An Efficient Vector Quantization-Based Semantic Communication System for Virtual Reality Video Transmission

The large volume of data associated with virtual reality (VR) video presents significant challenges for wireless transmission. This letter proposes an efficient transmission system for VR video, based on semantic vector quantization (VQ) and moving object tracking, termed SeVQ-VR. This system not only substantially reduces the transmission data volume but also ensures full compatibility with existing communication systems. Specifically, it prioritizes the transmission of semantically significant moving objects, optimizing bandwidth utilization. Additionally, it utilizes semantic vector quantization to further compress video feature data by transmitting only the feature indices, while maintaining accurate reconstruction of the content. Simulation results indicate that SeVQ-VR outperforms the H.265 standard in terms of both Multi-Scale Structural Similarity (MS-SSIM) and Learned Perceptual Image Patch Similarity (LPIPS) metrics, especially under low channel signal-to-noise ratio (SNR) in multi-path channels.