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In information theory, Shannon–Fano–Elias coding is a precursor to arithmetic coding, in which probabilities are used to determine codewords. [1] It is named for Claude Shannon , Robert Fano , and Peter Elias .
Fano's method usually produces encoding with shorter expected lengths than Shannon's method. However, Shannon's method is easier to analyse theoretically. Shannon–Fano coding should not be confused with Shannon–Fano–Elias coding (also known as Elias coding), the precursor to arithmetic coding.
In the field of data compression, Shannon coding, named after its creator, Claude Shannon, is a lossless data compression technique for constructing a prefix code based on a set of symbols and their probabilities (estimated or measured).
In information theory, an entropy coding (or entropy encoding) is any lossless data compression method that attempts to approach the lower bound declared by Shannon's source coding theorem, which states that any lossless data compression method must have an expected code length greater than or equal to the entropy of the source.
Here is the formal definition of each element (where the only difference with respect to the nonfeedback capacity is the encoder definition): W {\displaystyle W} is the message to be transmitted, taken in an alphabet W {\displaystyle {\mathcal {W}}} ;
Thus, encoding/decoding is the translation needed for a message to be easily understood. When you decode a message, you extract the meaning of that message in ways to simplify it. Decoding has both verbal and non-verbal forms of communication: Decoding behavior without using words, such as displays of non-verbal communication.
The decisive event which established the discipline of information theory, and brought it to immediate worldwide attention, was the publication of Claude E. Shannon's classic paper "A Mathematical Theory of Communication" in the Bell System Technical Journal in July and October 1948.
In computer networking, linear network coding is a program in which intermediate nodes transmit data from source nodes to sink nodes by means of linear combinations. Linear network coding may be used to improve a network's throughput, efficiency, and scalability , as well as reducing attacks and eavesdropping.