Image Compression

Image Compression

After registering for the course, take the guided tour to gain teams access and to learn important details about the lecture.

Description

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.

 

Format

The lecture is offered in a inverted class room format. The course is structued into learning units which are each addressed by four different components.

• Component 1 (passive): Self-study of lecture materials.
  Students watch videos and read lecture materials as preparation for the discussion sessions. The official time slot is Mo 16-18, but students can determine this freely at will when they want to participate.
• Component 2 (semi-active): Discussion meetings, Wed 12:15-14:00, E1.3, HS001
  Students discuss with the lecturer. The focus lies on student questions, but the sessions will also always contain a (voluntary and anonymous) quiz and a short summary of the current learning unit (this is not a lecture that replaces self-study). Participation is voluntary and possible on campus and online via MS Teams.
• Component 3 (active): Tutorials. (Friday 12:30 and 16:15)
  Students work on class room assignments during the tutorial hours. These are often designed to be more open and inviting for discussions. Tutors support the students during their work and also answer questions on homework assignments on request. Attendance is not strictly mandatory, but tutorial participation yields points that count towards exam admission.
• Component 4 (active): Homework. 
  Students solve theoretical or programming assignments. Most assignments are due one week after they are published, but some larger programming assignments span multiple weeks. Example solutions are available in the CMS after the deadline. Homework is graded and points count towards exam admission.

For more information on the lecture format attend the first meeting (17.4.) and take our guided tour after registration.

 

Entrance requirements

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.

 

Assessments / Exams

There will be two written exams with a limited open book format.

2.8.2024, 13:30, E2.2 Günter Hotz Lecture Theatre
25.09.2024, 13:30, E1.3 HS002

Detailed rules for our exams are published in the self test assignment which also provides an impression of the structure and assignment types to be expected from the real exams.

You can participate in both exams, and the better grades counts. Please remember that you have to register online for the exam in the LSF system of 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 Materials / Assignments

All slides, assignments, example solutions, and an exam formulary are offered for download in the CMS.

 

References

There is no specific book that covers the complete content of this class. However, each of the following books covers several of the topics discussed in the lecture:

T. Strutz: Bilddatenkompression. Vieweg+Teubner (in German)

D. Hankerson, G. A. Harris, and P. D. Johnson, Jr.: Introduction to Information Theory and Data Compression. Chapman & Hall/CRC

K. Sayood: Introduction to Data Compression. Morgan Kaufmann

Further references will be given during the lecture.