We consider the image transmission problem over a noisy wireless channel via deep learning-based joint source-channel coding (DeepJSCC) along with a denoising diffusion probabilistic model (DDPM) at the receiver. Specifically, we are interested in the perception-distortion trade-off in the practical finite block length regime, in which separate source and channel coding can be highly suboptimal. We introduce a novel scheme that utilizes the range-null space decomposition of the target image. We transmit the range-space of the image after encoding and employ DDPM to progressively refine its null space contents. Through extensive experiments, we demonstrate significant improvements in distortion and perceptual quality of reconstructed images compared to standard DeepJSCC and the state-of-the-art generative learning-based method. We will publicly share our source code to facilitate further research and reproducibility.

翻译：我们考虑基于深度学习的联合信源信道编码（DeepJSCC）结合接收端去噪扩散概率模型（DDPM）在噪声无线信道上的图像传输问题。具体而言，我们关注在实际有限码长体制下感知-失真权衡，其中分离的信源和信道编码可能高度次优。我们提出了一种利用目标图像的范围-零空间分解的新方案。我们在编码后传输图像的范围空间，并采用DDPM逐步完善其零空间内容。通过大量实验，我们证明与标准DeepJSCC及最先进的基于生成学习的方法相比，重建图像的失真和感知质量有显著提升。我们将公开共享源代码，以促进进一步研究和可复现性。