用语义对齐提升多跳图像传输的视觉质量
Multi-hop Deep Joint Source-Channel Coding with Deep Hash Distillation for Semantically Aligned Image Recovery
- 引入预训练哈希蒸馏模块，实现图像语义聚类
- 联合优化重建误差与哈希余弦距离，提升感知质量
- 适合需要高保真视觉恢复的多跳通信场景
针对多跳加性白高斯噪声（AWGN）信道下的图像传输问题，本文采用深度联合源信道编码（DeepJSCC）框架，并结合预训练的深度哈希蒸馏（DHD）模块，对图像进行语义聚类，以增强语义一致性并提升感知重建质量。通过联合优化均方误差（MSE）和源图与重建图在DHD哈希空间中的余弦距离，实现了在不同多跳设置下的显著感知质量提升。相较于传统DeepJSCC因噪声累积导致性能下降的情况，本方法在学习型感知图像块相似性（LPIPS）指标上表现更优。
原文摘要 · Abstract (English)
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.
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