In this paper, a novel learning-based Wyner-Ziv coding framework is considered under a distributed image transmission scenario, where the correlated source is only available at the receiver. Unlike other learnable frameworks, our approach demonstrates robustness to non-stationary source correlation, where the overlapping information between image pairs varies. Specifically, we first model the affine relationship between correlated images and leverage this model for learnable mask generation and rate-adaptive joint source-channel coding. Moreover, we also provide a warping-prediction network to remove the distortion from channel interference and affine transform. Intuitively, the observed performance improvement is largely due to focusing on the simple geometric relationship, rather than the complex joint distribution between the sources. Numerical results show that our framework achieves a 1.5 dB gain in PSNR and a 0.2 improvement in MS-SSIM, along with a significant superiority in perceptual metrics, compared to state-of-the-art methods when applied to real-world samples with non-stationary correlations.

翻译：本文提出了一种在分布式图像传输场景下的新型基于学习的Wyner-Ziv编码框架，其中相关信源仅在接收端可用。与其他可学习框架不同，我们的方法对非平稳信源相关性（即图像对之间的重叠信息会变化）表现出鲁棒性。具体而言，我们首先对相关图像之间的仿射关系进行建模，并利用该模型进行可学习掩码生成与速率自适应的联合信源信道编码。此外，我们还提供了一个扭曲预测网络，以消除信道干扰和仿射变换带来的失真。直观而言，所观察到的性能提升主要源于关注简单的几何关系，而非信源间复杂的联合分布。数值结果表明，当应用于具有非平稳相关性的真实世界样本时，与现有先进方法相比，我们的框架在PSNR上实现了1.5 dB的提升，在MS-SSIM上取得了0.2的改进，并在感知指标上展现出显著优势。