Thrasyvoulos Pappas and Sheila S. Hemami
In the content of the course, we will examine objective criteria for the evaluation of image quality that are based on models of visual perception. Our primary emphasis will be on image fidelity, i.e., how close an image is to a given original or reference image, but we will also discuss no-reference and limited-reference metrics. Our main focus will be on image and video compression and transmission. We will consider realistic distortions that arise from compression and error concealment in transmission over lossy cannels. We will also examine both near-threshold perceptual metrics, which explicitly account for human visual system (HVS) sensitivity to noise by estimating thresholds above which the distortion is just-noticeable, and supra-threshold metrics, which attempt to quantify visible distortions encountered in high compression applications or when there are losses due to channel conditions. We will consider structural similarity metrics, which model perception implicitly by taking into account the fact that the HVS is adapted for extracting structural information from images, and are thus insensitive to distortions (such as spatial and intensity shifts, contrast and scale changes) that do not change the structure of an image. Finally, we will present a unified framework for perceptual and structural similarity metrics.
Image and video compression specialists who wish to gain an understanding of how performance can be quantified; engineers and scientists who wish to learn about objective image and video quality evaluation; managers who wish to gain a solid overview of image and video quality evaluation; students who wish to pursue a career in digital image processing; intellectual property and patent attorneys who wish to gain a more fundamental understanding of quality metrics and the underlying technologies; and government laboratory personnel who work in imaging.
The instructors will assume a basic understanding of image compression algorithms and a background in digital signal processing and basic statistics: frequency-based representations, filtering, and distributions.
Thrasyvoulos Pappas received the SB, SM, and PhD degrees in electrical engineering and computer science from MIT (1979, 1982, and 1987). From 1987 until 1999, he was a member of the Technical Staff at Bell Laboratories, Murray Hill, NJ. In 1999, he joined the Department of Electrical and Computer Engineering at Northwestern University as an associate professor. His research interests are in image and video quality and compression, perceptual models for image processing, model-based halftoning, image and video analysis, and multimedia signal processing. Pappas has served as co-chair of the 2005 SPIE/IS&T Electronic Imaging Symposium, and since 1997, he has been co-chair of the SPIE Conference on Human Vision and Electronic Imaging. He is a Fellow of IEEE and SPIE and a member of the Board of Governors of the Signal Processing Society of IEEE.
Sheila S. Hemami received the BS from the University of Michigan (1990) and MS and PhD degrees from Stanford University (1992 and 1994), all in electrical engineering. In 1995, Hemami joined the faculty of the School of Electrical and Computer Engineering at Cornell University, Ithaca, NY, where she holds the title of professor and directs the Visual Communications Laboratory. Her research interests include general problems in visual communication, and visual system understanding and modeling. She is a Senior Member of the IEEE. Hemami is currently Chair of the IEEE Image and Multidimensional Signal Processing Technical Committee and has served as an Associate Editor for the IEEE Transactions on Signal Processing.