  As a new communication paradigm, semantic communication has received
widespread attention in communication fields. However, since the decoding of
semantic signals relies on contextual knowledge, misalignment between the
starting position of the semantic signal and the AI-based semantic decoder
would prevent source signal recovery and reconstruction. To achieve more
precise semantic communication, this study proposes an image-based semantic
synchronization method leveraging intrinsic semantic features of image content.
Specifically, a shared synchronized image (SyncImg) is encoded into a
synchronization vector header at the transmitter and sent to the receiver. The
receiver adopts a sliding window semantic decoder combined with classification
and template matching methods to locate the synchronization point. Experimental
results demonstrate that compared with traditional methods, the proposed method
achieves a lower miss detected ratio (MDR) and root-mean-square error (RMSE)
under low signal-to-noise ratios, realizing accurate synchronization of
semantic signals across different devices.
