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One situation in which this might not pose a problem is the distribution of large software packages. LZ77 works well here because the data only needs to be compressed once (at the production facility), and clients benefit from the considerably faster operation of uncompressing the data. On the other hand, suppose we are sending large amounts of data across a network interactively and would like to compress it before each transmission. In this case, for every transmission, we must compress data on one end of the connection and uncompress it on the other. Therefore, it is best to use Huffman coding. We may not achieve as much compression as with LZ77, but compressing and uncompressing together are faster.

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 benefit of adaptive Huffman coding is in using statistical models greater than the order-0 model described earlier. An order-0 model does not require much space, but the substantial space requirements of higher-order models make prepending a table impractical.

Arithmetic coding

A popular method of data compression that addresses the inaccuracies in Huffman coding brought about by entropies that are fractional values of bits. Arithmetic coding avoids this by encoding data as a single, very long floating-point value that can be uniquely decoded.

LZ78 ( Lempel-Ziv-1978) and LZW (Lempel-Ziv-Welch) compression

Variations of LZ77 that use more effective methods than a sliding window to keep track of previously seen phrases. Generally, each method uses some type of data structure for efficient searching, such as a hash table (Chapter 8), a binary tree (see Chapter 9), or a trie (see the related topics at the end of Chapter 9), and applies some unique approach to optimizing the process of encoding and decoding phrases.

Chapter 15. Data Encryption

Data encryption, or cryptography, is the science of secrecy. Its purpose is to keep information in the hands of those who should have it and out of the hands of those who should not. Considering such a statement, it probably comes as no surprise that cryptographic algorithms, called ciphers , historically have had profound political, social, and ethical implications. Data encryption, like data compression, is another product of information theory, an area of mathematics that addresses various ways to manage and manipulate information. Data encryption entails two processes: in one process we encipher recognizable data, called plaintext, into an unrecognizable form, called ciphertext ; in a second process we decipher the ciphertext back into the original plaintext. The main idea