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    Area of Science:

    • Computer Vision
    • Multimedia Signal Processing
    • Data Compression

    Background:

    • Video-based Point Cloud Compression (V-PCC) utilizes 2D video coding for dynamic 3D point clouds.
    • The computational complexity of V-PCC leads to lengthy encoding times for projection videos.

    Purpose of the Study:

    • To develop a low-complexity algorithm for accelerating V-PCC intra coding.
    • To reduce the time consumption of projection sequence compression in V-PCC.

    Main Methods:

    • Proposed a low complexity coding unit decision algorithm for V-PCC intra coding.
    • Explored cross-projection information among occupancy, geometry, and attribute sequences to improve CU partition prediction.
    • Developed a rate-distortion-oriented learning approach for accurate CU partitioning decisions.
    • Devised an overall framework with targeted feature extraction for V-PCC intra/inter coding.

    Main Results:

    • Reduced time consumption of total projection sequence compression by 57.80%.
    • Achieved negligible coding losses: 0.08% (Geom.BD-TotalRate D1), 0.33% (Geom.BD-TotalRate D2), and 0.16% (Attr.BD-TotalRate Luma).
    • Demonstrated state-of-the-art performance for accelerating V-PCC All-Intra projection sequence compression.

    Conclusions:

    • The proposed algorithm effectively accelerates V-PCC projection sequence compression.
    • The method achieves significant time savings with minimal impact on coding efficiency.
    • This work offers a valuable solution for efficient dynamic point cloud compression.