Data compression symmetry
In today's world, Data compression symmetry has become increasingly relevant. Whether due to its impact on society, its influence on popular culture or its relevance in the scientific field, Data compression symmetry has become a topic of general interest for a wide spectrum of audiences. In this article we will explore topics related to Data compression symmetry, analyzing its importance, its implications and its evolution over time. From its origins to the present, Data compression symmetry has been the object of debate, controversy and fascination, and in the following lines we will seek to shed light on this topic that is so relevant in the contemporary world.
Symmetry and asymmetry, in the context of data compression, refer to the time relation between compression and decompression for a given compression algorithm.
If an algorithm takes the same time to compress a data archive as it does to decompress it, it is considered symmetrical. Note that compression and decompression, even for a symmetric algorithm, may not be perfectly symmetric in practice, depending on the devices the data is being copied to and from, and other factors such as latency and the fragmentation on the device.
In turn, if the compression and decompression times of an algorithm are vastly different, it is considered asymmetrical.
Examples
Symmetric algorithms are typically used for media streaming protocols, as either the server taking too long to compress the data, or the client taking too long to decompress, would lead to delays in the viewing of the data.
Asymmetrical algorithms wherein the compression is faster than the decompression can be useful for backing up or archiving data, as in these cases data is typically much more often stored than retrieved.
References
 | This article needs additional citations for verification. (February 2009) |
Further reading
- Peter D. Symes (2004). "Symmetric and Asymetric Systems". Digital Video Compression. McGraw-Hill Professional. pp. 11. ISBN 9780071424875.
- David Salomon (2008). A Concise Introduction to Data Compression. Springer. pp. 15–16. ISBN 9781848000711.
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