DATA COMPRESSION

Data Compression

Data Compression

Data compression algorithms are designed to reduce the size of the data so that it requires less disk space for storage and less bandwidth to be transmitted on a data communication channel, and. Data compression is usually obtained by substituting a shorter symbol for an original symbol in the data. The symbols may be characters, words, phrases, or any other unit that may be stored in a dictionary of symbols.

Data compression algorithms can be classified in two different ways. They may be classified as lossless or lossy, or may be classified according to a fixed or variable size of group taken from the original file and written to the compressed file and. Lossy compression can provide compression ratios of 100:1 to 200:1, depending on the type of information being compressed, while lossless compression techniques can usually achieve a 2:1 to 8:1 compression ratio. Lossy compression techniques are often tuneable in that they can turn the compression up to improve throughput, but at the cost of a loss in quality. Compression can also be turned downed to the point at which there is little loss of data, but throughput will be affected. A detailed discussion of data compression techniques are found in and.

Data communication and data storage applications have benefited greatly from data compression methodology. By reducing the size of the transmitted data, the effective bandwidth of the communications channel can be increased. The obvious advantage for data storage applications is that smaller data require less storage space. Thus, the effective storage capacity of any storage medium is increased if the data are compressed. There is, however, with current technology, another important implication of data compression on storage technology.

There are three major types of compression techniques: Substitution, Statistical and Dictionary based compression. Substitution data compression techniques involve the swapping of repeating characters by a shorter representation, such as null suppression, Run Length Encoding (RLE) and, bit mapping and half byte packing. Statistical data compression techniques involve the generation of the shortest average code length based on an estimated probability of the characters, such as Shannon-Fano coding, and , static/dynamic/adaptive Huffman coding , , and , and arithmetic coding and . Finally, dictionary data compression techniques involve the substitution of sub-strings of text by indices or pointer code, relative to a dictionary of the sub-strings, such as Lempel-Zif-Welch (LZW) data compression technique , and .

Many compression algorithms use a combination of different data compression techniques to improve compression ratios. For example, Fax machines use a combination of RLE and Huffman coding to achieve compression ratios of about 10:1. Since both coder and decoder use the same modification algorithm, the decoder will adjust its decoding table in the same way as the coder, and the two will remain in sync.

Data compression techniques

Data compression squeezes data so that it requires less disk space for storage and less bandwidth to be transmitted on a data communication channel. In this section, we present a brief description for the different classes and ...