It is well-known in the field of lossless data compression that probabilistic next-symbol prediction can be used to compress sequences of symbols. Deep neural networks are able to capture rich dependencies in data, offering a powerful means of estimating these probabilities and hence an avenue towards more effective compression algorithms. However, both compressor and decompressor must have exactly matching predictions; even small non-deterministic differences (which often happen with learned models due to hardware, software, or computation order) can lead to cascading decoding failures. In this paper, we formalize the problem of prediction mismatch in model-driven compression, and introduce Probability Matching Interval Coding (PMATIC), a model-agnostic algorithm that tolerates bounded prediction mismatch with low overhead. PMATIC works with the predicted probabilities, making it compatible as a drop-in replacement for the arithmetic encoder in model-driven compression tools. We show theoretical correctness and performance bounds for PMATIC, and validate these results on text data. These results confirm that, when paired an advanced prediction model, PMATIC is robust to prediction mismatch while achieving compression rates that out-perform standard modern compression tools.

翻译：在无损数据压缩领域，利用概率性下一符号预测进行符号序列压缩是众所周知的技术。深度神经网络能够捕捉数据中丰富的依赖关系，为概率估计提供了强大手段，从而为实现更高效的压缩算法开辟了途径。然而，压缩器与解压器必须具有完全匹配的预测结果；即使是微小的非确定性差异（在基于学习模型的系统中常因硬件、软件或计算顺序差异而产生）也可能导致级联的解码失败。本文形式化定义了模型驱动压缩中的预测失配问题，并提出了一种模型无关的算法——概率匹配区间编码（PMATIC），该算法能够以较低开销容忍有界的预测失配。PMATIC基于预测概率工作，可作为即插即用组件替代模型驱动压缩工具中的算术编码器。我们证明了PMATIC的理论正确性与性能边界，并在文本数据上验证了这些结果。实验表明，当与先进的预测模型结合时，PMATIC在保持优于标准现代压缩工具压缩率的同时，对预测失配具有鲁棒性。