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 16.35% channel bandwidth saving versus the state-of-the-art engineered VTM + 5G LDPC coded transmission system with lower processing latency.

翻译：近年来，深度学习方法在解决高效端到端传输问题方面引起了广泛关注。这些方法被称为非线性变换信源信道编码（NTSCC），通过提取信源信号的语义潜在特征，并学习熵模型以引导可变速率联合信源信道编码，从而在无线信道上传输潜在特征。本文提出了一个用于改进NTSCC的综合框架，实现了更高的系统编码增益、更好的模型通用性以及更灵活的、与语义引导对齐的自适应策略。这一新型精细化的NTSCC模型现已能够支持新兴扩展现实（XR）中的大规模数据交互，从而促进了语义通信的应用。具体而言，我们提出了三种有效的改进方法。首先，引入上下文熵模型以更好地捕捉语义潜在特征中的空间相关性，从而开发出更精确的速率分配和上下文联合信源信道编码，以实现更高的编码增益。在此基础上，进一步提出响应网络架构以构建通用型NTSCC，即单次训练模型可支持多种速率和信道状态，有利于实际部署。随后，我们提出在线潜在特征编辑方法，以实现与特定语义引导对齐的更灵活的编码速率控制。通过综合应用上述三种NTSCC改进方法，最终形成了一种部署友好的语义编码传输系统。实验验证表明，我们的改进型NTSCC系统相较于最先进的工程化VTM+5G LDPC编码传输系统，在降低处理延迟的同时，实现了16.35%的信道带宽节省。