Abstract
In digital communications, it is necessary to compress the data for a faster and more reliable transmission. As such, the data should undergo source encoding, also known as data compression, which is the process by which data are compressed into a fewer number of bits, before transmission. Also, source encoding is essential to limit file sizes for data storage. Two of the most common and most widely used source encoding techniques are the Huffman Algorithm and Lempel-Ziv Algorithm. The main objective of this research is to identify which technique is better in text, image and audio compression applications. The files for each data type were converted into bit streams using an analog-to-digital converter and pulse code modulation. The bit streams underwent compression through both compression algorithms and the efficiency of each algorithm is quantified by measuring their compression ratio for each data type.
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