Convolutional neural networks (CNNs) have been shown to deliver standout performance on a wide variety of visual information processing applications. However, this rapidly developing technology has only recently been applied with systematic energy to the problem of picture-quality prediction, primarily because of limitations imposed by a lack of adequate ground-truth human subjective data. This situation has begun to change with the development of promising data-gathering methods that are driving new approaches to deep-learning-based perceptual picture-quality prediction. Here, we assay progress in this rapidly evolving field, focusing, in particular, on new ways to collect large quantities of ground-truth data and on recent CNN-based picture-quality prediction models that deliver excellent results in a large, real-world, picture-quality database.
Bibliographical noteFunding Information:
Jongyoo Kim (email@example.com) received his B.S. and M.S. degrees in electrical and electronic engineering from Yonsei University, Seoul, South Korea, in 2011 and 2013, respectively. He is currently working toward his Ph.D. degree in the Department of Electrical and Electronic Engineering, Yonsei University, South Korea. His research interests include two-dimensional (2-D)/three-dimensional (3-D) image and video processing based on the human visual system, quality assessment of 2-D/3-D image and video, 3-D computer vision, and deep learning. He was a recipient of the Global Ph.D. Fellowship by the National Research Foundation of Korea from 2011 to 2016. Hui Zeng (firstname.lastname@example.org) received his M.S. degree from the School of Information and Communication Engineering, Dalian University of Technology, China, in 2016. He is currently pursing his Ph.D. degree in the Department of Computing, The Hong Kong Polytechnic University, under the supervision of Prof. Lei Zhang. His research interests include computer vision, image and video processing, and deep learning. Deepti Ghadiyaram (email@example.com) received her Ph.D. degree from the Department of Computer Science at the University of Texas (UT) at Austin. Her research interests include image and video processing, computer vision, and machine learning. Her Ph.D. work focused on perceptual image and video quality assessment, particularly on building quality-prediction models for pictures and videos captured in the wild and understanding a viewer’s time-varying quality of experience while streaming videos. She was a recipient of the UT Austin’s Microelectronics and Computer Development Fellowship from 2013 to 2014 and the Graduate Student Fellowship from the Department of Computer Science from 2013 to 2016. She joined Facebook Research in September 2017. Sanghoon Lee (firstname.lastname@example.org) received the B.S. degree from Yonsei University, Seoul, South Korea, in 1989, the M.S.
© 1991-2012 IEEE.
All Science Journal Classification (ASJC) codes
- Signal Processing
- Electrical and Electronic Engineering
- Applied Mathematics