HMM Based Distributed Arithmetic Coding and Its Application in Image Coding

Distributed source coding was proved to be suitable to correlated sources environments. This paper intends to explore a new way to improve distributed arithmetic coding and apply it in image coding. The proposed method models the correlation between sources as a Hidden Markov Model process. The decoder uses Viterbi algorithm to get a better error rate. Adjacent lines of the image are regarded as correlated sources in image coding process. Experiments that compress synthetic and real image data are carried on and the result shows that Hidden Markov Model-based distributed arithmetic coding can get better code rate and lower frame error rate.