IEM Seminar Series: Designing Application-dependent Graph Fourier Transforms
Event details
| Date | 04.06.2024 |
| Hour | 16:00 › 17:00 |
| Speaker | Prof. Antonio Ortega, University of Southern California |
| Location | |
| Category | Conferences - Seminars |
| Event Language | English |
Abstract
Defining elementary frequency modes is key to graph signal processing (GSP). In conventional signal processing, Fourier transforms provide representations with well-understood properties (oscillatory behavior, time-frequency localization) for all scenarios of interest. Instead, in GSP, standard definitions of frequency derived from graph spectra have very different behaviors depending on the graph (e.g., regular vs irregular) or the normalization choice. Moreover, standard frequencies may be difficult to obtain for certain directed graphs and may have limited interpretability. We present new definitions of GFTs that can address these concerns. For undirected graphs, our new designs are based on letting the choice of the inner product be a function of the application and/or graph. We demonstrate their application for perceptual image coding and graph filterbank design. For directed graphs, we show that the spectral decomposition of the adjacency matrix leads to interpretable frequency mode definitions.
Joint work with Phil A. Chou, Samuel Fernández Menduiña, Benjamin Girault, Semin Kwak, Eduardo Pavez and Laura Shimabukuro
Biography
Antonio Ortega is Dean's Professor of Electrical and Computer Engineering at the University of Southern California (USC). He received his undergraduate and doctoral degrees from Universidad Politecnica de Madrid, Madrid, Spain, and Columbia University, New York, NY. He is a Fellow of the IEEE and EURASIP and he currently serves as the VP for Publications of the IEEE Signal Processing Society. He has received several paper awards, including the 2016 Signal Processing Magazine award. His recent research focuses on graph signal processing, machine learning, and multimedia compression. He is the author of the book, "Introduction to Graph Signal Processing," published by Cambridge University Press in 2022.
Defining elementary frequency modes is key to graph signal processing (GSP). In conventional signal processing, Fourier transforms provide representations with well-understood properties (oscillatory behavior, time-frequency localization) for all scenarios of interest. Instead, in GSP, standard definitions of frequency derived from graph spectra have very different behaviors depending on the graph (e.g., regular vs irregular) or the normalization choice. Moreover, standard frequencies may be difficult to obtain for certain directed graphs and may have limited interpretability. We present new definitions of GFTs that can address these concerns. For undirected graphs, our new designs are based on letting the choice of the inner product be a function of the application and/or graph. We demonstrate their application for perceptual image coding and graph filterbank design. For directed graphs, we show that the spectral decomposition of the adjacency matrix leads to interpretable frequency mode definitions.
Joint work with Phil A. Chou, Samuel Fernández Menduiña, Benjamin Girault, Semin Kwak, Eduardo Pavez and Laura Shimabukuro
Biography
Antonio Ortega is Dean's Professor of Electrical and Computer Engineering at the University of Southern California (USC). He received his undergraduate and doctoral degrees from Universidad Politecnica de Madrid, Madrid, Spain, and Columbia University, New York, NY. He is a Fellow of the IEEE and EURASIP and he currently serves as the VP for Publications of the IEEE Signal Processing Society. He has received several paper awards, including the 2016 Signal Processing Magazine award. His recent research focuses on graph signal processing, machine learning, and multimedia compression. He is the author of the book, "Introduction to Graph Signal Processing," published by Cambridge University Press in 2022.
Practical information
- General public
- Free
Contact
- Anne De Witte