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Retargeting Video With an End-to-End Framework.

Thi-Ngoc-Hanh Le, HuiGuang Huang, Yi-Ru Chen

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    Summary
    This summary is machine-generated.

    This study introduces RETVI, an efficient video retargeting method that preserves visual content and avoids artifacts. RETVI significantly improves quality and processing time for adapting videos to various aspect ratios.

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    Area of Science:

    • Computer Graphics
    • Image and Video Processing

    Background:

    • Video retargeting is crucial for diverse digital devices but faces challenges in content preservation, flicker reduction, and processing time.
    • Existing methods often rely on slow preprocessing and struggle with arbitrary aspect ratios and object distortion.

    Purpose of the Study:

    • To develop an end-to-end video retargeting method that addresses limitations of prior work.
    • To enable video retargeting to arbitrary aspect ratios while preserving visual integrity and reducing computational cost.

    Main Methods:

    • Introduced RETVI, an end-to-end method comprising a Content Feature Analyzer (CFA) and an Adaptive Deforming Estimator (ADE).
    • Designed to eliminate computational bottlenecks common in conventional video retargeting approaches.

    Main Results:

    • RETVI demonstrates superior performance compared to previous methods in terms of visual quality.
    • Achieved significant improvements in processing time, overcoming computational limitations.

    Conclusions:

    • The proposed RETVI method offers an effective solution for video retargeting to arbitrary aspect ratios.
    • Achieves a balance between visual quality preservation and computational efficiency in video retargeting.