Turbo source coding: a noise-robust approach to data compression

Abstract

Summary form only given. All traditional data compression techniques, such as Huffman coding, the Lempel-Ziv algorithm, run-length limited coding, Tunstall coding and arithmetic coding are highly susceptible to residual channel errors and noise. We have previously proposed the use of parallel concatenated codes and iterative decoding for fixed-length to fixed-length source coding, i.e., turbo coding for data compression purposes. The work presented here extends these results and also considers the case when decompression must be done from compressed data corrupted by additive white Gaussian noise (AWGN).