An order-2 context model for data compression with reduced time and space requirements
- Author(s): Lelewer, Debra A.
- Hirschberg, Daniel S.
- et al.
Context modeling has emerged as the most promising new approach to compressing text. While context-modeling algorithms provide very good compression, they suffer from the disadvantages of being quite slow and requiring large amounts of main memory in which to execute. We describe a context-model-based algorithm that runs significantly faster and uses less space than earlier context models. Although our algorithm does not achieve the compression performance of competing context models, it does provide a significant improvement over the widely-used Unix utility compress in terms of both use of memory and compression performance.