Joint source and channel coding

In information theory, joint source–channel coding is the encoding of a redundant information source for transmission over a noisy channel, and the corresponding decoding, using a single code instead of the more conventional steps of source coding followed by channel coding. ^[1]

7 relations: Allen Gersho, Code, Data compression, Forward error correction, Information source, Information theory, Noisy-channel coding theorem.

Allen Gersho

Allen Gersho is a professor emeritus at UCSB who made significant contributions in the area of signal compression and speech coding.

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Code

In communications and information processing, code is a system of rules to convert information—such as a letter, word, sound, image, or gesture—into another form or representation, sometimes shortened or secret, for communication through a communication channel or storage in a storage medium.

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Data compression

In signal processing, data compression, source coding, or bit-rate reduction involves encoding information using fewer bits than the original representation.

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Forward error correction

In telecommunication, information theory, and coding theory, forward error correction (FEC) or channel coding is a technique used for controlling errors in data transmission over unreliable or noisy communication channels.

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Information source

An information source is a person, thing, or place from which information comes, arises, or is obtained.

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Information theory

Information theory studies the quantification, storage, and communication of information.

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Noisy-channel coding theorem

In information theory, the noisy-channel coding theorem (sometimes Shannon's theorem or Shannon's limit), establishes that for any given degree of noise contamination of a communication channel, it is possible to communicate discrete data (digital information) nearly error-free up to a computable maximum rate through the channel.

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References

[1] https://en.wikipedia.org/wiki/Joint_source_and_channel_coding

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