标题： 通过稳定级联的高效稳健语义图像通信 显示英文标题

标题： Efficient and Robust Semantic Image Communication via Stable Cascade

摘要： 基于扩散模型（DM）的语义图像通信（SIC）系统面临显著挑战，如推理速度慢和生成随机性，这限制了其可靠性和实用性。 为克服这些问题，我们提出了一种受Stable Cascade启发的新SIC框架，其中使用极紧凑的潜在图像嵌入作为扩散过程的条件。 我们的方法大幅降低了数据传输开销，将传输的嵌入压缩到原始图像大小的0.29%。 在噪声信道条件下，它在多个指标上验证了优于三种基准方法——基于分割图的扩散SIC模型（GESCO）、最近的基于Stable Diffusion（SD）的SIC框架（Img2Img-SC）以及传统的JPEG2000 + LDPC编码——实现了更优的重建质量。 值得注意的是，它还提供了显著的计算效率，与Img2Img-SC中采用的方法相比，对于512 x 512图像的重建速度快了3倍以上，对于1024 x 1024图像的重建速度快了16倍以上。 显示英文摘要

摘要： Diffusion Model (DM) based Semantic Image Communication (SIC) systems face significant challenges, such as slow inference speed and generation randomness, that limit their reliability and practicality. To overcome these issues, we propose a novel SIC framework inspired by Stable Cascade, where extremely compact latent image embeddings are used as conditioning to the diffusion process. Our approach drastically reduces the data transmission overhead, compressing the transmitted embedding to just 0.29% of the original image size. It outperforms three benchmark approaches - the diffusion SIC model conditioned on segmentation maps (GESCO), the recent Stable Diffusion (SD)-based SIC framework (Img2Img-SC), and the conventional JPEG2000 + LDPC coding - by achieving superior reconstruction quality under noisy channel conditions, as validated across multiple metrics. Notably, it also delivers significant computational efficiency, enabling over 3x faster reconstruction for 512 x 512 images and more than 16x faster for 1024 x 1024 images as compared to the approach adopted in Img2Img-SC.