1 / 10

QUASID: Quantifying 3D Image Perception in VR

Ferdi Smit Center for Mathematics and Computer Science (CWI) Amsterdam. QUASID: Quantifying 3D Image Perception in VR. Why quantify image perception?. QUASID: Quantify Interaction Evaluate/improve 3D input-devices Hypothesis: Improved perceived 3D images  Improved 3D interaction

tale
Download Presentation

QUASID: Quantifying 3D Image Perception in VR

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Ferdi Smit Center for Mathematics and Computer Science (CWI) Amsterdam QUASID:Quantifying 3D Image Perceptionin VR

  2. Why quantify image perception? • QUASID: Quantify Interaction • Evaluate/improve 3D input-devices • Hypothesis: • Improved perceived 3D images  Improved 3D interaction • Challenge: • Subjective quality of 3D images/animations • How to measure/quantify?

  3. Example: Crosstalk Reduction • Left: Crosstalk ghost image in 3D stereo-viewing • Right: Crosstalk reduction algorithm applied • Which is better? How much better? • [Non-uniform Crosstalk Reduction for Dynamic Scenes, IEEE-VR 2007]

  4. Crosstalk Evaluation: Data • Photographs of the display • Through the shutter glasses • What the user sees • Compare photographs to evaluate differences • Per-pixel comparison (RMSE) useless

  5. Crosstalk Evaluation: VDP • Need perceptual comparison • Existing software: Visible Differences Predictor[Daly90] • Measure percentage of perceptually different pixels • How different is different? • Compare different pixels in perceptual color space • [Three Extensions to Subtractive Crosstalk Reduction, EGVE 2007 (to appear)]

  6. Example: Effects of Motion • Motion can cause ‘judder’: • Visible double-image artifacts • Perceived jerky motion • Extrapolate motion field • Results in smooth perceived motion • Eliminates perceptual artifacts • [The Design and Implementation of a VR-Architecture for Smooth Motion, submitted to VRST 2007]

  7. Example: Effects of Motion • Judder reduction: • Increased feeling of system responsiveness • Lower perceived latency • Better interaction? • How to measure improvement? • Only still image comparisons • Need to quantify temporal perception! How?

  8. Open Questions • We can now determine: • which algorithm is perceptually better … • … and by how much • However: • What about animations and temporal perception? • How is interaction influenced by better images?

  9. Current Status • Crosstalk papers published: • Non-Uniform Crosstalk Reduction for Dynamic Scenes, IEEE-VR 2007 • Three Extensions to Subtractive Crosstalk Reduction, EGVE 2007 (to appear) • Other Work: • GraphTracker: A Topology Projection Invariant Optical Tracker, EGVE 2006, and Computers and Graphics Feb. 2007 • The Design and Implementation of a VR-Architecture for Smooth Motion, Submitted to VRST 2007

  10. The End • Thank you for your attention. • Questions / Suggestions ?

More Related