Joint source-channel coding schemes based on deep neural networks (DeepJSCC) have recently achieved remarkable performance for wireless image transmission. However, these methods usually focus only on the distortion of the reconstructed signal at the receiver side with respect to the source at the transmitter side, rather than the perceptual quality of the reconstruction which carries more semantic information. As a result, severe perceptual distortion can be introduced under extreme conditions such as low bandwidth and low signal-to-noise ratio. In this work, we propose CommIN, which views the recovery of high-quality source images from degraded reconstructions as an inverse problem. To address this, CommIN combines Invertible Neural Networks (INN) with diffusion models, aiming for superior perceptual quality. Through experiments, we show that our CommIN significantly improves the perceptual quality compared to DeepJSCC under extreme conditions and outperforms other inverse problem approaches used in DeepJSCC.

翻译：基于深度神经网络的联合源信道编码方案（DeepJSCC）近年来在无线图像传输中取得了显著性能。然而，这类方法通常仅关注接收端重建信号相对于发送端源信号的失真度，而非承载更多语义信息的重建感知质量。因此，在低带宽和低信噪比等极端条件下，可能引入严重的感知失真。本文提出CommIN方法，将基于降质重建恢复高质量源图像的问题视为逆问题。为此，CommIN结合可逆神经网络（INN）与扩散模型，旨在获得卓越的感知质量。实验表明，在极端条件下，我们的CommIN相比DeepJSCC显著提升了感知质量，并优于DeepJSCC中使用的其他逆问题方法。