发表机构
Beijing University of Posts and Telecommunications; China Mobile (Suzhou) Software Technology Company Limited（北京邮电大学; 中国移动（苏州）软件技术有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出轻量级语义感知联合信源-信道优化方案，可即插即用地集成于现有数字视频通信系统，通过多智能体近端策略优化联合调整信源与信道编码率，在H.265和DCVC-RT上分别实现34.86%和18.01%的BD码率节省。
AI 中文摘要
视频语义通信作为一种提高视频传输效率的有前景的方法，已引起越来越多的关注。然而，大多数现有方法依赖于计算密集型的基于深度学习的视频编码器和解码器，这阻碍了它们在资源受限场景中的部署。为解决这一问题，我们提出了一种轻量级的语义感知联合信源-信道优化（SAJSCO）方案，该方案可作为插件模块集成到现有数字视频通信系统中。具体而言，我们建立了一个视频通信系统模型，其中发射机基于输入视频的帧间语义重要性和估计的信道状态信息，联合优化信源和信道编码参数。在此基础上，我们制定了一个优化问题，在最大比特率约束下最大化语义重要性加权的视频重建质量。为解决该问题，我们首先使用基于余弦相似度的度量并采用移位窗口机制来量化帧间语义重要性。然后，我们开发了一种多智能体近端策略优化（MPPO）算法，通过联合调整信源压缩率和信道编码率来解决所提出的问题。学习到的策略可直接应用于不同的视频编码器，无需针对特定编码器进行重新训练或微调。SAJSCO在与传统视频编码器H.265和基于深度学习的视频编码器DCVC-RT集成时，分别实现了34.86%和18.01%的Bjøntegaard Delta码率降低。在硬件测试平台上的空中实验进一步表明，与各自性能最佳的固定参数基线相比，使用H.265时PSNR增益高达1.448 dB，使用DCVC-RT时LPIPS降低高达0.033。
英文摘要
Video semantic communication has attracted increasing attention as a promising approach to improving video transmission efficiency. However, most existing approaches rely on computationally intensive deep learning-based video encoders and decoders, which hinders their deployment in resource-constrained scenarios. To address this issue, we propose a lightweight semantic-aware joint source-channel optimization (SAJSCO) scheme that can be integrated into existing digital video communication systems as a plug-in module. Specifically, we develop a video communication system model in which the transmitter jointly optimizes source and channel coding parameters based on the inter-frame semantic importance of the input video and estimated channel state information. On this basis, we formulate an optimization problem that maximizes semantic importance weighted video reconstruction quality under a maximum bitrate constraint. To solve it, we first quantify inter-frame semantic importance using a cosine similarity-based metric with a shifted window mechanism. We then develop a multi-actor proximal policy optimization (MPPO) algorithm to solve the formulated problem by jointly adapting the source compression rate and channel coding rate. The learned policy can be directly applied to different video encoders without encoder-specific retraining or fine-tuning. SAJSCO achieves Bjøntegaard Delta rate reductions of 34.86\% and 18.01\% when integrated with H.265, a conventional video encoder, and DCVC-RT, a deep learning-based video encoder. Over-the-air experiments on a hardware testbed further demonstrate a PSNR gain of up to 1.448 dB with H.265 and an LPIPS reduction of up to 0.033 with DCVC-RT compared with the respective best-performing fixed-parameter baselines.
