chucko@saturn.ucsc.edu (Chuck Stein) (06/11/88)
Distribution: na
Organization: University of California, Santa Cruz; CIS/CE
The University of California
Eighteenth Annual
INSTITUTE IN COMPUTER SCIENCE
presents courses in:
* Scientific Visualization * Fault Tolerant Computing
* Parallel Computation * Image Engineering
* Data Compression * Machine Learning
at
Techmart, Santa Clara
and
on campus in Santa Cruz
Following is a course description for:
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Arithmetic Coding and Practical Data Compression Applications
July 27-29
Instructor: GLEN G. LANGDON, JR.
X419 Computer Engineering (2)
This course is intended for data compression system engineers and
algorithm developers who want a modern background in the key
compression system components of data modeling by application,
statistics gathering, and encoding (decoding) events according to their
own relative frequencies.
Overview
Arithmetic coding is a relatively new approach to encoding information
for compression purposes. Arithmetic coding techniques have been
developed that make it easy to adapt to the statistics of the file being
compressed. Techniques to model data for ordinary 8-bit data files,
black-white images, and grey-scale images are discussed.
Wednesday:
Introduction. Data compression systems: applications, purposes,
components.
Theoretical aspects. Entropy of a source, stationary sources, dependent
sources, and the advantages of context dependence. Relationship of
information theoretical aspects to the engineering of practical data
compression systems.
Coding. Prefix codes, Huffman codes, arithmetic codes, variable-to-
fixed codes, run-length codes. Comparisons of strengths and
weaknesses.
FIFO arithmetic coding. General alphabets versus binary alphabets.
Encoding multi-symbol alphabets with binary codes.
Thursday
Modeling versus coding. Two important requirements for achieving
data compression. The data class, determining events, and useful
contexts. Converting the input data to statistical events for coding
purposes.
The statistical aspect. Techniques for estimation of symbol
frequencies. Estimation compromises for coding purposes.
Adaptation to symbol frequencies under stationarity versus
nonstationarity assumptions. Adapting in context-dependent
environment. Deceptively simple dynamic adaptation techniques for
nonstationary binary sources.
Example: Discrete domain. Data compression of text documents and
messages composed of strings of words. Huffman-based approach.
Ziv-Lempel coding. Mixed-order Markov models. Dictionary-based
models. Hybrid schemes.
Friday
Example: Analog domain.
Pulse code modulation. Information models: lossy versus lossless
Compressing binary image data. CCITT Group IV compression
algorithm for facsimile. Example of lossy compression of scanned
documents by symbol-matching techniques. Compression of bilevel
images by a simple model and one-pass adaptive binary arithmetic
code.
Compressing grey-level images: Effect of transform approaches. An
example information lossless approach using adaptive arithmetic
coding. Speech compression example by companding and lossless
model with arithmetic coding.
Instructor:
GLEN G. LANGDON, JR. is a Professor of Computer Engineering at the
University of California, Santa Cruz, and a retired Research Staff
Member from the IBM Research Division's Almaden Research Center in
San Jose, California.
Fee: Credit, $875 (EDP C6032)
Dates: Three Days, Wed.-Fri., Jul. 27-29, 9 a.m.-5 p.m.
Place: Techmart, 5201 Great America Pkwy., Santa Clara
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RESERVATIONS:
Enrollment in these courses is limited. If you wish to attend a course
and have not pre-registered, please call (408) 429-4535 to insure that
space is still available and to reserve a place.
DISCOUNTS:
Corporate, faculty, IEEE member, and graduate student discounts and
fellowships are available. Please call Karin Poklen at (408) 429-4535
for more information.
COORDINATOR:
Ronald L. Smith, Institute in Computer Science, (408) 429-2386.
FOR FURTHER INFORMATION:
Please write Institute in Computer Science, University of California
Extension, Santa Cruz, CA 95064, or phone Karin Poklen at (408) 429-
4535. You may also enroll by phone by calling (408) 429-4535. A
packet of information on transportation and accommodations will be sent
to you upon receipt of your enrollment.