Streamlining Context Models For Data Compression (0) [7 citations — 1 self]
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author = {Debra Lelewer and Daniel S. Hirschberg},
title = {Streamlining Context Models For Data Compression},
booktitle = {in Proc. Data Compression Conference},
year = {},
pages = {313--322},
publisher = {IEEE Press}
}
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Abstract:
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 slow and requiring large amounts of main memory in which to execute. We describe a context-model-based algorithm that runs significantly faster, uses much less space, and provides compression ratios close to those of earlier context modeling algorithms. We achieve these improvements through the use of self-organizing lists. Introduction The most widely used data compression algorithms, including the Unix utility compress, are based on the work of Ziv and Lempel [ZL78]. These are dynamic algorithms that build a dictionary representative of the input text and code dictionary entries using fixed-length codewords. Compress typically reduces a file to approximately 50% of its original size and is extremely fast, but has a large memory requirement (450 Kbytes). Algorithm FG, an updated version of t...
Citations
| 515 | Compression of individual sequences via variable-rate coding – Ziv, Lempel - 1978 |
| 55 | Data compression with finite windows – Fiala, Greene - 1989 |
| 10 | A Double-adaptive File Compression Algorithm – Langdon, Rissanen - 1983 |
| 7 | An adaptive dependency source model for data compression – Abrahamson - 1989 |
| 1 | An order-2 context model for data compression with reduced time and space requirements – Lelewer, Hirschberg - 1990 |
