Burrows-Wheeler Transform, followed by Move-To-Front Transform, are often used transformation techniques in data compression. They may reduce the information entropy of the input sequence, which becomes more compressible in this way. This paper suggests an alternative, a Prediction-based Move-To-Front Transform, which may replace the aforementioned transformations. According to the context, consisting of a few already seen symbols, the Prediction-based Move-To-Front Transform selects an appropriate ordered domain of symbols to achieve a better match with the currently transforming symbol. Freeman chain code in four and eight directions, Three-Orthogonal chain code, and Vertex Chain Code were used for experiments. We confirmed that the proposed approach, when using an appropriate length of context, reduces the information entropy to a similar extent as the Burrows-Wheeler Transform followed by the Move-To-Front Transform on chain code data. Both approaches led to a very similar compression efficiency on 32 testing shapes when an arithmetic coder was used in the final stage. The proposed approach turned out to be more efficient when longer chain code sequences were used, obtained by merging all the testing chain codes of the same type.

在数据压缩中经常使用Burrows-Wheeler转换，然后是Move-To-Front转换。它们可以减少输入序列的信息熵，这种信息熵以这种方式变得更加可压缩。本文提出了一种替代方法，即基于预测的“移动到前端”变换，它可以替代上述变换。根据上下文，该上下文由一些已经看到的符号组成，基于预测的“移动到前端”变换选择符号的适当有序域，以实现与当前正在变换的符号的更好匹配。实验使用了四个方向和八个方向的Freeman链代码，三正交链代码和顶点链代码。我们确认，所建议的方法在使用适当长度的上下文时，降低信息熵的程度与对链码数据进行Burrows-Wheeler变换和随后的Move-To-Front变换类似。当在最后阶段使用算术编码器时，这两种方法都对32种测试形状产生了非常相似的压缩效率。当使用更长的链码序列（通过合并所有相同类型的测试链码获得）时，所提出的方法更有效。