Data Compression and Source Coding
Office: Jeffery Hall, Room 401
- Teaching Assistant
- Mohammad Akbari
Tuesday 8:30 - 9:45 am, Friday 10-11:15 am, Jeffery Hall 126
- Office Hours
- 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.
- Homework 15%, Midterm Test 30%, Final Exam 55%
- The Midterm Test is scheduled for Week 9, Tuesday, March 10, in
- 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.
- Recommended Text
- A. Gersho and R. M. Gray, Vector Quantization and Signal
Compression, Kluwer, 1992.
- F. Alajaji and P.-N. Chen, An Introduction to Single-User
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
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 Rate-Distortion Theory: The
rate-distortion function and its properties, Shannon's lossy source
coding theorem, calculation of the rate-distortion function, Joint
source-channel coding, Shannon's lossy source-channel coding
theorem, Shannon limit for communication systems.
- Lossless Coding: Arithmetic coding, lossless universal
coding, Kolmogorov complexity, Lempel-Ziv coding.
- Scalar Quantization: uniform and nonuniform 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 (Lloyd-Max and related
methods), lattice quantization, Bennett's integral, the Zador-Gersho formula.
Information Theory MATH 474/874