Data compression

Data compression:
Data compression in GIS refers to the compression of geospatial data so that the volume of data transmitted across networks can be reduced. A properly choosen compression algorithm can reduce data size upto 5 - 10% of the original image and 10 - 20% for vector and text data. Such compression ratios could result in significant performance improvement.
Data compression algorithms can be classified into lossless and lossy . Lossless compression algorithms are those where the bit streams can be recovered to original data. These algorithms should be used if loss of even a single bit may cause serious and unpredictable consequences.
Lossy compression algorithms should be used where a certain level of distortion can be tolerated. They are used to achieve a higher level of compression.
Examples of lossless compression algorithms are:
-Huffman coding
-Arithmetic coding
-Lempel-Ziw Coding (LZC) and
-Burrows-Wheeler Transform (BWT)

Examples of lossy compression algorithms are:
-Differential Pulse Coded Modulation (DPCM)
-Transform Coding
-Subband Coding and
-Vector Quantization

Data compression refers to the process of reducing the size of a file or database. Compression improves data handling, storage, and database performance.
Examples of compression methods include quadtrees, run-length encoding, and wavelets.

Typically, a GIS software refers to data compression as a process that removes unreferenced rows from geodatabase system tables and user delta tables. Compression helps maintain versioned geodatabase performance.