The US patent that Unisys held expired in June , and patents in other parts of the world had all expired by July 7, Unisys currently holds and has patents pending on improvements to the LZW compression algorithm.
This is document aghf in the Knowledge Base. Last modified on Skip to: content search login. Knowledge Base Toggle local menu Menus About the team. 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. Lossless compression reduces bits by identifying and eliminating statistical redundancy. No information is lost in lossless compression. On the other hand, Lossy compression reduces bits by removing unnecessary or less important information.
So we need Data Compression mainly because: Attention reader! Uncompressed data can take up a lot of space, which is not good for limited hard drive space and internet download speeds. While hardware gets better and cheaper, algorithms to reduce data size also help technology evolves. How can we fit a two-hour film on a 25 GB Blu-ray disc? The LZW algorithm is a very common compression technique. It is lossless, meaning no data is lost when compressing. The algorithm is simple to implement and has the potential for very high throughput in hardware implementations.
It is the algorithm of the widely used Unix file compression utility compress and is used in the GIF image format. The Idea relies on reoccurring patterns to save data space. LZW is the foremost technique for general-purpose data compression due to its simplicity and versatility. How does it work? LZW compression works by reading a sequence of symbols, grouping the symbols into strings, and converting the strings into codes.
Because the codes take up less space than the strings they replace, we get compression. Characteristic features of LZW includes, LZW compression uses a code table, with as a common choice for the number of table entries. 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.
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