Data Compression Task
(Redirected from source coding)
Jump to navigation
Jump to search
A Data Compression Task is a computational task that requires the transformation of a data set into a smaller data set that retains the information.
- AKA: Source Coding.
- Context:
- output: a Compressed Dataset, such as a compressed file.
- It can range from being a Lossless Data Compression Task to being a Lossy Data Compression Task.
- It can be solved by a Data Compression System (that implements a Data Compression Algorithm).
- It can be instantiated in a Data Compression Act.
- …
- Example(s):
- Counter-Example(s):
- See: Entropy, Information Theory.
References
2016
- (Wikipedia, 2016) ⇒ https://en.wikipedia.org/wiki/data_compression Retrieved:2016-5-13.
- In signal processing, data compression, source coding, or bit-rate reduction involves encoding information using fewer bits than the original representation. Compression can be either lossy or lossless. Lossless compression reduces bits by identifying and eliminating statistical redundancy. No information is lost in lossless compression. Lossy compression reduces bits by identifying unnecessary information and removing it. The process of reducing the size of a data file is referred to as data compression. In the context of data transmission, it is called source coding (encoding done at the source of the data before it is stored or transmitted) in opposition to channel coding. Compression is useful because it helps reduce resource usage, such as data storage space or transmission capacity. Because compressed data must be decompressed to use, this extra processing imposes computational or other costs through decompression; this situation is far from being a free lunch. Data compression is subject to a space–time complexity trade-off. For instance, a compression scheme for video may require expensive hardware for the video to be decompressed fast enough to be viewed as it is being decompressed, and the option to decompress the video in full before watching it may be inconvenient or require additional storage. The design of data compression schemes involves trade-offs among various factors, including the degree of compression, the amount of distortion introduced (when using lossy data compression), and the computational resources required to compress and decompress the data. [1]
- ↑ S. Mittal and J. Vetter, "A Survey Of Architectural Approaches for Data Compression in Cache and Main Memory Systems", IEEE Transactions on Parallel and Distributed Systems, 2015.