Image Compression (5 op) 175512

Course description

The course introduces to image compression algorithms covering coding methods such as Huffman, Golomb and arithmetic coding; statistical, predictive and context modeling methods; DCT and wavelet transforms, vector quantization, and fractal-based compression. Existing image compression standards are studied including JBIG, JBIG-2, JPEG, JPEG-LS, JPEG-2000 and MPEG but the emphasizes is on the compression methods in these standards rather than the specifications details.


Teacher: Pasi Fränti
Schedule: 32 h, starting from 3.3.
Monday 12-14 (D106)
Monday 16-18 (D106)
Tuesday 12-14 (D106)
Lecture material


Teacher: Ville Hautamäki and Qinpei Zhao
Schedule: 16 h, starting from 6.3.
Thursday 10-12 (B179)

Preliminary knowledge

This is graduate level course so BSc degree or equivalent knowledge is required. It is recommended that you have completed Design and Analysis of algorithms and Image Analysis before taking Image Compression course. Other relevant courses are Pattern Recognition, and Advanced Digital Signal Processing.


Final exam: 21.4. at 12.00-14.00, (D106)
General exam dates

Recommended literature

P. Fränti, "Image Compression", Lecture Notes, 2002. (Word document)
A. Kolesnikov, "Image Compression", Lecture Notes, 2004. (PPT files)
M. Nelson, Data Compression: The Complete Reference (2nd edition). Springer-Verlag, New York, 2000.
M. Rabbani, P.W. Jones, Digital Image Compression Techniques. Bellingham, USA, SPIE Optical Engineering Press, 1991.
I.H. Witten, A. Moffat, and T.C. Bell, Managing Gigabytes: Compressing and Indexing Documents and Images. Van Nostrand Reinhold, 1994.
W.B. Pennebaker, J.L. Mitchell, JPEG Still Image Data Compression Standard. Van Nostrand Reinhold, 1993.
D.S. Taubman, M.W. Marcellin, JPEG-2000: Image Compression Fundamentals, Standards and Practice. Kluwer, 2002

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