Image semantic communication is a critical component in next-generation wireless communication systems. However, such systems typically suffer from large memory footprints and high computational complexity, making them difficult to deploy on resource-constrained devices. To address these challenges, we propose a vision transformer (ViT)-enabled image semantic communication system. In this system, a recursive structure is introduced to iteratively refine semantic features and reduce the parameter count. In addition, three dynamic adjustment strategies are designed to adaptively reduce computational complexity: dynamic depth adjustment, dynamic width adjustment, and joint width-depth optimization. Dynamic depth adjustment adaptively determines the number of recursive modules according to image content and channel conditions, while dynamic width adjustment selectively preserves important neurons and attention heads. The joint width-depth optimization further enables flexible computation configurations. Simulation results verify that the proposed recursive ViT-based system, combined with the three dynamic adjustment strategies, reduces the parameter count by 48.7% and achieves higher reconstruction quality than existing baselines under comparable computational complexity.

翻译：图像语义通信是下一代无线通信系统的关键组成部分。然而，这类系统通常存在内存占用大、计算复杂度高的问题，难以部署在资源受限设备上。针对这些挑战，本文提出一种基于视觉Transformer（ViT）的图像语义通信系统。该系统引入递归结构以迭代优化语义特征并减少参数数量。同时，设计三种动态调整策略以自适应降低计算复杂度：动态深度调整、动态宽度调整以及联合宽度-深度优化。动态深度调整根据图像内容与信道条件自适应确定递归模块数量，动态宽度调整选择性保留重要神经元和注意力头，联合宽度-深度优化进一步支持灵活的计算配置。仿真结果表明，所提基于递归ViT的系统结合三种动态调整策略，在参数数量减少48.7%的同时，于相当的计算复杂度下实现了优于现有基准的重建质量。