  The joint source-channel coding (JSCC) framework leverages deep learning to
learn from data the best codes for source and channel coding. When the output
signal, rather than being binary, is directly mapped onto the IQ domain
(complex-valued), we call the resulting framework joint source coding and
modulation (JSCM). We consider a JSCM scenario and show the existence of a
strict tradeoff between channel rate, distortion, perception, and
classification accuracy, a tradeoff that we name RDPC. We then propose two
image compression methods to navigate that tradeoff: the RDPCO algorithm which,
under simple assumptions, directly solves the optimization problem
characterizing the tradeoff, and an algorithm based on an inverse-domain
generative adversarial network (ID-GAN), which is more general and achieves
extreme compression. Simulation results corroborate the theoretical findings,
showing that both algorithms exhibit the RDPC tradeoff. They also demonstrate
that the proposed ID-GAN algorithm effectively balances image distortion,
perception, and classification accuracy, and significantly outperforms
traditional separation-based methods and recent deep JSCM architectures in
terms of one or more of these metrics.
