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Differential Transform for Video-Based Plenoptic Point Cloud Coding.

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    This study introduces efficient video-based compression for plenoptic point clouds (PPC), enhancing realism. The novel method is backward compatible with existing single-color V-PCC decoders, achieving superior performance.

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    Area of Science:

    • Computer Vision
    • Computer Graphics
    • Data Compression

    Background:

    • Point cloud compression is a key area in multimedia standards.
    • Existing Moving Picture Experts Group video-based point cloud compression (V-PCC) handles single-color data.
    • Plenoptic point clouds (PPC) offer enhanced realism with multi-directional color attributes.

    Purpose of the Study:

    • To develop an efficient, backward-compatible compression method for plenoptic point clouds (PPC).
    • To improve the realism of point cloud representations using multi-directional color information.
    • To integrate plenoptic enhancement into existing V-PCC frameworks.

    Main Methods:

    • A novel method generates Nc+3 image atlases for PPC, with Nc differential images against a reference color image.
    • Differential images undergo pixel-wise Nc-point discrete cosine transform (DCT) to create secondary payload atlases.
    • The method ensures backward compatibility with single-color V-PCC decoders, adapting payload based on reference attribute availability.

    Main Results:

    • The proposed method achieves unrivaled performance in video-based point cloud compression.
    • Differential DCT-transformed atlases effectively encode plenoptic enhancement information.
    • Backward compatibility with existing V-PCC decoders is maintained.

    Conclusions:

    • The novel approach significantly advances plenoptic point cloud compression efficiency and realism.
    • The method offers a viable solution for compressing complex, multi-attribute point cloud data.
    • This technique enhances existing V-PCC standards for richer visual experiences.