Nankai University International E-Forum on Artificial Intelligence and Robotics

（第59期）

2025年南开大学人工智能与机器人国际学术讲坛

College of Artificial Intelligence, Nankai University

报告时间：2025年11月14日（周五）11:00-12:00

报告嘉宾：Sam Kwong

腾讯会议：860-413-775

报告题目：Overview of High Dynamic Video

报告摘要：

High Dynamic Range (HDR) video is a technology that significantly enhances the visual experience by expanding the range of contrast and color in video content. Unlike standard dynamic range (SDR) video, HDR allows for brighter highlights, deeper shadows, and a wider color gamut. This results in more realistic and vibrant images that closely mimic the way the human eye perceives the real world. In this segment, we will delve into the fundamental principles of HDR, exploring how it works, the technical standards behind it (such as HDR10, Dolby Vision, and HLG), and the benefits it brings to various types of content, from movies and TV shows to video games and live broadcasts In this talk, I will talk about the following:

HDR Image Reconstruction

High dynamic range (HDR) image reconstruction is a process that aims to create images with a greater range of luminance levels than what is achievable with standard digital imaging techniques. This allows for the capture of both very bright and very dark details in a scene, closely mimicking human vision. By combining multiple images taken at different exposure levels, HDR reconstruction techniques can produce visually stunning and highly detailed images that better represent the range of light present in real-world scenes.

EIN: Exposure Induced Network for Single Image HDR Reconstruction

The Exposure Induced Network (EIN) for Single Image HDR Reconstruction is a novel deep learning approach designed to generate HDR images from a single standard dynamic range (SDR) input. Unlike traditional methods that require multiple exposures, EIN leverages a neural network to predict and reconstruct HDR content by learning the relationships between different exposure levels. This enables the creation of high-quality HDR images even in situations where only a single exposure is available, making HDR imaging more practical and accessible for various applications.

LGFM: HDR Image Quality Assessment Based on Frequency Disparity

The Local and Global Frequency Modulation (LGFM) method for HDR image quality assessment is a sophisticated approach that evaluates the quality of HDR images based on the disparity in frequency components. By analysing both local and global frequency information, LGFM can more accurately reflect the human visual system's sensitivity to different types of artifacts and distortions in HDR content. This results in a more reliable and comprehensive assessment of HDR image quality.

报告人简介：

Professor KWONG Sam Tak Wu is the Associate Vice-President (Strategic Research), J.K. Lee Chair Professor of Computational Intelligence, the Dean of the School of Graduate Studies, and the Acting Dean of the School of Data Science of Lingnan University. Professor Kwong is a distinguished scholar in evolutionary computation, artificial intelligence (AI) solutions, and image/video processing, with a strong record of scientific innovations and real-world impacts. Professor Kwong is one of the most highly cited researchers by Clarivate in 2022, 2023, and 2024. He has also been actively engaged in knowledge transfer between academia and industry. He was elevated to IEEE Fellow in 2014 for his contributions to optimization techniques in cybernetics and video coding. He was the President of the IEEE Systems, Man, and Cybernetics Society (SMCS) in 2021-22. He is a fellow of the US National Academy of Inventors (NAI), the Canadian Academy of Engineering, and the Hong Kong Academy of Engineering (HKAE). Professor Kwong has a prolific publication record with over 500 journal articles, and 160 conference papers with an h-index of 99 based on Google Scholar. He is currently the associate editor of several leading IEEE transaction journals.