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Efficient Compression Techniques for Large-scale Volume Rendering

Explore novel approaches, such as Hierarchical Vector Quantization and PCA-Split, for rendering large-scale multi-dimensional data sets with high speeds and quality output. Includes GPU-based decoding and rendering methods for interactive framerates.

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Efficient Compression Techniques for Large-scale Volume Rendering

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  1. Jens Schneider Rüdiger Westermann Technical University Munich Compression Domain Volume Rendering

  2. Motivation Need to deal with data of increasing size: • Large-scale • Multi-dimensional • Multi-parameter Increasing problems: • Compression • Representation • Rendering We will adress all three problems!

  3. Talk Outline The Approach – Vector Quantization Contributions Quality and speed • Hierachical encoding • PCA-Split • Progressive encoding of time-resolved data Multi-dimensional data • Vectors of arbitrary length Rendering from compressed data • GPU-based decoding and rendering • Per-fragment evaluation • Interactive framerates

  4. Talk Outline The Application – Volume Rendering • Large-scale volumetric data sets • Time-varying sequences 1.4 GB / 20 fps 16 MB / 14 fps 0.78 MB / 11 fps 70 MB / 24 fps

  5. Input mapping Encoder in=E(Xn) Xn Codebook C with codewords Decoder in X‘n=C(in) Output mapping Vector Quantization

  6. Vector Quantization LBG-Algorithm • Linde, Buzo and Gray 1980 • Iterative refinement of a previous Codebook • Sensitive to quality of first Codebook • Usually computationally expensive Speed-Up possible (and necessary) • Partial searches • Fast searches • Better initial Codebook (i.e. PCA-Splits) LBG-Algorithm can be fast!

  7. Vector Quantization The PCA-Split • Lensch et.al. 2001 – BRDF Compression • Covariance analysis to find optimal splitting plane • Cut a cluster of input vectors in two by this plane. • Plane is given by centroid of current set and largest Eigenvector (= normal) of the Auto-Covariance Matrix

  8. Vector Quantization LBG as PCA post-processing • Increases fidelity • Leads to stable Voronoi-Regions • Only a few steps are necessary • Great speed-up compared to LBG only! A series of LBG steps, codebook from last slide

  9. 32D vectors, 1MB 4D vectors, 2MB Original, 32MB Example Full-color confocal microscopy scan, 5122x32xRGB

  10. Hierarchical Vector Quantization Laplace Decomposition

  11. 43 dim. VQ 23 dim. VQ Direct Copy Hierarchical Vector Quantization

  12. Hierarchical Vector Quantization Output: • One RGB Index-Volume • Two Codebooks RGB Index-Volume  3D Texture Codebooks  2D -Textures

  13. Example Visible Human (Male), RGB slice 2048x1216 Compression took 10.0 seconds, PSNR = 34.72dB Original (7.1MB) Compressed (285KB)

  14. Timings Reference System: P4 2.8GHz, 1GB memory VHP Slice, 2048x1216 RGB 10.0 sec Engine 2562x128 CT-Scan 19.0 sec Skull 2563 CT-Scan 50.6 sec Vortex Sequence, 1283x100 13 (5) min Shockwave Sequence, 2563x89 29 (13) min

  15. Decoding process in flatland Rendering GPU-based decoding • Indices stored in 3D RGB-texture (3/64th original size) • Decode index per block  dependent fetch • Decode adress per block 43 adress texture

  16. Rendering Render 3D index and adress texture • Nearest neighbor interpolation for both • GL_REPEAT for adress texture Per-fragment decoding • Decode detail components and dependent fetch • Add the details to average component (Red channel) • Lookup result in 1D RGB transfer function Problem: Complex fragment shader slows down rendering

  17. Rendering Solution:Deferred Fragment Processing Avoid decoding in empty regions. „Empty“ means: a) -Transfer function maps 0  0. • Check on CPU • Switch between two possible rendering modes b) Average value is 0 (Red channel) • Check in a first, simple fragment program • Fragment‘s depth value is set accordingly • Second pass: discard (early Z-Test) or render fragment • Full decoding only performed in second pass

  18. 2562x128 Engine CT Scan 19.0 seconds, PSNR = 36.17dB (P4 2.8GHz) Compressed (402KB) – 12 fps Original (8MB) – 19 fps

  19. 2563 Skull CT Scan 50.6 seconds, PSNR = 35.35dB (P4 2.8GHz) Original (16MB) – 14 fps Compressed (780KB) – 11 fps

  20. Time-resolved Sequences Exploit temporal coherences during compression: • Group of Frames (GOF) First frame in a GOF: • PCA-Split followed by LBG-Refinement Other frames: • LBG-refinement of last Index-Volume and Codebook Result: • Great speed-up (factor 2 to 3) • Very large GOFs possible (64+ frames) • Virtually same fidelity as frame-by-frame

  21. 1283x100 Vortex-Simulation 5 minutes, PSNR = 34.43dB (P4 2.8 GHz) Original (200MB) - 28 fps Compressed (11MB) - 16 fps

  22. 2563x89 Shockwave-Sequence 13 minutes, PSNR = 51.36dB (P4 2.8 GHz) Original (1.4GB) - 20 fps Compressed (70MB) - 24 fps

  23. Conclusions • Compression ratios of approx. 20:1 • Interactive rendering possible • Easy random access to each frame • Wide variety of data sets handled • Currently only nearest neighbor interpolation • Mainly limited by performance / instruction count. • Tri-linear interpolation can be done on newer GPUs!

  24. Online Demo Demo-machine with ATi 9800 Pro kindly provided by ATi

  25. Questions ? Thank You!

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