Adaptive lossless data compression algorithms with new approaches and modeling techniques

In many applications involving storage and transmission of data there is an explicit need for data compression. The aim of "data compression" is to convert information to new representation in the most efficient way possible. The information can be in many forms, such as text, voice, images, videos, and any other types of data which can be transmitted from one place to another or stored in one place.

We have considered lossless data compression techniques, where the data can be reconstructed uniquely and identically in absence of channel noise. We have introduced improvements on the existing modeling techniques such as LZW and CTW and have also introduced some new modeling techniques, such as Switching algorithm, Difference modeling technique, and Grouping algorithm.

All lossless data compression algorithms use some kind of prediction technique to overcome the zero frequency problem. In this aspect, two new prediction techniques have been presented and explored.