Center for Signal and Image Processing Seminar
Design and Quality Assessment of Forward and Inverse
Error Diffusion Halftoning Algorithms
Prof. Brian L. Evans
Laboratory for Image and Video
Engineering
Dept. of Electrical and Computer Engineering
The University of Texas at Austin, Austin, Texas
bevans@ece.utexas.edu
Collaboration with
Dr.
Thomas Kite and
Mr. Niranjan
Damera-Venkata
Thursday, August 27, 3:00 PM
Georgia Center for Advanced Telecommunications Technology Building
Georgia Institute of Technology
Slides in PDF Format
Abstract
Digital halftoning quantizes a grayscale image to one bit per pixel
for printing or display on binary devices.
Error diffusion is a halftoning method which employs
feedback to preserve the local image intensity and reduce
low frequency quantization noise.
Error diffusion essentially sharpens the original and adds highpass
quantization noise.
As a consequence, PSNR is not an appropriate quality measure.
As printer resolution increases, images must be enlarged and halftoned,
so fast low-cost interpolated halftoning systems become important.
However, halftones cannot be scaled, sharpened, rotated, or compressed
without causing severe degradation.
Degradation can be significantly reduced if the halftone is inverse
halftoned (converted to grayscale) before processing.
Inverse halftoning essentially blurs the halftone and adds noise.
Rehalftoning, which is inverse halftoning followed by halftoning,
is a technique under consideration for lossy coding of binary images
for the emerging JBIG2 standard.
The talk presents objective measures for the frequency distortion
and noise component in forward and inverse halftoning.
The measures are based on modeling the quantizer in error diffusion
as a gain plus additive noise and modeling the human visual system
as a lowpass linear system.
We compare competing forward and inverse halftoning schemes
in terms of computational complexity, PSNR, and quality.
This talk also presents a new algorithm for inverse halftoning,
which is based on anisotropic diffusion and is well-suited for
implementation in VLSI circuits and embedded software.
The new algorithm provides comparable results to the best
inverse halftoning method based on wavelet denoising, but at a
fraction of the implementation cost.
Finally, this talk applies the quantizer model to the improving
the design of interpolated halftoning and rehalftoning systems.
Biography
Brian L. Evans is an Assistant Professor in the
Department of Electrical and
Computer Engineering at
The University of Texas at Austin,
and the Director of the
Embedded Signal
Processing Laboratory within the
Center for Vision and
Image Sciences.
His research interests include
real-time embedded systems; signal, image and video processing systems;
system-level design; symbolic computation; and filter design.
Dr. Evans has published over 50 refereed conference and journal papers
in these fields.
He developed and currently teaches
EE381K
Multidimensional Digital Signal Processing,
EE382C
Embedded Software Systems, and
EE379K
Real-Time Digital Signal Processing Laboratory.
His B.S.E.E.C.S. (1987) degree is from the
Rose-Hulman Institute of Technology,
and his M.S.E.E. (1988) and Ph.D.E.E. (1993) degrees are from the
Georgia Institute of Technology.
From 1993 to 1996, he was a post-doctoral researcher at the
University of California at
Berkeley with the
Ptolemy Project.
Ptolemy is a
research
project and
software
environment focused on design methodology for signal processing,
communications, and controls systems.
In addition to Ptolemy, he has played a key role in the development and
release of six other computer-aided design frameworks, including the
Signals and Systems Pack for Mathematica, which has been on
the market since the Fall of 1995.
He is an Associate Editor of the IEEE Transactions on Image
Processing,
a Senior Member of the IEEE,
and the recipient of a 1997
National
Science Foundation CAREER Award.
Mail comments about this page to
bevans@ece.utexas.edu.