Welcome to the homepage of the lecture

Image Compression

Summer Term 2020

Image Compression

Image Compression

Two Computer Science Teaching Awards (Summer Terms 2017 and 2019)

Lecturer: Dr. Pascal Peter

Summer Term 2020

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

Lectures: Digital Video Lectures with Online Q&A Sessions
Monday 14-16 c.t.
Wednesday 12-14 c.t.
First Q&A: Monday, May 4th, 2020

AnnouncementsDescriptionEntrance requirementsTutorialsExams
Lecture notes/AssignmentsReferences

12/05/2020 Registration is now closed.
30/04/2020 Introduction video and slides are online.
15/04/2020 In order to protect your health and inhibit the spread of Sars-Cov2, this years iteration of IC will be fully digital until further notice. The website has been updated accordingly and more information will be availbe soon. Regular teaching will begin on May 4th, but you can already register for the course
12/03/2020 Due to the Sars-Cov2 outbreak, the start of the summer semester has been rescheduled to May 4th. This will also affect the schedule of this lecture. This website will be updated as soon as Saarland university has finalised its pandemic plan.
19/02/2020 Website is online

Registration for this lecture was open until Monday, May 11th. 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 exams, one takes place at the end of the lecture period and a second one just before the start of the next semester. If possible, we will provide written open book exams as in previous years. However, the current situation might force us to provide alternatives such as contact free oral exams. Due to the dynamics of the Sars-Cov2 pandemic, decisions w.r.t. this issue can only be made later in the semester.

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 notes / Assignments

Lecture content in form of videos, slides, and assignments is available for download on the Moodle page of this course. Access will be granted automatically. For more information please watch the video below. You receive login credentials for the introduction video and slides in your welcome mail to the IC20 mailing list. This mail arrives after registration. Note that this mail is sent after manual confirmation and can thus be delayed.

No. Video Lecture Date Script Slides
1 Introduction to the Lecture 11/04 [download] [download]

  • 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.

MIA Group
The author is not
responsible for
the content of
external pages.

Imprint - Data protection