Recent deep learning methods have led to increased interest in solving high-efficiency end-to-end transmission problems. These methods, we call nonlinear transform source-channel coding (NTSCC), extract the semantic latent features of source signal, and learn entropy model to guide the joint source-channel coding with variable rate to transmit latent features over wireless channels. In this paper, we propose a comprehensive framework for improving NTSCC, thereby higher system coding gain, better model versatility, and more flexible adaptation strategy aligned with semantic guidance are all achieved. This new sophisticated NTSCC model is now ready to support large-size data interaction in emerging XR, which catalyzes the application of semantic communications. Specifically, we propose three useful improvement approaches. First, we introduce a contextual entropy model to better capture the spatial correlations among the semantic latent features, thereby more accurate rate allocation and contextual joint source-channel coding are developed accordingly to enable higher coding gain. On that basis, we further propose response network architectures to formulate versatile NTSCC, i.e., once-trained model supports various rates and channel states that benefits the practical deployment. Following this, we propose an online latent feature editing method to enable more flexible coding rate control aligned with some specific semantic guidance. By comprehensively applying the above three improvement methods for NTSCC, a deployment-friendly semantic coded transmission system stands out finally. Our improved NTSCC system has been experimentally verified to achieve considerable bandwidth saving versus the state-of-the-art engineered VTM + 5G LDPC coded transmission system with lower processing latency.

翻译：近年来的深度学习方法引发了解决高效端到端传输问题的广泛关注。这些方法（称为非线性变换源信道编码，NTSCC）提取源信号的语义潜在特征，并通过学习熵模型指导可变码率的联合源信道编码，从而在无线信道上传输潜在特征。本文提出一个全面框架来改进NTSCC，从而实现更高的系统编码增益、更好的模型通用性，以及更灵活、与语义引导相一致的适配策略。这种新型的精细化NTSCC模型现已能够支持新兴扩展现实（XR）中的大型数据交互，从而催化语义通信的应用。具体而言，我们提出三种有效的改进方法：首先，引入上下文熵模型以更好捕捉语义潜在特征间的空间相关性，进而发展出更精确的码率分配与上下文联合源信道编码，以实现更高编码增益。在此基础上，进一步提出响应型网络架构以构建通用NTSCC，即一次训练模型可支持多种码率与信道状态，有利于实际部署。随后，提出一种在线潜在特征编辑方法，以实现与特定语义引导相一致的更灵活编码码率控制。通过综合应用上述三种改进方法于NTSCC，最终形成一种易于部署的语义编码传输系统。实验验证表明，与当前最先进的工程化VTM+5G LDPC编码传输系统相比，我们改进的NTSCC系统在实现显著带宽节省的同时降低了处理延迟。