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ViComp: Video Compensation for Projector-Camera Systems.

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    This study introduces an online projector video compensation system that adapts parameters in real-time. It significantly outperforms existing methods by using parallel processing and deep learning for faster, more accurate video compensation.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Machine Learning

    Background:

    • Projector video compensation corrects geometric and photometric distortions from non-ideal surfaces.
    • Current methods require extensive offline training with sampling images, demanding user effort and lacking video content awareness.

    Purpose of the Study:

    • To develop an online adaptive video compensation system for projectors.
    • To reduce user effort and improve compensation accuracy by leveraging video content.

    Main Methods:

    • A five-thread parallel system for real-time compensation, projection, capture, and model updates.
    • A deep learning model combining a transformer-based method and a CNN for environmental adaptation.
    • A dual short-term and long-term memory update strategy for fast convergence and reduced error accumulation.

    Main Results:

    • The proposed system enables on-the-fly model updates using actual video frames, eliminating the need for offline sampling.
    • Experimental results demonstrate significant performance improvement over state-of-the-art baseline methods.
    • The system achieves faster convergence and mitigates error accumulation during fine-tuning.

    Conclusions:

    • The developed online adaptive system offers a more efficient and effective solution for projector video compensation.
    • The parallel processing and deep learning approach represent a significant advancement in real-time video distortion correction.
    • This work paves the way for more seamless and high-quality video projection experiences in diverse environments.