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Dual Representations for Light Field Compression

Dual Representations for Light Field Compression. EE368C Project January 30, 2001 Peter Chou Prashant Ramanathan. Outline. Background Model-based Coding Surface Light Fields Trade-offs Duality Proposed Experiments. Light Fields and Compression. What are light fields?

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Dual Representations for Light Field Compression

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  1. Dual Representations for Light Field Compression EE368C Project January 30, 2001 Peter Chou Prashant Ramanathan

  2. Outline • Background • Model-based Coding • Surface Light Fields • Trade-offs • Duality • Proposed Experiments

  3. Light Fields and Compression • What are light fields? • 2-D array of images • Why is compression necessary? • Light fields are very large data sets Mouse Hemispherical Light Field University of Erlangen Michelangelo’s Night 96 GB raw image data Stanford Computer Graphics Laboratory

  4. Light Fields with Geometry • Geometry used for light fields to aid compression • ex. model-based coding • Light fields are used with geometry for more realistic rendering • ex. surface light fields

  5. Model-based Coding • Model-based Coding of Multi-Viewpoint Imagery (Magnor and Girod, VCIP-2000) • Eigen-Texture Method: Appearance Compression based on 3D Model (Nishino, Sato, and Ikeuchi, CVPR-1999) http://www.lnt.de/~magnor

  6. Surface Light Fields • Surface Light Fields for 3D Photography (Wood et al., Siggraph 2000) http://grail.cs.washington.edu/projects/slf/

  7. Surface Light Fields (cont’d) • Geometry acquired through range scan • For each point on surface, a lumisphere represents radiance in all directions • Lumispheres are coded using either: • function quantization (similar to VQ) • principal function analysis (similar to PCA)

  8. Trade-offs • Textures + coherency along 4D coordinate directions – warping introduces artifacts, and possible loss of information • Surface Light Fields + more intuitive representation for compression – lumispheres are represented as continuous functions

  9. Duality • View-dominant organization (textures) • Geometry-dominant organization (surface light fields)  Surface Points  View 1 View 2 View N  Views  Surface Point 1 Surface Point 2 Surface Point N

  10. Proposed Experiments I • Compare the two organizations for any difference in compression results  Surface Points  View 1 View 2 View N  Views  Surface Point 1 Surface Point 2 Surface Point N

  11. Proposed Experiments II • Reparameterize geometry-dominant organization using local coordinate system w.r.t. surface normals  Views  Surface Point 1 Surface Point 2 Normal Direction View Surface Point N

  12. Proposed Experiments III • Use image data directly, instead of converting from warped texture data  Views  Surface Point 1 Surface Point 2 image pixels Surface Point N

  13. Workplan

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