Mastering Algorithms with C

Related Topics

Lossy compression

A broad class of approaches to data compression that do not produce an exact copy of the original data when the data is uncompressed. Lossy compression is useful primarily in graphics and sound applications, where a certain loss of accuracy is acceptable in exchange for greater compression ratios, provided the degradation is carefully managed.

Statistical modeling

The engine behind data compression methods based on minimum redundancy coding. This chapter worked with an order-0 model, which simply determines the probability of any one symbol occurring in the data. Higher-order models look at the probabilities associated with combinations of symbols to get a more accurate determination of the data’s entropy. For example, if we encounter the symbol “Q” in text data, in many languages the probability is high that the next symbol will be “U.” Higher-order models take considerations like this into account.

Shannon-Fano coding

The first form of minimum redundancy coding. Interestingly, it came about in the 1940s, apart from computers, as a result of experiments in information theory during World War II. Shannon-Fano coding is similar to Huffman coding, but it builds its tree from the top down instead of the bottom up.

Adaptive Huffman coding

A variation of Huffman coding that does not require that the table of frequencies be passed along with the compressed data. Instead, a statistical model is adapted as the data is compressed and uncompressed. The main ...