Keynote Lectures
Available Soon
Alain Tremeau, Université Jean Monnet in Saint Etienne, France
JPEG AI: The First International Standard for Image Coding Based on an End-to-End Learning-Based Approach
João Ascenso, Instituto Superior Técnico, Portugal
Available Soon
Valerio Giuffrida, University of Nottingham, United Kingdom
Keynote Lecture
Alain Tremeau
Université Jean Monnet in Saint Etienne
France
Brief Bio
Alain Trémeau is Professor at University Jean Monnet and Vice-Rector for International Relations. He is member of the Laboratoire Hubert Curien (UMR 5516). His research activity covers several topics and applications related to color imaging, color science and computer vision. He published numerous book chapters and articles in the field of color science and recently in the domain of cultural heritage. He coordinates two international master degrees in the fields of color science and computer vision (COSI and 3DMT).
JPEG AI: The First International Standard for Image Coding Based on an End-to-End Learning-Based Approach
João Ascenso
Instituto Superior Técnico
Portugal
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Brief Bio
João Ascenso is a professor in the Department of Electrical and Computer Engineering at Instituto Superior Técnico and a member of the Multimedia Signal Processing Group at Instituto de Telecomunicações in Lisbon, Portugal. He earned his E.E., M.Sc., and Ph.D. degrees in Electrical and Computer Engineering from Instituto Superior Técnico in 1999, 2003, and 2010, respectively. He currently serves as the chair of the JPEG CPM (Coding and Performance for Machines) subgroup and the JPEG AI ad-hoc group, where he leads efforts focused on evaluating and developing event-based and learning-based image solutions. With over 150 publications in international journals and conferences, he has accumulated more than 5,000 citations and an h-index of 33. João Ascenso has served as an associate editor for IEEE Transactions on Image Processing, IEEE Signal Processing Letters, and IEEE Transactions on Multimedia. He was the Technical Program Chair for PCS2022 and EUVIP2022 and has contributed to the organizing committees of prominent international conferences, including IEEE ICIP 2023, IEEE ICME 2020, IEEE MMSP 2020, and IEEE ISM 2020. He has received three Best Paper Awards. His research interests include visual coding, quality assessment, 3D visual representation processing, machine coding, super-resolution, and denoising, among others.
Abstract
The JPEG Standardization Committee has recently standardized the JPEG AI standard, marking the introduction of the first image coding specification that utilizes an end-to-end learning-based method. By harnessing cutting-edge deep learning techniques, JPEG AI is designed with future practical applications in mind. The standard has undergone multiple refinements to ensure it is both mature and viable for image encoding and decoding, particularly on mobile devices. When compared to traditional coding systems, JPEG AI offers several distinct advantages: 1) improved rate-distortion performance that enhances perceptual visual quality; 2) considerably faster encoding speeds; and 3) the ability to support diverse optimization goals, such as coding for both human and machine use cases. Built on a learning-based image coding algorithm, JPEG AI generates a compact, single-stream compressed representation that boosts compression efficiency for human visualization, while also delivering strong performance for image processing and computer vision tasks. The goal is to provide a royalty-free baseline for the technology. This talk delves into the core technical principles behind the design of JPEG AI version 1 and presents an outlook on future advancements and extensions of the standard.
Keynote Lecture
Valerio Giuffrida
University of Nottingham
United Kingdom
Brief Bio
Not Available