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High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
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Learning Compressible 360 ° Video Isomers.

Yu-Chuan Su, Kristen Grauman

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |February 21, 2020
    PubMed
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    Researchers developed a convolutional neural network to find optimal sphere rotations for 360° video compression. This method improves compression rates by 8-18% by predicting the most compressible video orientation.

    Area of Science:

    • Computer Vision
    • Video Compression
    • Machine Learning

    Background:

    • Standard video encoders are applied to 360° video, projecting spherical frames arbitrarily.
    • The compressibility of 360° video varies significantly with projection orientation.

    Purpose of the Study:

    • To develop a method for predicting sphere rotations that maximize 360° video compression rates.
    • To leverage machine learning to identify the most compressible orientations without repeated rendering.

    Main Methods:

    • A convolutional neural network was trained to associate visual content with compressibility at different cubemap projection rotations.
    • The model learns to predict the optimal sphere rotation for maximal compression in a single pass.

    Main Results:

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    • "Good" rotations are 8-18% more compressible than "bad" ones.
    • The learning-based approach reliably predicts optimal rotations 78% of the time.
    • Validation was performed on thousands of video clips across multiple codecs.

    Conclusions:

    • Sphere rotation is a significant, previously untapped dimension for 360° video compression.
    • Machine learning offers an efficient and effective solution for optimizing 360° video compression through rotation prediction.