We consider low-latency image transmission over a noisy wireless channel when
correlated side information is present only at the receiver side (the Wyner-Ziv
scenario). In particular, we are interested in developing practical schemes
using a data-driven joint source-channel coding (JSCC) approach, which has been
previously shown to outperform conventional separation-based approaches in the
practical finite blocklength regimes, and to provide graceful degradation with
channel quality. We propose a novel neural network architecture that
incorporates the decoder-only side information at multiple stages at the
receiver side. Our results demonstrate that the proposed method succeeds in
integrating the side information, yielding improved performance at all channel
noise levels in terms of the various distortion criteria considered here,
especially at low channel signal-to-noise ratios (SNRs) and small bandwidth
ratios (BRs). We also provide the source code of the proposed method to enable
further research and reproducibility of the results.

在低延迟图像传输中，我们考虑了有相关辅助信息只存在于接收方的噪声无线信道（Wyner-Ziv 场景）。我们提出了一种新颖的神经网络架构，在接收方的多个阶段上整合解码器的辅助信息，结果表明该方法在所有信道噪声水平上在各种畸变标准下取得了改进的性能，特别是在低信道信噪比和小带宽比下。