Neural compression has the potential to revolutionize lossy image compression. Based on generative models, recent schemes achieve unprecedented compression rates at high perceptual quality but compromise semantic fidelity. Details of decompressed images may appear optically flawless but semantically different from the originals, making compression errors difficult or impossible to detect. We explore the problem space and propose a provisional taxonomy of miscompressions. It defines three types of 'what happens' and has a binary 'high impact' flag indicating miscompressions that alter symbols. We discuss how the taxonomy can facilitate risk communication and research into mitigations.

翻译：神经压缩有望彻底革新有损图像压缩技术。基于生成模型的最新方案在保持高感知质量的同时实现了前所未有的压缩率，但牺牲了语义保真度。解压缩图像的细节可能在光学上完美无瑕，但在语义层面与原始图像存在差异，使得压缩误差难以甚至无法被检测。本文系统探索了该问题空间，提出了一种暂行的误压缩分类学。该分类体系定义了三种“发生机制”类型，并设置二元“高影响”标志来标识改变符号的误压缩现象。我们进一步探讨了该分类学如何促进风险沟通与缓解措施研究。