We consider image transmission via deep joint source-channel coding (DeepJSCC) over multi-hop additive white Gaussian noise (AWGN) channels by training a DeepJSCC encoder-decoder pair with a pre-trained deep hash distillation (DHD) module to semantically cluster images, facilitating security-oriented applications through enhanced semantic consistency and improving the perceptual reconstruction quality. We train the DeepJSCC module to both reduce mean square error (MSE) and minimize cosine distance between DHD hashes of source and reconstructed images. Significantly improved perceptual quality as a result of semantic alignment is illustrated for different multi-hop settings, for which classical DeepJSCC may suffer from noise accumulation, measured by the learned perceptual image patch similarity (LPIPS) metric.

翻译：本文研究通过多跳加性高斯白噪声信道进行图像传输的深度联合信源信道编码方法。我们训练一个DeepJSCC编码器-解码器对，并引入预训练的深度哈希蒸馏模块对图像进行语义聚类，通过增强语义一致性以支持安全导向的应用，同时提升感知重建质量。训练DeepJSCC模块时，我们同时优化源图像与重建图像之间的均方误差，并最小化其深度哈希蒸馏特征间的余弦距离。实验表明，在不同多跳传输场景下，由于实现了语义对齐，感知质量得到显著提升——这通过学习感知图像块相似度指标进行衡量。相比之下，经典DeepJSCC方法在这些场景中可能因噪声累积而导致性能下降。