What is Arithmetic Coding & How Does it's Works

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Arithmetic coding is a lossless data compression algorithm used to encode a message into a smaller set of symbols. Unlike other data compression techniques, arithmetic coding operates on a stream of symbols rather than a block of data. This makes it particularly effective for compressing text and other forms of natural language.

In this article, we will explore the workings of arithmetic coding in detail. We will also discuss its advantages and disadvantages, as well as provide examples of its use.

How Arithmetic Coding Works

At a high level, arithmetic coding works by assigning each symbol in the message to a unique range of values between 0 and 1. These ranges are then combined to form a single value, which represents the entire message. To decode the message, the original ranges are reconstructed using the encoded value, and the symbols are recovered.

To illustrate this process, let's consider the following message:

HELLO WORLD

To encode this message using arithmetic coding, we first need to assign each symbol a range of values between 0 and 1. We can do this by using the frequency of each symbol in the message. The more frequently a symbol appears, the larger its range of values will be.

For example, the letter 'L' appears twice in the message, so it should have a larger range of values than the other letters. Let's say we assign the following ranges to each symbol:

H: [0.0, 0.1)
E: [0.1, 0.3)
L: [0.3, 0.5)
O: [0.5, 0.6)
W: [0.6, 0.7)
R: [0.7, 0.8)
D: [0.8, 0.9)

To encode the message, we then take the product of the ranges for each symbol. For example, the range for the first symbol 'H' is [0.0, 0.1), so its value is 0.1 - 0.0 = 0.1. We then multiply this value by the range for the next symbol 'E', which is [0.1, 0.3), giving us a new value of 0.02. We continue this process for each symbol in the message, multiplying the current value by the range for the next symbol.

At the end of this process, we have a single value that represents the entire message. In our example, the encoded value is approximately 0.415. To decode the message, we simply reverse this process, reconstructing the original ranges using the encoded value, and then recovering the symbols.

Arithmetic coding has several advantages over other data compression techniques:

1. Higher Compression Ratios: Arithmetic coding can achieve higher compression ratios than other techniques, such as Huffman coding or Lempel-Ziv-Welch (LZW) coding. This is because it operates on a stream of symbols rather than a block of data, allowing it to take advantage of the statistical properties of natural language.

2. No Need for a Codebook: Unlike Huffman coding and other techniques that use a codebook to encode symbols, arithmetic coding does not require a codebook. This can simplify the implementation of the algorithm and reduce the amount of memory required.

3. Simple Decoding: Decoding the encoded message is a simple process of reconstructing the original ranges using the encoded value and then recovering the symbols. This makes it easy to implement and efficient to decode.

1. Slower Encoding: Arithmetic coding can be slower than other techniques, such as Huffman coding or LZW coding. This is because it requires more computational resources to compute the ranges and the encoded value.

2. Susceptible to Errors: Arithmetic coding is highly sensitive to rounding errors and precision loss. This means that even small errors in the calculation of the ranges or the encoded value can result in significant changes in the decoded message.

3. Not Widely Used: Arithmetic coding is not as widely used as other data compression techniques, such as Huffman coding or LZW coding. This is due to its computational complexity and the potential for precision loss.

Example of Arithmetic Coding

Let's take a look at an example of how arithmetic coding can be used to compress text. Suppose we have the following message:

To be or not to be, that is the question

To compress this message using arithmetic coding, we first need to determine the frequency of each symbol in the message. We can use this information to assign each symbol a range of values between 0 and 1. The more frequently a symbol appears, the larger its range of values will be.

Using the frequency of each symbol in the message, we can assign the following ranges:

T: [0.0, 0.206)
,: [0.206, 0.341)
E: [0.341, 0.408)
O: [0.408, 0.429)
B: [0.429, 0.434)
R: [0.434, 0.464)
N: [0.464, 0.479)
T: [0.479, 0.685)
O: [0.685, 0.706)
B: [0.706, 0.711)
T: [0.711, 0.917)
H: [0.917, 0.921)
A: [0.921, 0.940)
I: [0.940, 0.949)
S: [0.949, 0.963)
Q: [0.963, 0.968)
U: [0.968, 0.974)
E: [0.974, 1.0)

To encode the message, we then take the product of the ranges for each symbol. For example, the range for the first symbol 'T' is [0.0, 0.206), so its value is 0.206 - 0.0 = 0.206. We then multiply this value by the range for the next symbol 'o', which is [0.685, 0.706), giving us a new value of approximately 0.144. We continue this process for each symbol in the message, multiplying the current value by the range for the next symbol.

At the end of this process, we have a single value that represents the entire message. In our example, the encoded value is approximately 0.00225. To decode the message, we simply reverse this process, reconstructing the original ranges using the encoded value and then recovering the symbols.

Conclusion

Arithmetic coding is a powerful data compression technique that can achieve high compression ratios. It operates on a stream of symbols, allowing it to take advantage of the statistical properties of natural language. While it has some disadvantages, such as slower encoding and sensitivity to errors, it remains a useful tool for data compression.