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.

翻译：图像传输中的联合源信道编码（JSCC）因其鲁棒性和高效性受到广泛关注。然而，现有深度JSCC研究主要侧重于在固定可用信道数量下最小化传输信息与接收信息之间的失真，导致传输速率可能远超所需最小值。本文提出一种自适应信息瓶颈（IB）引导的联合源信道编码方法（AIB-JSCC）用于图像传输，其目标是在提升图像重建质量的同时降低传输速率。具体而言，我们提出了针对图像传输的新IB目标函数，以同时最小化失真与传输速率。推导出该目标函数的数学可处理下界，并将其作为AIB-JSCC的损失函数。为平衡压缩与重建质量，提出自适应算法，在训练过程中根据动态失真调整损失函数的超参数。实验结果表明，AIB-JSCC能够显著减少所需传输数据量，并提升图像重建质量及下游任务精度。