With the rapid growth of multimedia data volume, there is an increasing need
for efficient video transmission in applications such as virtual reality and
future video streaming services. Semantic communication is emerging as a vital
technique for ensuring efficient and reliable transmission in low-bandwidth,
high-noise settings. However, most current approaches focus on joint
source-channel coding (JSCC) that depends on end-to-end training. These methods
often lack an interpretable semantic representation and struggle with
adaptability to various downstream tasks. In this paper, we introduce the use
of object-attribute-relation (OAR) as a semantic framework for videos to
facilitate low bit-rate coding and enhance the JSCC process for more effective
video transmission. We utilize OAR sequences for both low bit-rate
representation and generative video reconstruction. Additionally, we
incorporate OAR into the image JSCC model to prioritize communication resources
for areas more critical to downstream tasks. Our experiments on traffic
surveillance video datasets assess the effectiveness of our approach in terms
of video transmission performance. The empirical findings demonstrate that our
OAR-based video coding method not only outperforms H.265 coding at lower
bit-rates but also synergizes with JSCC to deliver robust and efficient video
transmission.

利用对象 - 属性 - 关系（OAR）作为视频的语义框架，以便在低比特率编码和增强联合源信道编码（JSCC）过程中实现更有效的视频传输。