The bottleneck of satellite-to-ground links poses a major challenge for the timely downlink of massive on-board imagery. This paper studies adaptive image transmission over LEO satellite-to-ground links using joint source-channel coding (JSCC). We propose an RL-based framework that dynamically selects the channel dimension (compression ratio) of a SwinJSCC encoder to maximize the number of received satisfying reconstruction-quality constraints (PSNR and MS-SSIM) within a finite visibility window. The agent leverages SNR prediction to perform proactive rate adaptation and incorporates an on-board transmission-queue model that captures bursty encoding while penalizing both buffer overflow and underutilization. Simulations under realistic overpass conditions show that the proposed policy substantially outperforms fixed-rate baselines, achieving nearly 95% qualified frames with zero packet loss.

翻译：星地链路的瓶颈对星载海量影像的及时下行构成了重大挑战。本文研究利用联合信源信道编码（JSCC）在低地球轨道（LEO）卫星到地面链路上实现自适应图像传输。我们提出一种基于强化学习的框架，该框架动态选择SwinJSCC编码器的信道维度（压缩比），以在有限可见窗口内最大化满足重建质量约束（PSNR和MS-SSIM）的接收帧数量。智能体利用信噪比预测执行主动速率自适应，并引入捕获突发编码特性的星上传输队列模型，同时惩罚缓冲区溢出与利用率不足。在真实过境场景下的仿真表明，所提策略显著优于固定速率基线，在零丢包条件下实现了近95%的合格帧率。