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Dual Representations for Light Field Compression

This project delves into innovative approaches for compressing light fields through dual representations, which balance texture and geometry. It examines the challenges posed by the large data sets of light fields, highlights the necessity for effective compression techniques, and discusses model-based coding strategies that improve visualization and rendering. The presentation outlines recent experiments proposed to evaluate the effectiveness of surface light fields against traditional viewpoint-dominant methods, aiming to enhance compression efficiency while maintaining image quality.

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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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