulence small-scale structure and the relationships between small-and large-scale turbulent motions are not yet fully understood. The visually guided exploration of turbulence features, including the interactive selection and simultaneous visualization of multiple features, can further progress our understanding of turbulence. Accomplishing this task for flow fields in which the full turbulence spectrum is well resolved is challenging on desktop computers. This is due to the extreme resolution of such fields, requiring memory and bandwidth capacities going beyond what is currently available. To overcome these limitations, we present a GPU system for feature-based turbulence visualization that works on a compressed flow field representation. We use a wavelet-based compression scheme including run-length and entropy encoding, which can be decoded on the GPU and embedded into brick-based volume ray-casting. This enables a drastic reduction of the data to be streamed from disk to GPU memory. Our system derives turbulence properties directly from the velocity gradient tensor, and it either renders these properties in turn or generates and renders scalar feature volumes. The quality and efficiency of the system is demonstrated in the visualization of two unsteady turbulence simulations, each comprising a spatio-temporal resolution of 1024 4 . On a desktop computer, the system can visualize each time step in 5 seconds, and it achieves about three times this rate for the visualization of a scalar feature volume.

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Section: Related Workmentioning

confidence: 99%

Section: Lossy Compressionmentioning

confidence: 99%

Turbulence Visualization at the Terascale on Desktop PCs

Treib

Burger

Reichl

et al. 2012

IEEE Trans. Visual. Comput. Graphics

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“…In this paper we refer to the polygon meshes produced by these and related algorithms as isosurface meshes. Despite the widespread use of these meshes in scientific visualization and medical applications, and their very large size, special purpose algorithms to compress them for efficient storage and fast download have not been proposed until very recently [24,18,38,20,37]. We compare our new algorithm with these recent approaches in section 8, after the relevant concepts are introduced.…”

Section: Introductionmentioning

confidence: 99%

BLIC: Bi-Level Isosurface Compression

Taubin¹

IEEE Visualization, 2002. VIS 2002.

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A B C Figure 1: Compressed Cuberille isosurfaces as rendered by our simple Java decoder. A: spine set, 91×512×512 voxels, level 1500, 381,278 faces, 381,667 vertices, compressed to 0.6182 bits per face. B: UNC CThead data set, 113×256×256 voxels , level 600, 294,524 faces, 294,018 vertices, compressed to 0.7437 bits per face. C: UNC CThead data set, level 1160, 312,488 faces, 312,287 vertices, compressed to 0.8081 bits per face. ABSTRACTIn this paper we introduce a new and simple algorithm to compress isosurface data. This is the data extracted by isosurface algorithms from scalar functions defined on volume grids, and used to generate polygon meshes or alternative representations. In this algorithm the mesh connectivity and a substantial proportion of the geometric information are encoded to a fraction of a bit per Marching Cubes vertex with a context based arithmetic coder closely related to the JBIG binary image compression standard. The remaining optional geometric information that specifies the location of each Marching Cubes vertex more precisely along its supporting intersecting grid edge, is efficiently encoded in scan-order with the same mechanism. Vertex normals can optionally be computed as normalized gradient vectors by the encoder and included in the bitstream after quantization and entropy encoding, or computed by the decoder in a postprocessing smoothing step. These choices are determined by trade-offs associated with an in-core vs. out-of-core decoder structure. The main features of our algorithm are its extreme simplicity and high compression rates

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“…Expression methods based on the volume are commonly carried out by octree model, the literature [6][7][8][9] take split on the three-dimensional volume data based on octree structure then do wavelet transformation and further develop the various transformations compression. The 3D spatial data model *Address correspondence to this author at the Department of Computer and Information Science, Hunan Institute of Technology, Hengyang 421002, China; Tel: 13973454583; E-mail: dcq7777@163.com used in this paper is based on the octree model.…”

Section: Introductionmentioning

confidence: 99%

Compression Algorithm of 3D Point Cloud Data Based on Octree

Cheng-qiu¹,

Chen²,

Fang³

2015

TOAUTOCJ

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Abstract:Octree is a tree data structure which is used to describe three-dimensional space such as three-dimensional modeling, 3D collision detection, and 3D data compression and so on. This paper focuses on compression algorithm of 3D point cloud data based on the octree. Firstly, the coordinates of 3D point cloud data converted to Morton without distortion were discussed. As the Morton is too discrete, an optimization algorithm for the Morton is proposed by IT on this basis. Finally, this paper proposed a program which is attached to 1-byte-status bit to represent the Morton who had the same parent. The program can improve the result of compression

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Rapid High Quality Compression of Volume Data for Visualization

Cited by 57 publications

References 16 publications

Turbulence Visualization at the Terascale on Desktop PCs

Turbulence Visualization at the Terascale on Desktop PCs

BLIC: Bi-Level Isosurface Compression

Compression Algorithm of 3D Point Cloud Data Based on Octree

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