众所周知，基于上下文建模的算术编码是JPEG2000标准中的关键技术，而对于重要性编码，JPEG2000中采用的上下文模型是码块内的8邻域系数，由于其根据经验将所有上下文分成9类，且所有比特平面都使用这套固定的上下文分类方案。因此针对这种上下文分类方案的不足，对JPEG2000中重要性编码及其上下文建模方式进行了改进，即首先建立比原来的3×3更大的上下文模型；然后提出了一种基于重要上下文的扫描方式；最后基于新的扫描方式根据最小码长的准则进行上下文量化的优化，考虑到不同子扫描之间的统计差异，对不同的扫描子过程使用不同的上下文模型方案。实验结果表明，与JPEG2000的无损压缩相比，新的重要性编码算法的无损压缩结果（平均比特率）较JPEG2000提高了1.312％。

Context based arithmetic coding is the key component of the new JPEG2000 standard. For significance coding, JPEG2000 designs empirically context model using eight adjacent coefficients and classifies all context events into 9 conditional contexts and every bit plane use the same context classification maps. This paper addresses the improvement of significance coding and context modeling for significance coding in the JPEG2000 which enlarges the context template which was 3×3 square region in JPEG2000.We propose a new scan method based on significant context and exploit optimization techniques for context quantization by minimum description length considering the difference of statistic among sub scans.Finally, we establish different context models for different sub scans. Our experimental results show that the lossless compression performance of our scheme is improved 1.312% upon that of JPEG2000.