Call for Papers
IEEE Journal of Selected Topics in Signal Processing
IEEE Signal Processing Society
Special Issue on Visual Media Quality Assessment
With the increasing demand for digital image and video technologies in
applications as broad as entertainment and communications, security,
monitoring, and medical imaging, there is a growing need for the
automatic assessment of the quality of visual media. Many factors can
affect and impair the quality of visual media including compression,
transmission, protection, display, printing, acquisition and
reproduction systems. Automatic visual media quality assessment is
crucial for monitoring and controlling the visual quality in existing
and emerging multimedia systems, and has the potential to impact
next-generation systems by providing objective metrics for use during
the design and testing stages and by reducing the need for extensive
evaluation with human subjects.
Visual media quality assessment aims at quantifying the quality of
visual media, including still pictures, image sequences, 3D visual
data, and 3D models, by means of quality metrics. These metrics vary
with the considered applications, and range from metrics that measure
specific visual impairments to those that assess the overall visual
quality in the presence of various impairments. For applications and
products that target human consumers, it is desirable to have metrics
that will predict the perceived visual quality as measured with human
subjects. Visual quality assessment metrics can be further divided
into full-reference, reduced-reference, and no-reference quality
metrics. Full-reference visual quality metrics compare the
to-be-assessed visual media to a reference, which is typically the
original visual data. In many applications where the original visual
data is not available, reduced-reference and no-reference metrics are
used. Reduced-reference metrics make use of a set of reference
features or characteristics, which could have been extracted from the
original visual data. No-reference quality metrics attempt to predict
the visual quality without any reference, which is very useful in
practice but very challenging.
A great deal of interest and research have been devoted to the design
and development of visual quality metrics, particularly full-reference
and reduced-reference metrics for image quality assessment. However,
for many applications, reliable automatic visual quality assessment is
lacking, particularly those requiring no-reference visual quality
assessment. In addition, there is a need for methods that can reliably
assess the visual quality of video and other 3D visual media. The
motivation for this special issue is to highlight the importance,
challenges, and applications of visual media quality assessment and
its interdisciplinary nature which includes vision science, optics,
color science, signal processing, psychology, and biology. Our goal is
to feature recent advances in the area of automatic visual media
quality assessment, including theoretical, experimental, and
computational methods and results. We invite researchers to submit
original papers describing new approaches in all areas related to
automatic visual media quality assessment including, but not limited
to, the following topics:
- Global and impairment-specific visual quality assessment metrics
- Full-reference, reduced-reference, and no-reference visual quality
assessment of still-pictures and video
- Visual quality assessment of 3D visual data and 3D models
- Visual quality assessment of High-Definition image and video content
- Statistical methods for automatic visual quality assessment
- Perceptually/Biologically-inspired automatic visual quality assessment
- Visual quality metrics for specific applications
Submission procedure
Prospective authors can find submission information at
http://www.ece.byu.edu/jstsp . Submitted manuscripts should
not have been previously published nor be currently under
consideration for publication elsewhere. Authors are advised
to follow the Author's Guide for the formats of manuscripts
submitted to the IEEE Transactions on Signal Processing
as detailed at http://ewh.ieee.org/soc/sps/tsp/
The manuscript will undergo a standard peer review process.
Manuscript submissions due: April 30, 2008
First review completed: July 31, 2008
Revised manuscripts due: September 15, 2008
Second review completed: October 31, 2008
Final manuscript due: November 30, 2008
Lead Guest Editor:
Lina Karam, Arizona State University, Tempe, Arizona
(karam@asu.edu)
Guest Editors:
Touradj Ebrahimi, EPFL, Lausanne, Switzerland
Q2S-NTNU, Trondheim, Norway
(touradj.ebrahimi@epfl.ch)
Sheila Hemami, Cornell University, Ithaca, New York
(hemami@ece.cornell.edu)
Thrasos Pappas, Northwestern University, Evanston, Illinois
(pappas@ece.northwestern.edu)
Robert Safranek, Benevue, Warren, New Jersey
(rjs@@benevue.com)
Zhou Wang, University of Waterloo, Waterloo, Ontario, Canada
(z.wang@ece.uwaterloo.ca)
Andrew B. Watson, NASA Ames Research Center, Moffett Field, California
(andrew.b.watson@nasa.gov)
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