MATH/MTHE 477/877
Data Compression and Source Coding
Winter 2020
Announcements 
Slides 
Homework
Course Outline]
 Instructor

Tamas Linder
Office: Jeffery Hall, Room 401
Telephone: 5332417
Email: tamas.linder@queensu.ca
 Teaching Assistant
 Mohammad Akbari
Email: 13mav1@queensu.ca

Announcements

 Lectures

Tuesday 8:30  9:45 am, Friday 1011:15 am, Jeffery Hall 126
 Office Hours

by appointment
 Homework Assignments

Assignments will be posted on this web
site (click here to see them); no
paper copies will be handed out. Solutions to the assignments
will be on reserve at the Circulation Desk of Stauffer Library.
 Evaluation
 Homework 15%, Midterm Test 30%, Final Exam 55%
 The Midterm Test is scheduled for Week 9, Tuesday, March 10, in
class.
 Policy for missing exams

There will be no makeup exams. If a student misses the midterm
due to severe illness or a personal tragedy, then the final exam
will count towards 85% of the student's mark.
 Text
 Recommended Text
 A. Gersho and R. M. Gray, Vector Quantization and Signal
Compression, Kluwer, 1992.
 F. Alajaji and P.N. Chen, An Introduction to SingleUser
Information Theory, Springer, 2018.
 T. M. Cover and J. A. Thomas, Elements of Information Theory,
2nd Ed., Wiley, 2006.
 Course Outline

Efficient transmission and storage of information is of critical
importance in many branches of science
and engineering. The means by which to achieve this is source coding
(a.k.a. data compression), a discipline
that studies the compact representation of information bearing signals
(such as text, speech, still image, and
video) for the purpose of storage or transmission. Source coding is
part of the general theory of communication,
and is closely related to and information theory, signal processing,
as well as probability and random
processes.
In this course the fundamentals of the theory and practice of data
compression will be studied. The following is a list of topics
that will be covered in more or less detail.
 Fundamentals of RateDistortion Theory: The
ratedistortion function and its properties, Shannon's lossy source
coding theorem, calculation of the ratedistortion function, Joint
sourcechannel coding, Shannon's lossy sourcechannel coding
theorem, Shannon limit for communication systems.
 Lossless Coding: Arithmetic coding, lossless universal
coding, Kolmogorov complexity, LempelZiv coding.
 Scalar Quantization: uniform and nonuniform quantization,
companding quantization,
predictive quantization, speech coding fundamentals, CELP.
 Frequency Domain Coding: Transform coding, bit
allocation, subband coding, wavelet coding, image coding
fundamentals, JPEG, JPEG2000.
 Vector Quantization amd High Resolution Theory:
Optimality conditions, design algorithms (LloydMax and related
methods), lattice quantization, Bennett's integral, the ZadorGersho formula.
 Prerequisite

Information Theory MATH 474/874
