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    This study enhances geometric point cloud compression (G-PCC) by improving predictive coding with Region Adaptive Hierarchical Transform (RAHT). New methods reduce distortion and complexity for better immersive multimedia experiences.

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    Area of Science:

    • Computer Science
    • Multimedia Engineering
    • Signal Processing

    Background:

    • Point cloud compression is vital for immersive multimedia.
    • Region Adaptive Hierarchical Transform (RAHT) is standard for G-PCC attribute coding.
    • Existing RAHT predictive coding faces challenges with distortion and complexity.

    Purpose of the Study:

    • To advance predictive coding within RAHT for G-PCC.
    • To reduce decoding complexity and improve prediction accuracy.
    • To enhance the efficiency and performance of G-PCC.

    Main Methods:

    • Implemented sample domain prediction to mitigate transform-related distortion.
    • Introduced reference node resampling for improved inter-prediction signal alignment.
    • Developed a sub-band conditioned temporal filtering mechanism for better prediction.
    • Derived an inter-eligibility criterion using DC coefficient information.

    Main Results:

    • Achieved significant gains in compression efficiency.
    • Demonstrated considerable reduction in decoding complexity.
    • Validated the utility of proposed methods through experimental results.
    • All methods were adopted into the second version of G-PCC.

    Conclusions:

    • The proposed advances significantly improve RAHT-based G-PCC.
    • Sample domain prediction, reference node resampling, temporal filtering, and inter-eligibility enhance compression and reduce complexity.
    • These advancements are crucial for the future of immersive multimedia applications.