Hamid Rahim Sheikh, UT Austin, 8/26/03
An Information Theoretic Criterion for Image Quality Assessment
Measurement of quality is of fundamental importance for numerous image and
video processing algorithms. The goal of Quality Assessment (QA) research
is to design algorithms that can automatically assess the quality of
images or videos in a perceptually consistent manner. Traditionally, image
QA algorithms interpret image quality as fidelity or similarity with a
`reference' or `perfect' image in some perceptual space. Such
`Full-Reference' (FR) QA methods attempt to achieve consistency in quality
prediction by modelling salient physiological and psychovisual features of
the Human Visual System (HVS), or by arbitrary, albeit motivated,
mathematical criteria. In this talk I will present my approach to the
problem of image QA by proposing an information fidelity criterion that is
based on Natural Scene Statistics. QA systems are invariably involved with
judging the visual quality of images and videos that are meant for `human
consumption'. Researchers have developed sophisticated models to capture
the statistics of natural signals, that is, pictures and videos of the
visual environment. Using these statistical models in an
information-theoretic setting, I will present a QA algorithm that
is not only parameterless, but also outperforms current methods. We also
show that our approach from a diametrically opposite direction to
traditional HVS based QA methods, is functionally equivalent to them under
certain conditions. I will also present results that validate the
performance of the algorithm with an extensive subjective study involving
about 800 images.
Bio:
Hamid Rahim Sheikh is a PhD candidate at the University of Texas
at Austin. He is affiliated with the Laboratory for Image and Video
Engineering under the supervision of Dr. Alan C. Bovik. His research
interests include quality assessment, application of HVS and NSS models to
image and video processing problems, video coding, and embedded
implementations.