  Joint source and channel coding (JSCC) for image transmission has attracted
increasing attention due to its robustness and high efficiency. However, the
existing deep JSCC research mainly focuses on minimizing the distortion between
the transmitted and received information under a fixed number of available
channels. Therefore, the transmitted rate may be far more than its required
minimum value. In this paper, an adaptive information bottleneck (IB) guided
joint source and channel coding (AIB-JSCC) method is proposed for image
transmission. The goal of AIB-JSCC is to reduce the transmission rate while
improving the image reconstruction quality. In particular, a new IB objective
for image transmission is proposed so as to minimize the distortion and the
transmission rate. A mathematically tractable lower bound on the proposed
objective is derived, and then, adopted as the loss function of AIB-JSCC. To
trade off compression and reconstruction quality, an adaptive algorithm is
proposed to adjust the hyperparameter of the proposed loss function dynamically
according to the distortion during the training. Experimental results show that
AIB-JSCC can significantly reduce the required amount of transmitted data and
improve the reconstruction quality and downstream task accuracy.
