Talk
When we look at an image, we instantly judge whether it is clear or distorted. Teaching computers to make the same judgment is the goal of Image Quality Assessment (IQA), a branch of data quality assessment. This is especially important because images can lose clarity at many stages-from capture and processing to storage and sharing. Automatic evaluation of image quality has a far-reaching impact, from improving photos and video streaming to ensuring accuracy in medical imaging, where reliable images are essential for diagnosis. Recent progress in IQA draws inspiration from how the human brain and eyes process visual information. By mimicking biological vision, computers can now “see” more like humans, going beyond older methods such as Mean Squared Error (MSE), which often fail to reflect real human perception.
This session introduces the latest advances in single-image “blind” IQA methods that assess image quality without a reference image. We will explore how bio-inspired algorithms are making medical imaging more accurate, trustworthy, and efficient-paving the way for a new era of human-centered, AI-driven visual technology
About the Speaker
Sos S. Agaian, PhD in Mathematics, Dr, Engineering Science, and is a Distinguished Professor of
Computer Science at College of Staten Island and the Graduate Center, City University of New York
(CUNY). Prior to joining CUNY, Dr. Agaian was the Peter T. Flawn Professor of Electrical and
Computer Engineering at the University of Texas at San Antonio; Graduate School of Biomedical
Sciences UTHSCSA at San Antonio; Professor, UT System Cyber and Cloud Security Initiative, and
Director of Multimedia and Mobile Signal Processing Laboratory. He has been a visiting faculty at
the Tufts University, the Tampere Institute of Technology, and the Leading Scientist at the AWARE,
Inc. at Bedford, MA.
Dr. Agaian received his M.S. in Mathematics and Mechanics (summa cum laude) from the Yerevan State
University, Armenia; his Ph.D. in Mathematics and Physics from the Steklov Institute of Mathematics,
Russian Academy of Sciences (RAS); and his Doctor of Engineering Sciences degree from the Institute
of Control Systems, RAS.