  Semantic communication has emerged as a promising approach for improving
efficient transmission in the next generation of wireless networks. Inspired by
the success of semantic communication in different areas, we aim to provide a
new semantic communication scheme from the semantic level. In this paper, we
propose a novel DL-based semantic communication system for video transmission,
which compacts semantic-related information to improve transmission efficiency.
In particular, we utilize the Bi-optical flow to estimate residual information
of inter-frame details. We also propose a feature choice module and a feature
fusion module to drop semantically redundant features while paying more
attention to the important semantic-related content. We employ a frame
prediction module to reconstruct semantic features of the prediction frame from
the received signal at the receiver. To enhance the system's robustness, we
propose a noise attention module that assigns different importance weights to
the extracted features. Simulation results indicate that our proposed method
outperforms existing approaches in terms of transmission efficiency, achieving
about 33.3\% reduction in the number of transmitted symbols while improving the
peak signal-to-noise ratio (PSNR) performance by an average of 0.56dB.
