Linear video coding
Conventional and learning-based video compression — codecs, rate–distortion, and the boundary between hand-designed and neural pipelines.
Research
Our work cuts across multimedia signal processing and modern deep learning. Below are the active research themes — each one is a thread that connects multiple PhD theses, industrial collaborations, and publications.
Rich-media scene representation
Representing complex scenes with multiple media, layouts, and interactions — beyond a single video stream.
Multimedia content adaptation
Adapting multimedia content to networks, devices, and users — quality of experience under constraints.
Multimedia distribution networks
Transport, orchestration, and protocols for delivering multimedia at scale.
Frugal and efficient AI
Pruning, quantization, low-rank methods, and other tools to make deep models small enough to deploy.
Geometric deep learning
Learning on graphs, manifolds, and structured domains — where the geometry of the data shapes the architecture.
Multimodal learning
Joint models for vision, language, and beyond — alignment, fusion, and grounded reasoning.