  In this paper, the problem of semantic-based efficient image transmission is
studied over the Internet of Vehicles (IoV). In the considered model, a vehicle
shares massive amount of visual data perceived by its visual sensors to assist
other vehicles in making driving decisions. However, it is hard to maintain a
high reliable visual data transmission due to the limited spectrum resources.
To tackle this problem, a semantic communication approach is introduced to
reduce the transmission data amount while ensuring the semantic-level accuracy.
Particularly, an image segmentation semantic communication (ISSC) system is
proposed, which can extract the semantic features from the perceived images and
transmit the features to the receiving vehicle that reconstructs the image
segmentations. The ISSC system consists of an encoder and a decoder at the
transmitter and the receiver, respectively. To accurately extract the image
semantic features, the ISSC system encoder employs a Swin Transformer based
multi-scale semantic feature extractor. Then, to resist the wireless noise and
reconstruct the image segmentation, a semantic feature decoder and a
reconstructor are designed at the receiver. Simulation results show that the
proposed ISSC system can reconstruct the image segmentation accurately with a
high compression ratio, and can achieve robust transmission performance against
channel noise, especially at the low signal-to-noise ratio (SNR). In terms of
mean Intersection over Union (mIoU), the ISSC system can achieve an increase by
75%, compared to the baselines using traditional coding method
