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A Delay-Efficient Deep Learning Approach for Lossless Turbo Source Coding
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DOI：10.1109/TVT.2022.3155545.png)
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摘要
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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.
Keyword:
Encoding
Iterative decoding
Delays
Decoding
Source coding
Turbo codes
Neurons
Data compression
delay
neural networks
turbo code
期刊
IF:
7.1
论文数:
1.8W
被引数:
6.6W
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