#compression-algorithms #computer-vision #processing #signal #seminar

 

Abstract: This talk will delve into the evolution of visual data compression in recent years, spotlighting how compression techniques and AI models are integrated together. Traditionally, image and video codecs such as JPEG, HEVC, and AV1 are designed primarily for accurate pixel reconstruction. However, the advancement of AI technologies has begun transforming these frameworks to meet modern application demands. This talk will discuss such shift through three lenses: 

- Compression with AI: enhance the coding performance of compression algorithms with AI techniques. I will discuss the use of generative AI models, particularly variational autoencoders in lossy image compression, and their resemblance to traditional coding concepts such as transform coding, and wavelet transform.

- Compression for AI: design compression systems for AI-based recognition and processing (as opposed to human viewing only). I will showcase potential real-world applications of coding for machines and present several recent methods targeting mobile-cloud systems.
- Compression of AI: efficient compression of AI models to reduce computational costs. I will present pruning and quantization methods to address the challenges of compressing neural network-based codecs. 

 

Bio: Fengqing Maggie Zhu is an Associate Professor of the Elmore Family School of Electrical and Computer Engineering at Purdue University, West Lafayette, Indiana. Dr. Zhu received the B.S.E.E. (with highest distinction), M.S. and Ph.D. degrees in Electrical and Computer Engineering from Purdue University in 2004, 2006 and 2011, respectively. Prior to joining Purdue in 2015, she was a Staff Researcher at Futurewei Technologies, where she received a Certification of Recognition for Core Technology Contribution in 2012. She is the recipient of an NSF CISE Research Initiation Initiative (CRII) award, a Google Faculty Research Award, and an ESI and trainee poster award for the NIH Precision Nutrition workshop. Her group’s work on visual coding for machines has received the Best Algorithms Paper Award at the Winter Conference on Applications of Computer Vision (WACV) 2023 and the Best Paper Finalists at the Picture Coding Symposium (PCS) 2022. She is currently serving as the Vice Chair of the IEEE MMSP-TC (2025-2026) and an Elected Member of the IVMSP-TC (2025-2027). She is also an Associate Editor for the IEEE Transactions on Multimedia (2025-2027). She has served on the organizing and program committees of major conferences in her field and received recognition such as the Outstanding Area Chair for ICME 2021. Dr. Zhu is a senior member of the IEEE.