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GQE-Net: A Graph-Based Quality Enhancement Network for Point Cloud Color Attribute.

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    This study introduces GQE-Net, a graph-based network to enhance point cloud quality by reducing color distortion using geometry. The method achieves state-of-the-art results, significantly improving compression efficiency for 3D visual data.

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    Area of Science:

    • Computer Vision
    • 3D Data Processing
    • Signal Processing

    Background:

    • Point clouds are increasingly used for 3D object representation.
    • Existing compression methods often degrade point cloud quality, particularly color fidelity.
    • Efficient storage and transmission of 3D point cloud data remain a challenge.

    Purpose of the Study:

    • To propose a novel graph-based quality enhancement network (GQE-Net) for reducing color distortion in point clouds.
    • To leverage geometry information and graph convolutions for effective feature extraction.
    • To improve the quality of compressed 3D point cloud data.

    Main Methods:

    • Developed GQE-Net utilizing graph convolution blocks and a parallel-serial graph attention module with multi-head attention.
    • Incorporated a feature refinement module considering point normals and geometry distances.
    • Processed point clouds in overlap-allowed 3D patches to manage GPU memory constraints.
    • Trained separate models for Y, Cb, and Cr color components to handle data distribution variations.

    Main Results:

    • GQE-Net achieved state-of-the-art performance in point cloud quality enhancement.
    • Significant Bjφntegaard delta (BD)-peak-signal-to-noise ratio (PSNR) gains were observed: 0.43 dB (Y), 0.25 dB (Cb), and 0.36 dB (Cr).
    • Achieved substantial BD-rate savings: 14.0% (Y), 9.3% (Cb), and 14.5% (Cr) for dense point clouds when integrated with the geometry-based point cloud compression (G-PCC) standard.

    Conclusions:

    • GQE-Net effectively reduces color distortion in point clouds.
    • The proposed method offers significant improvements in compression efficiency and quality for 3D visual data.
    • The approach demonstrates the potential of graph-based networks in enhancing compressed 3D data representations.