1. huffman coding
about five bits per character
2. Ziv-Lempel coding和Arithmetic Coding都是使用自适应的压缩方式
其中Arithmetic Coding更基础,它可以使一类基于它的自适应的压缩方法得以实现。
Ziv-Lempel about four bits per character
Arithmetic Coding over two bits per character.-->压缩和解压都会慢,同时内存也会多。
3. PPM(Prediction by Partial matching)是基于Arithmetic Coding的。
4. 压缩方法可以分类两类: 符号方法(symbolwise methods)和字典方法(dictionary methods)。
Symbolwise methods are usually based on either Huffman coding or arithmetic coding, and they
differ mainly in how they estimate probabilities for symbols。
Dictionary methods generally use quite simple representations to code references to
entries in the dictionary。The most significant dictionary methods are based on
Ziv-Lempel coding,which use the idea of replacing strings of characters with a reference
to a previous occurrence of the string。
2.2 Adaptive models The method that always uses the same model regardless of what text is being coded is called static modeling. One solution is to generate a model specially for each file that is to be compressed. An initial pass is made through the file to estimate symbol probabilities, and these are transmitted to the decoder before transmitting the encoded symbols.This approach is called semi-static modeling. Adaptive modeling is an elegant solution to these problems. An adaptive model begins with a bland probability distribution and gradually alters it as more symbols are encountered. Zero-frequency problem solution: One is to allow one extra count, which is divided evenly among any symbols that have not been obaserved int the input. Another possibility is to artificially inflate the count of ervery character in the alphabet by one, thereby ensuring that none has a zero frequency. 2.3 Huffman coding Coding is the task of determining the output representation of a symbol, based on a probability distribution supplied by a model.
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