Saltar al contingut Menu
  • Home
  • Information
  • Contact
  • Map

Data and Image Compression (CDI)

Credits Dept.
7.5 (6.0 ECTS) MAT


Person in charge:  (-)

General goals

The first general objective is to understand the importance of compressing, as for example in storing or sending data. The other general objective is to distinguish between lossless and lossy compressorss and to be able to acquire sound criteria to decide which one to use in each case.

Specific goals


  1. Compression schemes, lossy versus lossless compression, how and when to use each type.
    Concept of information and its measurement. Theoretical limit (under precise conditions) of lossless compression algorithms. The most commonly used lossless compression algorithms.
  2. Understanding that measurement of image compression quality methods implies statistical tests with users and images in order to establish less costly alternatives. Acquaintance with the general scheme used by compression methods for fixed images, and the need to control the stage where data losses are produced.
  3. To understand that lossy image compression methods also include a lossless compression stage.
  4. The most commonly used image compression algorithms. Image compression standards. JPEG and JPEG2000.
  5. Video compression schemes. MPEG1/MPEG2.


  1. Learn how to implement the main lossless compression methods and to assess their quality.
  2. Learn how to implement and modify existing implementations of various compression methods for fixed images.
  3. Learn how to assess the qualitative and quantitative goodness of image compression algorithms that students are likely to encounter.
  4. Ability to effectively implement a compression method that the student has read of in a magazine article, which is written in English and lacks a description of implementation details.


  1. Ability to design systems, components and processes meeting certain needs, using the most appropriate methods, techniques and tools in each case.
  2. Ability to take take decisions when faced with uncertainty or contradictory requirements.
  3. Ability to act independently: Know how to work on one's own with just the bare minimum of knowledge and guidance.
  4. Ability to learn on one's own.
  5. Ability to make convincing formal and informal oral presentations.


Estimated time (hours):

T P L Alt Ext. L Stu A. time
Theory Problems Laboratory Other activities External Laboratory Study Additional time

1. Lossless compression
T      P      L      Alt    Ext. L Stu    A. time Total 
8,0 4,0 8,0 0 8,0 12,0 0 40,0
1.1. Discrete memoryless sources. Information and entropy.
1.2. Fixed length codes. Source extensions.
1.3. Variable length codes.
1.4. Huffman codes. Shannon"s First Theorem.
1.5. Arithmetic coding.
1.6. Adaptive methods.
1.7. Dictionary methods.

2. Lossy compression (images), Part One
T      P      L      Alt    Ext. L Stu    A. time Total 
10,0 5,0 10,0 0 10,0 15,0 0 50,0

2.1. Light and colour.

2.2. Concept of digital/digitalised images. Quantitative and qualitative measurement of compression. The scaleability concept.

2.3. Lossy and lossless predictive methods. Functional scheme of the coder and decoder.

2.4. Images as points of a NxM-dimensional unitary cube. Contractive applications and Fixed Point Theorem. Fractal compression.

2.5. Images as elements in a vectorial space. Non-canonic bases and expansion of the whole image with a linear combination of basic images.

2.6. Concept of energy and orthogonal transforms. Energy compaction.

3. Lossy compression (images), Part Two
T      P      L      Alt    Ext. L Stu    A. time Total 
10,0 5,0 10,0 0 10,0 15,0 0 50,0

3.1. DCT. The JPEG2000 standard Bit assignment and entropy coding.

3.2. An introduction to linear filtering and the sampling theorem.

3.3. Sub-band decomposition. Perfect reconstruction.

3.4. Pyramid schemes and Discrete Wavelet Transformation (DWT).

3.5. Lifting method for DWT.

3.6. EZW algorithm.

3.7. The JPEG2000 standard.

3.8. Introduction to Compressed Sensing

3.9. Introduction to video compression schemes. Movement estimation and compensation. MPEG1/MPEG2 standards.

Total per kind T      P      L      Alt    Ext. L Stu    A. time Total 
28,0 14,0 28,0 0 28,0 42,0 0 140,0
Avaluation additional hours 2,0
Total work hours for student 142,0

Docent Methodolgy

There are always moments in which the teacher needs to pick up the chalk and explain things on the blackboard. However, this will not be the most commonly used teaching method. We will usually set out the basic ideas using slides, either of the overhead variety or on a laptop PC/LCD projector. In any event, all of the course materials will be available to students in pdf format.

Evaluation Methodgy

One of the skills students are expected to acquire in the course is the ability to effectively implement a compression method extracted from an article in which some of the details regarding implementation have been omitted. Accordingly, a key part of the course involves defending the implementation of a compression scheme before other students in class. The work will assigned during the first 3 weeks of the course and will be defended in class during the last few weeks. The exercises will be carried out by students working in pairs; students will be required to submit a report on the work to the teacher on digital support containing all necessary presentations, programme, images, and test results. There will also be a test on concepts.

Work on concepts will make up 25% of the final grade. The remaining 75% corresponds to laboratory and implementation assignments, and includes a final oral presentation. Given the weight carried by the practical work within the overall course assessment, the teacher will periodically monitor students individually during the lab sessions to check on their progress.

Basic Bibliography

  • Khalid, Sayood Introduction to data compression, Morgan Kaufmann Publishers, 2000.
  • David S. Taubman, Michael W. Marcellin JPEG2000 : image compression fundamentals, standards and practice; David S. Taubman, Michael W. Marcellin, Kluwer Academic Publishers, 2002.
  • David Salomon Data Compression: The Complete Reference, Springer, 2007.
  • Poynton, Charles Digital Video and HTDT. Algorithms and Interfaces, Morgan Kaufmann Publishers, 2007.

Complementary Bibliography

  • Bjarne Stroustrup The C Programming Language, Special Edition, Addison-Wesley, 2007.

Web links


Previous capacities

Students wishing to take this course must have passed Mathematics I, Mathematics II and Programming Practice. Students should have taken or be taking the Statistics course.


logo FIB © Barcelona school of informatics - Contact - RSS
This website uses cookies to offer you the best experience and service. If you continue browsing, it is understood that you accept our cookies policy.
Classic version Mobile version