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    Area of Science:

    • Computer Vision for Communications
    • Multimedia Services
    • Artificial Intelligence Foundation Models

    Background:

    • Real-time video conferences generate substantial traffic due to proliferating interactive multimedia services.
    • Existing Computer Vision (CV) techniques lack integration for diverse interactive video tasks like recognition and segmentation.
    • The rise of foundation models presents opportunities for task-oriented semantic communications.

    Purpose of the Study:

    • To propose a novel framework, Real-Time Video Conference with Foundation Model (RTVCFM), for enhanced interactivity in multimedia services.
    • To address the limitations of current CV methods in handling complex interactive video demands.
    • To leverage foundation models for efficient and high-fidelity video transmission.

    Main Methods:

    • At the transmitter: Causal understanding and spatiotemporal decoupling using Video Time-Aware Large Language Model (VTimeLLM), Iterated Integrated Attributions (IIA), and Segment Anything Model 2 (SAM2) for semantic segmentation.
    • During transmission: Two-stage semantic transmission optimization driven by Channel State Information (CSI) for low bit rate and high semantic fidelity.
    • At the receiver: Multidimensional fusion using Diffusion Model for Foreground Background Fusion (DMFBF) for video stream reconstruction.

    Main Results:

    • Achieved a compression ratio as high as 95.6%.
    • Guaranteed high semantic similarity with 98.73% in Multi-Scale Structural Similarity Index Measure (MS-SSIM) and 98.35% in Structural Similarity (SSIM).
    • Demonstrated that reconstructed videos are highly similar to original videos.

    Conclusions:

    • RTVCFM effectively satisfies the interactivity requirements of real-time multimedia services.
    • The proposed framework enables significant video compression while preserving high semantic fidelity.
    • This approach showcases the potential of foundation models in advancing computer vision for communications.