Context-adaptive binary arithmetic coding with precise probability estimation and complexity scalability for high-efficiency video coding

Abstract

An improved context-based adaptive binary arithmetic coding (CABAC) is presented. The idea for the improvement is to use a more accurate mechanism for estimation of symbol probabilities in the standard CABAC algorithm. The authors’ proposal of such a mechanism is based on the context-tree weighting technique. In the framework of a high-efficiency video coding (HEVC) video encoder, the improved CABAC allows 0.7% to 4.5% bitrate saving compared to the original CABAC algorithm. The application of the proposed algorithm marginally affects the complexity of HEVC video encoder, but the complexity of video decoder increases by 32% to 38%. In order to decrease the complexity of video decoding, a new tool has been proposed for the improved CABAC that enables scaling of the decoder complexity. Experiments show that this tool gives 5% to 7.5% reduction of the decoding time while still maintaining high efficiency in the data compression.