We investigate a lossy source compression problem in which both the encoder and decoder are equipped with a pre-trained sequence predictor. We propose an online lossy compression scheme that, under a 0-1 loss distortion function, ensures a deterministic, per-sequence upper bound on the distortion (outage) level for any time instant. The outage guarantees apply irrespective of any assumption on the distribution of the sequences to be encoded or on the quality of the predictor at the encoder and decoder. The proposed method, referred to as online conformal compression (OCC), is built upon online conformal prediction--a novel method for constructing confidence intervals for arbitrary predictors. Numerical results show that OCC achieves a compression rate comparable to that of an idealized scheme in which the encoder, with hindsight, selects the optimal subset of symbols to describe to the decoder, while satisfying the overall outage constraint.

翻译：本研究探讨一种有损源压缩问题，其中编码器和解码器均配备预训练的序列预测器。我们提出一种在线有损压缩方案，在0-1损失失真函数下，该方案能确保任意时刻的失真（中断）水平具有确定性的逐序列上界。中断保证的成立不依赖于待编码序列分布的任何假设，也不依赖于编码器和解码器预测器的质量。所提出的方法称为在线保形压缩（OCC），其构建于在线保形预测——一种为任意预测器构建置信区间的新方法。数值结果表明，OCC实现的压缩率与一种理想化方案相当，在该理想化方案中，编码器在事后选择最优符号子集向解码器描述，同时满足整体中断约束。