| Session: | Machine Learning for Image and Video Coding |
| Location: | Lecture Room |
| Session Time: | Monday, June 25, 10:20 - 12:40 |
| Presentation Time: | Monday, June 25, 12:00 - 12:20 |
| Presentation: |
Special Session Lecture
|
| Paper Title: |
PROGRESSIVE MODELING OF STEERED MIXTURE-OF-EXPERTS FOR LIGHT FIELD VIDEO APPROXIMATION |
| Authors: |
Ruben Verhack; Ghent University - imec, IDLab / TU Berlin - Communication Systems Lab, Belgium | | |
| | Glenn Van Wallendael; Ghent University - imec, IDLab, Belgium | | |
| | Martijn Courteaux; Ghent University - imec, IDLab, Belgium | | |
| | Peter Lambert; Ghent University - imec, IDLab, Belgium | | |
| | Thomas Sikora; Technische Universität Berlin, Germany | | |
| Abstract: |
Steered Mixture-of-Experts (SMoE) is a novel framework for the approximation, coding, and description of image modalities. The future goal is to arrive at a representation for Six Degrees-of-Freedom (6DoF) image data. The goal of this paper is to introduce SMoE for 4D light field videos by including the temporal dimension. However, these videos contain vast amounts of samples due to the large number of views per frame. Previous work on static light field images mitigated the problem by hard subdividing the modeling problem. However, such a hard subdivision introduces visually disturbing block artifacts on moving objects in dynamic image data. We propose a novel modeling method that does not result in block artifacts while minimizing the computational complexity and which allows for a varying spread of kernels in the spatio-temporal domain. Experiments validate that we can progressively model light field videos with increasing objective quality up to 0.97 SSIM. |