DecMac: A Deep Context Model for High Efficiency Arithmetic Coding
Qian Liu, Yiling Xu, Zhu Li · 2019
Conventional lossless compression techniques that use look up table method tend to be inefficient. We propose a deep context model, named DecMac, which combines a three-layer LSTM with adaptive arithmetic coding for lossless compression. In order to capture much more context information for better predicting, we introduce a cycle connection to preserve the end of hidden states and reuse it as the initial states for the next batch. We evaluate our method on the text compression task, resulting in averaged 25% compressed size reduction over the state of the art PAQ, and averaged 45% reduction over GZIP and ZIP.