A Delay-Efficient Deep Learning Approach for Lossless Turbo Source Coding

doi:10.1109/TVT.2022.3155545

A Delay-Efficient Deep Learning Approach for Lossless Turbo Source Coding

2022-06-01

2

PRE

AI

DOI：10.1109/TVT.2022.3155545

Original

Original request for help

Save

Abstract

En 中文

Lossless turbo source coding with decremental/incremental redundancy is a variable-length source coding scheme which employs turbo codes for data compression. Although the scheme offers low compression rates and lends itself to joint source-channel coding, it suffers from a large delay in the encoding phase. The delay is imposed by several tentative encoding-decoding procedures performed at the encoder to search for the minimum compression length. In this work, we apply machine learning to provide a highly accurate estimate of the proper compression length. The encoder starts its search from this estimated length, thus the delay of turbo source coding will decrease considerably. The preliminary results show a four-fold reduction in the encoding delay at the expense of a negligible increase in the compression rate.

Keywords:

Encoding

Iterative decoding

Delays

Decoding

Source coding

Turbo codes

Neurons

Data compression

delay

neural networks

turbo code

A Delay-Efficient Deep Learning Approach for Lossless Turbo Source Coding

Abstract

Journal

Organization