“…Over the last decade, arithmetic coding has emerged as an important compression tool.
It is now the method of choice for adaptive coding on multi-symbol alphabets because of its speed, low
storage requirements, and efficiency of compression.” [The University of Melbourne]
“…Most of the data compression methods in common use today fall into one of two
camps: dictionary based schemes and statistical methods. In the world of small systems, dictionary
based data compression techniques seem to be more popular at this time. However, by combining
arithmetic coding with powerful modeling techniques, statistical methods for data compression
can actually achieve better performance.” [Mark Nelson - Dogma.net]
“…Arithmetic coding, is entropy coder widely used, the only problem is it's
speed, but compression tends to be better than Huffman can achieve. ” [Arturo San Emeterio Campos]
“…So far, this makes Arithmetic Coding sound very similar to Huffman coding.
However, there is an important difference. An arithmetic encoder doesn't have to use an integral
number of bits to encode a symbol. If the optimal number of bits for a symbol is 2.4, a Huffman
coder will probably use 2 bits per symbol, whereas the arithmetic encoder my use very close to 2.4.
This means an arithmetic coder can usually encode a message using fewer bits.” [Mark Nelson - DataCompression.info]
