Lossless Compression Algorithm Based on Context Tree

In order to deal with the context dilution problem introduced in the lossless compression of M-ary sources, a lossless compression algorithm based on a context tree model is proposed. By making use of the principle that conditioning reduces entropy, the algorithm constructs a context tree model to make use of the correlation among adjacent image pixels. Meanwhile, the M-ary tree is transformed into a binary tree to analyze the statistical information of the source in more details. In addition, the escape symbol is introduced to deal with the zero-frequency symbol problem when the model is used by an arithmetic encoder. The increment of the description length is introduced for the merging of tree nodes. The experimental results show that the proposed algorithm can achieve better compression results.

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