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Joint source and channel coding (JSCC) for image transmission has attractedincreasing attention due to its robustness and high efficiency. However, theexisting deep JSCC research mainly focuses on minimizing the distortion betweenthe transmitted and received information under a fixed number of availablechannels. Therefore, the transmitted rate may be far more than its requiredminimum value. In this paper, an adaptive information bottleneck (IB) guidedjoint source and channel coding (AIB-JSCC) method is proposed for imagetransmission. The goal of AIB-JSCC is to reduce the transmission rate whileimproving the image reconstruction quality. In particular, a new IB objectivefor image transmission is proposed so as to minimize the distortion and thetransmission rate. A mathematically tractable lower bound on the proposedobjective is derived, and then, adopted as the loss function of AIB-JSCC. Totrade off compression and reconstruction quality, an adaptive algorithm isproposed to adjust the hyperparameter of the proposed loss function dynamicallyaccording to the distortion during the training. Experimental results show thatAIB-JSCC can significantly reduce the required amount of transmitted data andimprove the reconstruction quality and downstream task accuracy.

Adaptive Information Bottleneck Guided Joint Source and Channel Coding for Image Transmission