The Rate-Distortion-Perception-Classification Tradeoff: Joint Source
  Coding and Modulation via Inverse-Domain GANs
Junli Fang João F. C. Mota Baoshan Lu Weicheng Zhang Xuemin Hong
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.