Entropy coding is a technique used in information theory to represent a finite discrete probability distribution (pdf) with a shorter code than the original dis...
Entropy coding is a technique used in information theory to represent a finite discrete probability distribution (pdf) with a shorter code than the original distribution. This is achieved by assigning a shorter code to more likely events and a longer code to less likely events.
To illustrate, consider a bag containing 5 different colored balls, with the color distribution represented by the following probabilities:
P(Red) = 0.3
P(Green) = 0.4
P(Blue) = 0.1
P(Yellow) = 0.1
P(Orange) = 0.05
If we were to simply use the probabilities as a code, it would be difficult to distinguish between Red, Green, Blue, Yellow, and Orange. However, by using a more complex code with the following probabilities:
P(Red) = 0.2
P(Green) = 0.3
P(Blue) = 0.2
P(Yellow) = 0.2
P(Orange) = 0.1
It becomes much easier to distinguish between the colors. This is the essence of entropy coding: by assigning a shorter code to more likely events and a longer code to less likely events, we can achieve better information compression.
Furthermore, entropy coding can also be used for error detection and error correction. By using a code that is more likely to contain certain symbols, we can detect errors that occur in the transmission of the data. This is particularly useful in situations where the data is transmitted over a noisy channel.
Overall, entropy coding is a powerful technique for information representation that can achieve significant compression while preserving the ability to distinguish between different symbols