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    Area of Science:

    • Computer Vision
    • Signal Processing
    • Data Compression

    Background:

    • Advancements in 3D sensing (e.g., LIDAR, depth cameras) and applications necessitate efficient 3D point cloud compression.
    • Existing compression algorithms and standardization efforts aim for high ratios and flexibility at reasonable computational costs.

    Purpose of the Study:

    • To develop an innovative and effective transform-based coding strategy for dynamic 3D point clouds.
    • To improve compression efficiency for both lossless geometry and lossy color data.

    Main Methods:

    • A region-adaptive transform-based coding strategy combining non-linear geometric and linear color transforms.
    • Exploitation of temporal redundancy for transform adaptation and attribute prediction.

    Main Results:

    • Significant bit rate reduction achieved in lossless geometry coding.
    • Improved rate-distortion performance in lossy color component coding compared to state-of-the-art methods.

    Conclusions:

    • The proposed strategy offers superior compression performance for dynamic 3D point clouds.
    • Region-adaptive transforms and temporal prediction are key to achieving these gains.