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LOD%20Map%20

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LOD Map A Visual Interface for Navigating Multiresolution Volume Visualization ... 'Overview first, zoom and filter, and then details-on-demand' [Shneiderman 1992] 4 ... – PowerPoint PPT presentation

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Title: LOD%20Map%20


1
LOD Map A Visual Interface for Navigating
Multiresolution Volume Visualization
  • Chaoli Wang and Han-Wei Shen
  • The Ohio State University
  • Presented at
  • IEEE Visualization 2006

2
Large Data Sets
  • The Visible Woman
  • 512 512 1728
  • Short integer (16 bits)
  • 864MB
  • Richtmyer-Meshkov Instability (RMI)
  • 2048 2048 1920
  • Byte integer (8 bits)
  • 7.5 GB per time step, 2TB in total

3
Motivation
  • Large data size makes interactive visualization
    difficult
  • High main / texture memory requirement
  • Slower rendering speed
  • Multiresolution volume visualization
  • Adaptive data exploration
  • Overview first, zoom and filter, and then
    details-on-demand Shneiderman 1992

4
Multiresolution Data Representation
  • The wavelet tree Guthe et al. 2002
  • Octree-based space partition
  • Block-wise wavelet transform and compression
  • Error metric calculation

5
Research Questions
  • How to measure and compare the quality of
    different LOD selections?
  • Are the computing resources effectively
    distributed?
  • Can we visualize what are being selected and make
    changes?

6
Our Approach
  • LOD entropy LOD quality index
  • Employ information theory
  • Measure information contained in the LOD
  • LOD map visual representation of LOD quality
  • A single number vs. a visual interface
  • Immediate suggestions for LOD improvement
  • Interactive techniques for LOD adjustment

7
Shannon Entropy
  • The source takes a sequence of finite symbols
    a1, a2, a3, , aM with probabilities p1, p2,
    p3, , pM
  • The amount of information contained is defined as
  • The entropy function is maximized when pi are all
    equal

An example of 3D probability vector p1, p2, p3
Bordoloi and Shen 2005
8
Probability Definition
  • Entropy
  • where
  • Ci contribution of data block i to the image
  • Di distortion of data block i with its child
    blocks
  • M total number of data blocks in the hierarchy
  • A global quality index
  • Quality of rendered images
  • Probability distribution of all data blocks

equal probability! C? ? D? C? ? D?
9
Contribution
Contribution mean value
average thickness screen
projection area estimated
visibility
10
Distortion
mean value
standard deviation
covariance between bi and bj and small
constants Distortion
(a) covariance (b) luminance distortion (c)
contrast distortion
11
Treemap
  • A space-filling method to visualize hierarchical
    information Shneiderman et al. 1992
  • Recursive subdivision of a given display area
  • Information of each individual node
  • Color and size of its bounding rectangle

http//www.cs.umd.edu/hcil/treemap-history/
12
LOD Map
  • Treemap representation of a LOD
  • User interface for visual LOD selections
  • Observe individual blocks and make adjustments
  • Information mapping
  • Distortion D maps to the color of rectangle
  • Contribution C
  • maps to the size of rectangle
  • maps to its opacity

13
LOD Map A First Look
entropy 0.238
14
How Can LOD Map Help?
  • Balance probability distribution
  • Large rectangles with bright red colors
  • Highly-visible
  • High contribution, large distortion
  • Split to increase resolutions (C? ? D?)
  • Small blue rectangles
  • Low contribution, small distortion
  • Join to decrease resolutions (C? ? D?)
  • Dark rectangles
  • Lowest visibility
  • Join to decrease resolutions (C? ? D?)

15
Results LOD Comparison
MSE-based 67 blocks
entropy 0.163
level-based, 67 blocks
entropy 0.381
16
Results LOD Comparison
17
Results View Comparison
entropy 0.330
entropy 0.343
entropy 0.384
entropy 0.390
18
Results LOD Adjustment
entropy 0.192
entropy 0.386
entropy 0.251
entropy 0.414
before, 90 blocks
after, 90 blocks
before, 108 blocks
after, 108 blocks
19
Results Budget Control
before, 365 blocks, entropy 0.448
after, 274 blocks, entropy 0.476
20
Summary Future Work
  • Summary
  • LOD entropy quality measure
  • LOD map visual navigation interface
  • Effectiveness and efficiency
  • Future work
  • Time-critical rendering
  • Eye-tracking application
  • Time-varying data visualization

21
Acknowledgements
  • Data sets
  • National Library of Medicine
  • Lawrence Livermore National Laboratory
  • Funding agencies
  • National Science Foundation
  • Department of Energy
  • Oak Ridge National Laboratory
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