In this paper, we propose a class of high-efficiency deep joint source-channel coding methods that can closely adapt to the source distribution under the nonlinear transform, it can be collected under the name nonlinear transform source-channel coding (NTSCC). In the considered model, the transmitter first learns a nonlinear analysis transform to map the source data into latent space, then transmits the latent representation to the receiver via deep joint source-channel coding. Our model incorporates the nonlinear transform as a strong prior to effectively extract the source semantic features and provide side information for source-channel coding. Unlike existing conventional deep joint source-channel coding methods, the proposed NTSCC essentially learns both the source latent representation and an entropy model as the prior on the latent representation. Accordingly, novel adaptive rate transmission and hyperprior-aided codec refinement mechanisms are developed to upgrade deep joint source-channel coding. The whole system design is formulated as an optimization problem whose goal is to minimize the end-to-end transmission rate-distortion performance under established perceptual quality metrics. Across test image sources with various resolutions, we find that the proposed NTSCC transmission method generally outperforms both the analog transmission using the standard deep joint source-channel coding and the classical separation-based digital transmission. Notably, the proposed NTSCC method can potentially support future semantic communications due to its content-aware ability and perceptual optimization goal.

中文翻译：

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用于语义通信的非线性变换源通道编码


在本文中，我们提出了一类能够紧密适应非线性变换下的源分布的高效深度联合源通道编码方法，可以将其统称为非线性变换源通道编码（NTSCC）。在所考虑的模型中，发射器首先学习非线性分析变换以将源数据映射到潜在空间，然后通过深度联合源信道编码将潜在表示传输到接收器。我们的模型将非线性变换作为强先验，有效地提取源语义特征并为源通道编码提供辅助信息。与现有的传统深度联合源通道编码方法不同，所提出的 NTSCC 本质上学习源潜在表示和熵模型作为潜在表示的先验。因此，开发了新颖的自适应速率传输和超先验辅助编解码器细化机制来升级深度联合源信道编码。整个系统设计被表述为一个优化问题，其目标是在既定的感知质量指标下最小化端到端传输速率失真性能。在各种分辨率的测试图像源中，我们发现所提出的 NTSCC 传输方法通常优于使用标准深度联合源通道编码的模拟传输和经典的基于分离的数字传输。值得注意的是，由于其内容感知能力和感知优化目标，所提出的 NTSCC 方法有可能支持未来的语义通信。