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InvVis: Large-Scale Data Embedding for Invertible Visualization.

Huayuan Ye, Chenhui Li, Yang Li

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    Summary
    This summary is machine-generated.

    InvVis enables embedding significant data into visualization images, making them perceptually identical to originals. This invertible visualization approach allows for high-quality data concealing and revealing.

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    Area of Science:

    • Computer Vision
    • Data Visualization
    • Information Security

    Background:

    • Traditional data embedding methods often alter visual perception.
    • Reconstructing visualizations from images presents significant challenges.

    Purpose of the Study:

    • To introduce InvVis, a novel invertible visualization technique.
    • To enable high-capacity data embedding within visualization images without perceptual changes.

    Main Methods:

    • Developed a method for efficient chart data expression in image form.
    • Utilized an invertible neural network model for data concealment and revelation.
    • Explored diverse application scenarios for the InvVis approach.

    Main Results:

    • Achieved perceptually indistinguishable visualization images after data embedding.
    • Demonstrated high-quality data concealing and revealing capabilities.
    • Evaluated embedding quality, restoration accuracy, and encoding capacity.

    Conclusions:

    • InvVis offers a powerful solution for invertible visualization.
    • The method shows great potential for secure data embedding in visual formats.