Ajman University, UAE
Evaluating Multimedia Content Quality in the Age of Generative AI
In an era marked by technological advancements, the automatic evaluation of multimedia content, spanning audio, images, and videos, has become integral to machine learning and computer vision-based multimedia systems. Despite the high correlation between current objective multimedia quality metrics and subjective scores, various challenges persist. These challenges encompass disparities in metric performance across datasets and distortions, handling multiple distortions, considerations of run-time performance, memory requirements, and application-specific metrics.
This presentation addresses the imperative of ensuring and enhancing multimedia content quality. We explore the confluence of multimedia content evaluation, advancements in AI/ML, and the transformative capabilities of Generative Artificial Intelligence (AI). The discussion delves into methodologies and frameworks employed to assess the quality of multimedia content across diverse platforms and formats.
We will start by first highlighting the contemporary challenges in evaluating multimedia content, considering factors such as visual aesthetics, perceptual quality, and user engagement. Emphasis will be placed on the dynamic nature of multimedia, where traditional evaluation methods may fall short in capturing the nuances of evolving content types.
The talk then shifts focus to the promising role of Generative AI tools in overcoming these challenges. We provide an overview of the latest advancements in generative models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), elucidating their applicability in both generating and evaluating high-quality multimedia content.
Lastly, the presentation concludes with a forward-looking perspective, addressing ethical considerations, potential pitfalls, and future directions in the symbiotic relationship between multimedia content evaluation and generative AI. The overarching goal is to furnish a roadmap for unlocking the potential of generative AI in the evaluation and enhancement of multimedia content, offering valuable insights for industry professionals, researchers, and enthusiasts.
Mohamed Deriche received his B.Sc. degree in electrical engineering from the National Polytechnic School, Algeria, and his Ph.D. degree in signal processing from the University of Minnesota in 1994. He worked at Queensland University of Technology, Australia, before joining King Fahd University of Petroleum and Minerals (KFUPM) in Dhahran, Saudi Arabia, where he led the signal processing group. He has published more than 300 papers in multimedia signal and image processing. In 2021, he joined Ajman University to promote the AIRC center and the new Masters in AI within the College of Eng and IT. He delivered numerous invited talks and chaired several conferences including GlobalSIP-MPSP, IEEE Gulf (GCC), Image Processing Tools and Applications, and TENCON (a Region 10 conference). He has supervised more than 50 M.Sc. and Ph.D. students and is the recipient of the IEEE Third Millennium Medal. He also received the Shauman Best Researcher Award, and both the Excellence in Research and Excellence in Teaching Awards while at KFUPM and at Ajman University. His research interests cover signal and image processing spanning from theory to models to diverse applications in multimedia, biomedical, seismics, to language processing.