Date: 14 August 2023
Time: 2:00 PM ET (New York Time)
Speaker(s): Dr. Hossein Talebi, Dr. Peyman Milanfar
Original Article (Open Access Free to Download)
Automatically learned quality assessment for images has recently become a hot topic due to its usefulness in a wide variety of applications, such as evaluating image capture pipelines, storage techniques, and sharing media. Despite the subjective nature of this problem, most existing methods either (1) only predict the mean opinion score provided by data sets, such as AVA, or (2) learn from pairwise comparisons that do not take into account the global ranking of images. In this talk we describe an approach that predicts the distribution of human opinion scores using a convolutional neural network. We show that this approach has the advantage of being significantly simpler than other methods with comparable performance. Then, we address the shortcomings of learning from pairwise comparisons by regularizing the pairwise empirical probabilities with aggregated rankwise probabilities. Our resulting models can be used to score images reliably with high correlation to human perception. Additionally, it can also assist with adaptation and optimization of photo editing/enhancement algorithms in a photographic pipeline. All this is done without a need for a “golden” reference image, consequently allowing for a single-image, semantic- and perceptually-aware, no-reference quality assessment.
Hossein Talebi received the B.S. and M.S. degrees in electrical engineering from the Isfahan University of Technology, Iran, and the Ph.D. degree in electrical engineering from the University of California at Santa Cruz, Santa Cruz, CA, USA.
He is currently a senior staff software engineer at Google Research, Mountain View, CA since 2015.
Dr. Talebi’s research interests are computational photography, image processing, inverse imaging problems, image quality assessment and machine learning.
Peyman Milanfar received his undergraduate education in electrical engineering and mathematics from the University of California, Berkeley, and the M.Sc. and Ph.D. degrees in electrical engineering from the Massachusetts Institute of Technology.
He is a Distinguished Scientist / Senior Director at Google Research, where he leads the Computational Imaging team. Prior to this, he was a Professor of Electrical Engineering at UC Santa Cruz from 1999-2014. He was an Associate Dean for Research at the School of Engineering from 2010-12.
Dr. Milanfar has been keynote speaker at numerous technical conferences including Picture Coding Symposium (PCS), SIAM Imaging Sciences, SPIE, and the International Conference on Multimedia (ICME). Along with his students, he has won several best paper awards from the IEEE Signal Processing Society. He was a Distinguished Lecturer of the IEEE Signal Processing Society, and is a Fellow of the IEEE "for contributions to inverse problems and super-resolution in imaging."