| 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, 10:40 - 11:00 |
| Presentation: |
Special Session Lecture
|
| Paper Title: |
Efficient Nonlinear Transforms for Lossy Image Compression |
| Authors: |
Johannes Ballé; Google, United States | | |
| Abstract: |
We assess the performance of two techniques in the context of nonlinear transform coding with artificial neural networks, Sadam and GDN. Both techniques have been successfully used in state-of-the-art image compression methods, but their performance has not been individually assessed to this point. Together, the techniques stabilize the training procedure of nonlinear image transforms and increase their capacity to approximate the (unknown) rate-distortion optimal transform functions. Besides comparing their performance to established alternatives, we detail the implementation of both methods and provide open-source code along with the paper. |