Data compression is based on the principle that the majority of data is known to be using more bits than its entropy indicates is optimal. Recall that entropy is a term used to specify the information that the data carries. It means that a more optimal bit representation of the same information is possible. Exploring and formulating more efficient bit representation becomes the basis for devising compression algorithms. Lossless data compression takes advantage of this redundancy to compress data without losing any information. In the late '80s, Ziv and Lempel proposed dictionary-based data compression techniques that can be used to implement lossless data compression. These techniques were an instant hit due to their speed and good compression rate. These techniques were used to create the popular Unix-based compress tool. Also, the ubiquitous gif image format uses these compression techniques, which proved to be popular as they could be used to represent the same information in a lesser number of bits, saving space and communication bandwidth. These techniques later became the basis of developing the zip utility and its variants. The compression standard, V.44, used in modems is also based on it.

Let's now look at the techniques one by one in the upcoming sections.