Distributed Arithmetic Coding for Sources with Hidden Markov Correlation | IEEE Conference Publication | IEEE Xplore

Distributed Arithmetic Coding for Sources with Hidden Markov Correlation


Abstract:

Distributed arithmetic coding (DAC) has been shown to be effective for Slepian- Wolf coding, especially for short data blocks. In this paper, we propose to use the DAC to...Show More

Abstract:

Distributed arithmetic coding (DAC) has been shown to be effective for Slepian- Wolf coding, especially for short data blocks. In this paper, we propose to use the DAC to compress memory-correlated sources. More specifically, the correlation between sources is modeled as a hidden Markov process. Because image pixels are correlated, the image is modeled as a hidden Markov source then DAC compression is implemented, and forward algorithms are embedded in the decoding process. Experimental results show that the performance is close to the theoretical Slepian- Wolf limit. When the image is used as a hidden Markov source for DAC compression, it shows lower error rate.
Date of Conference: 14-17 December 2021
Date Added to IEEE Xplore: 03 February 2022
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Conference Location: Tokyo, Japan

References

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