Machine learning has had a major impact on data compression over the last decade and inspired many new, exciting theoretical and applied questions. This paper describes one such direction -- relative entropy coding -- which focuses on constructing stochastic codes, primarily as an alternative to quantisation and entropy coding in lossy source coding. Our primary aim is to provide a broad overview of the topic, with an emphasis on the computational and practical aspects currently missing from the literature. Our goal is threefold: for the curious reader, we aim to provide an intuitive picture of the field and convince them that relative entropy coding is a simple yet exciting emerging field in data compression research. For a reader interested in applied research on lossy data compression, we provide an account of the most salient contemporary applications. Finally, for the reader who has heard of relative entropy coding but has never been quite sure what it is or how the algorithms fit together, we hope to illustrate how simple and elegant the underlying constructions are.

翻译：机器学习在过去十年中对数据压缩领域产生了重大影响，并激发了许多新颖且令人兴奋的理论与应用问题。本文描述了其中一个方向——相对熵编码，该方向主要关注构建随机码，作为有损信源编码中量化与熵编码的替代方案。我们的主要目标是提供该主题的广泛概述，并着重阐述当前文献中缺失的计算与实践层面。我们的目标有三重：对于感兴趣的读者，我们旨在呈现该领域的直观图景，并使其相信相对熵编码是数据压缩研究中一个简单而令人兴奋的新兴领域；对于关注有损数据压缩应用研究的读者，我们阐述了当前最突出的实际应用案例；最后，对于曾听闻相对熵编码但始终不确定其具体内涵或算法如何协同工作的读者，我们希望展示其底层构建原理的简洁与优雅。