University of Minnesota
Institute of Technology
myU OneStop


Electrical and Computer Engineering

A Signal-Processing Approach to Modeling Vision and Application

Sheila Hemami, Ph.D.
Cornell University

Current state-of-the-art algorithms that process visual information for end use by humans treat images and
video as traditional signals and employ sophisticated signal processing strategies to achieve their excellent
performance. These algorithms also incorporate characteristics of the human visual system (HVS), but
typically in a relatively simplistic manner, and achievable performance is reaching an asymptote. However,
large gains are still realizable with current techniques by aggressively incorporating HVS characteristics,
combined with a good dose of clever signal processing. Achieving these gains requires HVS characterizations
which better model natural image perception ranging from sub-threshold perception (where distortions are not
visible) to suprathreshold perception (where distortions are clearly visible).  In this talk, I will present results
from our lab characterizing the responses of the HVS to natural images, and contrast these results with
'classical' psychophysical results. I will also present applications of these results to image compression and
quality assessment, as well as some signal processing problems (and their solutions) that emerged in
applying the psychophysical results.