Lecture (4h) with exercises (2h)
9 credit points

Lectures: Digital Video Lectures with Online Q&A Sessions
Monday 12-14 c.t.
Wednesday 14-16 c.t.
First Q&A: Wednesday, April 14th, 2021

Announcements – Description – Entrance requirements – Tutorials – Exams
Lecture notes/Assignments – References

14/04/2022 This is the website of IC2021, if you are looking for the current semester, please visit the IC22 website
08/04/2021 Lecture slots have been updated. First lecture discussion on April 14th. Watch the introduction video on Teams first if possible.
23/03/2021 In order to protect your health and inhibit the spread of Sars-Cov2, IC will be fully digital. Regular teaching will begin in April, but you can already register for the course, which enables you to join the course on MS Teams. There, you can already ask questions and watch the introduction video
25/03/2021 Website is online

Keep in mind that in most courses of studies, you also have to register via the HISPOS system of the Saarland University

Motivation: High resolution image data is becoming increasingly popular in research and commercial applications (e.g. entertainment, medical imaging). In addition, there is also a high demand for content distribution via the internet. Due to the resulting increase in storage and bandwith requirements, image compression is a highly relevant and very active area of research.

Teaching Goals: The course is designed as a supplement for image processing lectures, to be attended before, after or parallel to them. After the lecture, participants should understand the theoretical foundations of image compression and be familiar with a wide range of classical and contemporary compression methods.

Contents: The lecture spans the whole evolution of image compression from the dawn of information theory to recent machine-learning approaches. It is seperated into two parts:

The first half of the lecture deals with lossless image compression. We discuss the information theoretic background of so-called entropy coders (e.g. Huffman-coding, arithmetic coding, ...), talk about dictionary methods (e.g. LZW), and cover state-of-the-art approaches like PPM and PAQ. These tools are not limited to compressing image data, but also form core parts of general data compression software such as BZIP2. Knowledge about entropy coding and prediction is key for understanding the classic and contemporary lossless codecs like PNG, gif or JBIG.

The second part of the lecture is dedicated to lossy image compression techniques. We deal with classic transformation based compression (JPEG, JPEG2000), but also with emerging approaches like inpainting-based, fractal, or neural network compression. Furthermore, we consider related topics like human perception, and error measures.

Basic mathematics courses (such as Mathematik für Informatiker I-III) are recommended. Understanding English is necessary. Image processing lectures such as "Image Processing and Computer Vision" are helpful for some specific topics, but not necessary. For the programming assignments, some elementary knowledge of C is required.

The tutorials include homework assignments as well as self-study assignments. Homework assignments are handed in and graded, while self-study assignements are problems that are often designed to entice discussion. The latter type of assignments will not be handed in, but can be discussed in Q&A sessions. Homework consists of both theoretical and programming assignments, while self-study assignments are all theoretical. Working together in groups of up to 3 people is permitted and highly encouraged, especially for self-study assignments.

If you have questions concerning the tutorials, please do not hesitate to contact Pascal Peter.

There will be two open book written exams:

The first written exam will take place on Tuesday, July 27, 2021 from 2:00 to 4:00 pm in Building E2.2, Günter Hotz Lecture Theatre.
The second written exam will take place on Monday, October 11, 2021 from 2:00 to 4:00 pm in Building E2.2, Günter Hotz Lecture Theatre.


You can find the detailed rules for our exams in the self test assignemnt that will be published towards the end of the semester.
You can participate in both exams, and the better grades counts. Please remember that you have to register online for the exam in the HISPOS system of the Saarland University.

If you cannot attend the exam, contact Pascal Peter as early as possible. In case you have proof that you cannot take part for medical reasons or you have another exam on the same day, we can offer you an oral exam as a replacement. Note that we need written proof (e.g. a certificate from a physician/Krankenschein) for the exact date of the exam.

Lecture content in form of videos, slides, and assignments is available for download in MS Teams. This also holds for assignment submission and grading. You will be granted access to Teams after registration.

• B. Jähne, H. Haußecker, P. Geißler, editors, Handbook of Computer Vision and its Applications. Volume 1: Sensors and Imaging. Academic Press, San Diego 1999.
• S. Webb, The Physics of Medical Imaging. Institute of Physics Publishing, Bristol 1988.
• C. L. Epstein, Introduction to the Mathematics of Medical Imaging. Pearson, Upper Saddle River 2003.
• R. Blahut, Theory of Remote Image Formation. Cambridge University Press, 2005.
• A. C. Kak, M. Slaney, Principles of Computerized Tomographic Imaging. SIAM, Philadelphia 2001.
• Articles from journals and conferences.

Further references will be given during the lecture.