Goal-Oriented Semantic Communication for Video Transmission via Optical Flow-Based Autoencoder
Yujie Xu, Nan Li, Yansha Deng · 2025
Efficient video transmission is a critical task within the realm of current wireless communication system. Addressing the imperative to alleviate the transmission burden and conserve communication resources, we propose an optical flow-based goal-oriented semantic communication framework for video transmission (OF-GSC). Our framework features an optical flow-based semantic encoder that includes a motion extractor for optical flow estimation and a patch-level optical flow-based semantic information (SI) extractor to effectively identify and select important SI, thereby reducing the transmission load while ensuring the high-quality video reconstruction in semantic decoder. Specifically, the first original frame is leveraged as the base knowledge. Once the base knowledge and the received SI are embedded at the receiver, the embedded data is then fed into the customized autoencoder model within the semantic decoder of OF-GSC framework for efficient video reconstruction. In comparison to DeepJSCC, our OF-GSC framework achieves a significant improvement in generated video quality, as evidenced by a 13.47% increase in the Structural Similarity Index Measure (SSIM) score. Under stringent communication constraints, OFGSC surpasses M-JPEG by 14.04% in SSIM score. These results highlight the robustness and superiority of our proposed OF-GSC in efficient video transmission.