A Historical Perspective on Approaches to Data Compression

: Several data compression algorithms are investigated in this study. Data compression is commonly utilized in the community. Because data compression allows us to conserve storage space, it can also assist to speed up data transport from one point to another. It is vital to have a compression tool on hand when compressing from one person to another. This method can be used to make data smaller. In addition to text data, images and video may be saved. Lossy and non-lossy compressions are the two types of compression techniques. Compression (lossless) and compression (lossy) which is, nevertheless, the most widely used? It is necessary to conduct lossless compression. Huffman, Shannon Fano, and other lossless compression techniques, as well as Tunstall, Lempel, Ziv Welch, and run-length encoding, are all instances of runlength encoding. This article explains how a compression strategy works and which approach is most typically used in data compression. A form of compression is text compression. The consequences of this process may be seen in the compressed file size, which is less than the original file. In this article, many data compression techniques are surveyed, including those developed by Shannon, Fano, and Huffman. Data compression seeks to increase active data density by minimizing redundant information in data that is stored or sent. Storage and distributed systems are two domains where data compression is crucial. Information theory ideas are thoroughly examined in relation to the objectives and assessment of data compression techniques. The algorithms that are presented are subjected to a framework that is created for the evaluation and comparison of approaches.