Distributed Arithmetic Coding for Sources with Hidden Markov Correlation

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.