State-based dynamic multi-alphabet arithmetic coding for image compression

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.

Ratio‐modified block truncation coding algorithms for reduced bitrates

“…The values of X avg and ratio values are decoded using the decoding algorithm described in the state-based dynamic multialphabet arithmetic coding. 9 Step 2. Using X avg and ratio, X Lavg is computed.…”

“…However, in the case of raw images or transformed images that locally produce a smaller set of symbols from a large alphabet, the conventional arithmetic coding 8 does not adapt efficiently to the locally occurring symbol statistics. A novel state-based dynamic multi-alphabet arithmetic coding algorithm that adapts efficiently to the locally occurring symbol statistics is also available 9 where the probabilities of locally occurring symbols instead of global symbol probabilities are used for coding.…”