The course treats the principles underlying the encoding of speech, audio, video, and images at low bit rates. Source coding techniques such as scalar and vector quantization, orthogonal transforms, and linear prediction are introduced and their performance is analyzed theoretically. The theoretical bounds on the performance of source coders are discussed.
Course memo Spring 2024
Course presentation
Headings denoted with an asterisk ( * ) is retrieved from the course syllabus version Spring 2019
Content and learning outcomes
Course contents
This course introduces the principles of information theory and source coding, discusses fundamental source coding concepts, and provides hands-on experience for selected popular source coding algorithms. The course includes topics on information and entropy, lossless coding, Shannon's noiseless source coding theorem, lossy coding, rate distortion, Shannon's noisy source coding theorem, scalar and vector quantization, transform and predictive coding.
Intended learning outcomes
After passing this course, participants should be able to
- describe and use the principles of information theory, like entropy, mutual information, asymptotic equipartition, data processing, prefix codes, Kraft inequality, noiseless source coding, maximum entropy, rate distortion, noisy source coding, Shannon lower bound, backward channel, reverse waterfilling, energy concentration, etc. to develop source coding algorithms,
- develop source coding schemes for lossless coding, like Huffman coding, arithmetic coding, Lempel-Ziv coding, universal source coding,
- develop source coding schemes for lossy coding, like scalar and vector quantization, Lloyd-Max quantization, entropy-constrained quantization, high-rate quantization, transform coding, predictive coding,
- implement (for example with MatLab) and assess the developed source coding schemes / algorithms,
- explain coding design choices using the principles of information theory,
- develop source coding schemes for a given source coding problem,
- model and assess source coding schemes using the principles of information theory,
- analyze given source coding problems, identify and explain the challenges, propose possible solutions, and explain the chosen design.
To achive higher grades, participants should also be able to
- solve more advanced problems in all areas mentioned above.
Learning activities
Lectures on selected topics with discussions, exercises with problem solving and peer discussions, individual homework assignements with problem solving and software simulations.
Preparations before course start
Recommended prerequisites
EQ1220 Signal Theory or equivalent.
Literature
No information insertedSupport for students with disabilities
Students at KTH with a permanent disability can get support during studies from Funka:
Examination and completion
Grading scale
A, B, C, D, E, FX, F
Examination
- INL1 - Assignment, 1.5 credits, Grading scale: P, F
- TEN1 - Exam, 6.0 credits, Grading scale: A, B, C, D, E, FX, F
Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.
The examiner may apply another examination format when re-examining individual students.
Homework assignments 1.5 ECTS (P/F). Written exam 6 ECTS (A-F).
The section below is not retrieved from the course syllabus:
The points that you collect with your homework assignments will be considered in the exam/final grade as explained in the course introduction.
Ethical approach
- All members of a group are responsible for the group's work.
- In any assessment, every student shall honestly disclose any help received and sources used.
- In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.
Further information
No information inserted
Contacts
Course Coordinator
Teachers
Teacher Assistants
Examiner
Round Facts
Start date
16 Jan 2024
Course offering
- Spring 2024-60396
Language Of Instruction
English