JUCS - Journal of Universal Computer Science 10(9): 1199-1211, doi: 10.3217/jucs-010-09-1199
Fast Two-Stage Lempel-Ziv Lossless Numeric Telemetry Data Compression Using a Neural Network Predictor
expand article infoRajasvaran Logeswaran‡
‡ Multimedia University, Malaysia

Corresponding author: Rajasvaran Logeswaran ( loges@ieee.org )

© Rajasvaran Logeswaran.
This article is freely available under the J.UCS Open Content License.
Citation: Logeswaran R (2004) Fast Two-Stage Lempel-Ziv Lossless Numeric Telemetry Data Compression Using a Neural Network Predictor. JUCS - Journal of Universal Computer Science 10(9): 1199-1211. https://doi.org/10.3217/jucs-010-09-1199
Open Access
Abstract
Lempel-Ziv (LZ) is a popular lossless data compression algorithm that produces good compression performance, but suffers from relatively slow processing speed. This paper proposes an enhanced version of the Lempel-Ziv algorithm, through incorporation of a neural pre-processor in the popular predictor-encoder implementation. It is found that in addition to the known dramatic performance increase in compression ratio that multi-stage predictive techniques achieve, the results in this paper show that overall processing speed for the multi-stage scheme can increase by more than 15 times for lossless LZ compression of numeric telemetry data. The benefits of the proposed scheme may be expanded to other areas and applications.
Keywords
Lempel-Ziv, neural networks, prediction, lossless compression, two-stage