The Study of Text Compression Algorithms and their Efficiencies Under Different Types of Files

As data volumes in the digital sphere increase exponentially, it has become imperative to develop efficient means of transmitting and storing this unprecedented volume of information. Data compression techniques offer an efficient solution to reduce data sizes under limited resources; their implementation has opened the way to various domains that address issues related to storage capacity and communications bandwidth. Comprehending all available compression algorithms can be daunting due to differing requirements. Therefore, it’s essential that performance evaluation of different algorithms takes place, with consideration given to compression ratio, compressed time and saved space as key performance indicators. While compression involves turning data into compact representations for storage purposes, decompression reverses this process and restores original formats of the information. As it is vitally important that no information or data are lost during compression and decompression processes, Huffman Coding (Run-length Encoding), H+R, Lempel-Ziv-Welch (Arithmetic Coding), Burrows-Wheeler Transform and Deflate are all employed here to compress text files without loss. In this paper we compare their respective algorithms such as Huffman Coding (Run-length Encoding), Lempel-Ziv-Welch (Arithmetic Coding), Burrows-Wheeler Transform and Deflate to discover their effectiveness; evaluation will include factors such as compressed file size as well as compression ratio in addition to time and space savings considerations.