  Semantic communication has undergone considerable evolution due to the recent
rapid development of artificial intelligence (AI), significantly enhancing both
communication robustness and efficiency. Despite these advancements, most
current semantic communication methods for image transmission pay little
attention to the differing importance of objects and backgrounds in images. To
address this issue, we propose a novel scheme named ASCViT-JSCC, which utilizes
vision transformers (ViTs) integrated with an orthogonal frequency division
multiplexing (OFDM) system. This scheme adaptively allocates bandwidth for
objects and backgrounds in images according to the importance order of
different parts determined by object detection of you only look once version 5
(YOLOv5) and feature points detection of scale invariant feature transform
(SIFT). Furthermore, the proposed scheme adheres to digital modulation
standards by incorporating quantization modules. We validate this approach
through an over-the-air (OTA) testbed named intelligent communication prototype
validation platform (ICP) based on a software-defined radio (SDR) and NVIDIA
embedded kits. Our findings from both simulations and practical measurements
show that ASCViT-JSCC significantly preserves objects in images and enhances
reconstruction quality compared to existing methods.
