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A Comparative Study of Algorithms for Realtime Panoramic Video Blending.

Zhe Zhu, Jiaming Lu, Minxuan Wang

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    This study evaluates six video blending algorithms for real-time panoramic video synthesis, crucial for virtual reality. Results analyze performance, visual quality, and resource usage for CPU and GPU, providing a benchmark for future research.

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    Area of Science:

    • Computer Vision
    • Computer Graphics
    • Virtual Reality

    Background:

    • Video blending algorithms are under-researched compared to image blending.
    • Real-time panoramic video blending is essential for immersive virtual reality experiences.

    Purpose of the Study:

    • To investigate and compare six popular blending algorithms for real-time panoramic video.
    • To evaluate their performance, resource requirements, and visual quality.

    Main Methods:

    • Developed a video benchmark with diverse conditions.
    • Implemented and tested feather, multi-band, Poisson, mean value coordinate, multi-spline, and convolution pyramid blending algorithms.
    • Analyzed CPU and GPU performance, memory usage, and visual quality (objective and subjective).

    Main Results:

    • Quantitative and qualitative analysis of six blending algorithms' suitability for panoramic video.
    • Performance metrics including time and memory consumption for both CPU and GPU.
    • Visual quality assessment based on objective and subjective evaluations.

    Conclusions:

    • Provides a comprehensive evaluation of blending algorithms for panoramic video synthesis.
    • Offers insights into the trade-offs between different algorithms for virtual reality applications.
    • Publicly releases benchmark data and algorithm implementations for community use.