基于分段模型的信源容错译码算法

Subsection Model Based Error-resilient Decoding Algorithm for Source Coding

针对无损信源编码存在误码扩散的问题,建立了以最大后验概率估计为基础的信源序列分段译码模型,设计了基于统计模型的容错译码算法。该算法充分利用了信源编码数据的残留冗余,较好地消除了无损压缩数据对误码的敏感性,为文本压缩数据的容错译码提供了新思路。实验结果表明,该算法具有纠正信源数据中误码的能力,能够显著减少信息损失。

Aiming at the error code diffusion problems of lossless source coding,a subsection decoding model for source sequence was constructed based on MAP(Maximum A Posteriori),and then a error-resilient decoding algorithm based on statistical model was proposed.The algorithm makes full use of the residual redundancy of source coding data,overcomes the sensitive characteristics of lossless data for error code well,and provides a new solution for error-resilient decoding of text compression data.Experimental results show that this algorithm has the ability of correcting errors in the source data and significantly reduce the information loss.