Abstract: Compression algorithms reduce the redundancy in
data representation to decrease the storage required for that data.
Lossless compression researchers have developed highly
sophisticated approaches, such as Huffman encoding, arithmetic
encoding, the Lempel-Ziv (LZ) family, Dynamic Markov
Compression (DMC), Prediction by Partial Matching (PPM), and
Burrows-Wheeler Transform (BWT) based algorithms.
Decompression is also required to retrieve the original data by
lossless means. A compression scheme for text files coupled with
the principle of dynamic decompression, which decompresses only
the section of the compressed text file required by the user instead of
decompressing the entire text file. Dynamic decompressed files offer
better disk space utilization due to higher compression ratios
compared to most of the currently available text file formats.
Abstract: The purpose of this paper is to show efficiency and capability LZWµ in data compression. The LZWµ technique is enhancement from existing LZW technique. The modification the existing LZW is needed to produce LZWµ technique. LZW read one by one character at one time. Differ with LZWµ technique, where the LZWµ read three characters at one time. This paper focuses on data compression and tested efficiency and capability LZWµ by different data format such as doc type, pdf type and text type. Several experiments have been done by different types of data format. The results shows LZWµ technique is better compared to existing LZW technique in term of file size.