We introduce deep learning based communication methods for successive refinement of images over wireless channels. We present three different strategies for progressive image transmission with deep JSCC, with different complexity-performance trade-offs, all based on convolutional autoencoders. Numerical results show that deep JSCC not only provides graceful degradation with channel signal-to-noise ratio (SNR) and improved performance in low SNR and low bandwidth regimes compared to state-of-the-art digital communication techniques, but can also successfully learn a layered representation, achieving performance close to a single-layer scheme. These results suggest that natural images encoded with deep JSCC over Gaussian channels are almost successively refinable.
@article{arxiv.1903.06333,
title = {Successive Refinement of Images with Deep Joint Source-Channel Coding},
author = {David Burth Kurka and Deniz Gunduz},
journal= {arXiv preprint arXiv:1903.06333},
year = {2019}
}