Lzw compression example pdf

Dictionary based algorithms scan a file for sequences of data that occur more than once. These sequences are then stored in a dictionary and within the compressed file, references are put where-ever repetitive data occurred. Lempel and Ziv published a series of papers describing various compression algorithms. Their first algorithm was published in , hence its name: LZ This compression algorithm maintains its dictionary within the data themselves.

Suppose you want to compress the following string of text: the quick brown fox jumps over the lazy dog. In , Lempel and Ziv published a second paper outlining a similar algorithm that is now referred to as LZ This algorithm maintains a separate dictionary. Suppose you once again want to compress the following string of text: the quick brown fox jumps over the lazy dog. In , Terry Welch was working on a compression algorithm for high-performance disk controllers. He developed a rather simple algorithm that was based on the LZ78 algorithm and that is now called LZW.

LZW compression replaces strings of characters with single codes. It does not do any analysis of the incoming text. Instead, it just adds every new string of characters it sees to a table of strings. Compression occurs when a single code is output instead of a string of characters.

The code that the LZW algorithm outputs can be of any arbitrary length, but it must have more bits in it than a single character. The first codes when using eight-bit characters are by default assigned to the standard character set. The remaining codes are assigned to strings as the algorithm proceeds. The sample program runs as shown with twelve-bit codes. This means codes refer to individual bytes, while codes refer to substrings. Codes in the code table are always assigned to represent single bytes from the input file.

When encoding begins the code table contains only the first entries, with the remainder of the table being blanks. Compression is achieved by using codes through to represent sequences of bytes. As the encoding continues, LZW identifies repeated sequences in the data and adds them to the code table.

Decoding is achieved by taking each code from the compressed file and translating it through the code table to find what character or characters it represents. Typically, every character is stored with 8 binary bits, allowing up to unique symbols for the data. This algorithm tries to extend the library to 9 to 12 bits per character. The new unique symbols are made up of combinations of symbols that occurred previously in the string. It does not always compress well, especially with short, diverse strings.

But is good for compressing redundant data, and does not have to save the new dictionary with the data: this method can both compress and uncompress data. Implementation The idea of the compression algorithm is the following: as the input data is being processed, a dictionary keeps a correspondence between the longest encountered words and a list of code values.

The words are replaced by their corresponding codes and so the input file is compressed. Therefore, the efficiency of the algorithm increases as the number of long, repetitive words in the input data increases. There is another variation of 6 different versions here. Also, Rosettacode lists several implementations of LZW in different languages. It starts with the first table entries initialized to single characters.

The string table is updated for each character in the input stream, except the first one. Decoding is achieved by reading codes and translating them through the code table being built.

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