The joint source coding and modulation (JSCM) framework was enabled by recent developments in deep learning, which allows to automatically learn from data, and in an end-to-end fashion, the best compression codes and modulation schemes. In this paper, we show the existence of a strict tradeoff between channel rate, distortion, perception, and classification accuracy in a JSCM scenario. We then propose two image compression methods to navigate that tradeoff: an inverse-domain generative adversarial network (ID-GAN), which achieves extreme compression, and a simpler, heuristic method that reveals insights about the performance of ID-GAN. Experiment results not only corroborate the theoretical findings, but also demonstrate that the proposed ID-GAN algorithm significantly improves system performance compared to traditional separation-based methods and recent deep JSCM architectures.

翻译：联合信源编码与调制（JSCM）框架得益于深度学习的最新进展，能够以端到端的方式从数据中自动学习最优压缩编码与调制方案。本文揭示了JSCM场景中信道速率、失真、感知质量与分类准确率之间存在严格的权衡关系。为此，我们提出两种图像压缩方法来驾驭这一权衡：一种实现极端压缩的逆域生成对抗网络（ID-GAN），以及一种能揭示ID-GAN性能机理的简易启发式方法。实验结果不仅验证了理论发现，更表明与传统分离式方法及近期深度JSCM架构相比，所提ID-GAN算法显著提升了系统性能。