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Abstract
In this paper, a novel state-based dynamic multi-alphabet arithmetic coding algorithm which adapts efficiently to the locally occurring symbol statistics is presented. The proposed coding algorithm is applicable to sources such as raw images or transformed images that locally produce a smaller set of symbols from a large alphabet, or any other source characterized by very large alphabet size and highly skewed distributions. The performance of the proposed algorithm is compared with two standard entropy coding schemes, CA-2D-VLC and CABAC, that have recently appeared in the literature, in terms of compression ratio, peak signal-to-noise ratio and subjective quality. The simulation results obtained are encouraging, paving the way for further research and hardware implementation of this algorithm. It is found that the proposed algorithm achieves an increase in the compression ratio of about 45% over the compared standards for the similar peak signal-to-noise ratio and subjective quality.