IEEE SPS SCV – Energy-Efficient Neural Image and Video Compression

This talk addresses modern approaches to image and video compression through the lens of energy-efficient hardware design. Traditional codecs like JPEG and H.264 are increasingly being challenged by learned compression techniques based on deep neural networks, particularly autoencoders. While these methods offer state-of-the-art performance in rate-distortion trade-offs, their deployment in real-world systems depends critically on efficient circuit and architectural design. We will explore the structure and training of neural compression models, including variational autoencoders and entropy bottlenecks, followed by the challenges of implementing these models in energy- and area-constrained environments such as mobile devices, cameras, and edge computing systems.
Speaker(s): Mateus Grellert
Virtual: https://events.vtools.ieee.org/m/487438