基于上下文的自适应二进制算术编码（context-based adaptive binary coding，CABAC）是一种高效的熵编码方法，但是其高计算复杂度制约了该算法的编码速度，已成为其应用的一个主要瓶颈。为解决此问题，在分析CABAC算法及其计算复杂度的基础上，对其概率估计更新部分进行了改进，提出了一种提高其编码速度的有效方法。该方法首先以N个符号的包为单位进行编码，仅每个包编码完成后再进行一次概率估计的更新，因而成倍地降低了概率估计更新的频度。实验数据表明，该方法较以往的方法使CABAC的编码速度平均提高了133%~307%，同时编码效率平均下降187%~298%。

Context-based adaptive binary arithmetic coding (CABAC) is a highly efficient entropy coding way, but its coding speed is restricted by high computational complexity, which becomes a major bottleneck in its application To solve this problem, an effective way, based on the analysis of the CABAC algorithm and its computational complexity, will be proposed in this article to improve the algorithms coding speed by improving the probability estimation update part The algorithm first codes packets which each include N symbols, and then updates the probability estimation part, which decreases the update frequency of probability estimation remarkably The experimental results show that, compared with the former algorithms, the coding speed of CABAC has been substantially increased from 133% to 307%, with coding efficiency declines a little from 187% to 298%