Data Compression
General Terms - Data Compression
Data compression, also known as source coding, refers to the process of encoding information in smaller units. Compressed data can work only when both the sender and the recipient understand the encoding scheme. Data compression is useful because it helps reduce expensive storage resources such as hard disks or transmission bandwidth.
There are two types of compression methods: Lossless and lossy compression. Lossless compression algorithm is possible because there is statistical redundancy for most of the real world data. As a result, these compression methods can represent sender’s data more concisely without error.
Lossy data compression or perceptual coding is allowed when some kind of loss of fidelity is acceptable. A typical example of lossy compression is the ‘rounding off’ of the subtle variations in a JPEG image. In some cases, transparent (unnoticeable) compression is desired, whereas in other cases, fidelity is sacrificed to a greater extent to compress the data as much as possible.
Lossless compression schemes are reversible, so original data can be reconstructed, whereas lossy schemes focus on higher compression, so original data may suffer some loss.
Disadvantages
On the negative side, decompression of the compressed data may become detrimental to some applications. For example, in some applications, the option of decompressing the video in full before watching it may be inconvenient, so the video may be viewed simultaneously as it is being decompressed. Such situations may require expensive hardware so as to make the decompressed video fast enough to be viewed as it is being decompressed.
