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Image Compression
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.
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 programming skills are required.
Assessments / Exams
There will be two written exams with a limited open book format.
21. July 14-17, E1.3, HS002
30. September 14-17, 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 Format
The lecture takes place in an inverted class room formats. Each weak, you have a self-study session where you watch lecture videos or read the materials. This prepares the active components of the lecture. In a weekly meeting we discuss the lecture contents in a Q&A format and you can participate in an anonymous weekly quiz. Additionally, the weekly tutorials provide class room assignments in addition to discussions of the homework.
Weekly meetings:
Monday 12:15-14:00, E1.3, HS001
First lecture: 07.04.2024
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.