Stationary model of probabilities for symbols emitted by bitplane image coders

Context-adaptive binary arithmetic coding (CABAC) is a popular approach to diminish the statistical redundancy of symbols emitted by bitplane image coders. The main idea behind CABAC is to set up appropriate context models for coefficients, and to adapt probability estimates for each context to the nonstationary statistical behavior of symbols as more data are coded. This works introduces a mathematical model to determine probability estimates conceived from a characterization of the signal's nature within wavelet subbands. The proposed model assumes stationary statistical behavior for emitted symbols, thus the context-adaptive process carried out by CABAC is avoided. Experimental results in the framework of JPEG2000 suggest 2% increment on coding efficiency.