[DM1-5] Data compression techniques

Big data like imagery but also for example GML data sets need compression to be accessed / transferred in an acceptable time. Therefore some compression techniques have to be taught: - Motivation, examples of data sets which need compression - General introduction, vector - / raster data compression, compression lossless, lossy - Popular compression techniques, LZW (Lempel-Ziv-Welch) encoding, Huffman encoding - Techniques for raster data, runlength encoding, JPEG coding, wavelet etc. - Techniques for the reduction of vector data (Douglas Peuker etc.) - Data formats, overview and relation to compression techniques

External resources

• Kolesnikov, Alexander, and Fränti, Pasi. "Data Reduction of Large Vector Graphics." Pattern Recognition 38, no. 3 (2005): 381-94.
• Mao, Qingzhou, Zhou, Baoding, Zou, Qin, and Li, Qingquan. "Efficient and Lossless Compression of Raster Maps." Signal, Image and Video Processing 9, no. 1 (2015): 133-45.
• Silva-Coira, Fernando, Paramá, José R, Ladra, Susana, López, Juan R, and Gutiérrez, Gilberto. "Efficient Processing of Raster and Vector Data." PloS One 15, no. 1 (2020): E0226943.
• Vani, A.Gokila. "Image Compression: Huffman and LZW Techniques." International Journal for Research in Applied Science and Engineering Technology, 2017, 110-33. DOI: 10.22214/ijraset.2017.10016

Learning outcomes

• 2287 - Work with different data compression techniques

  Work with different data compression techniques

Self assessment

Outgoing relations

• [DM1-5] Data compression techniques subclassof [DM1] Foundations for Data Modelling Storage and Exploitation

Incoming relations

• [DM3-3] Grid compression methods subclassof [DM1-5] Data compression techniques