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Learning to Restore Compressed Point Cloud Attribute: A Fully Data-Driven Approach and a Rules-Unrolling-Based

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    This study compares two methods for restoring visual quality in Geometry-based Point Cloud Compression (G-PCC). NeuralSAO offers significant improvements but is complex, while NeuralBF is simpler and generalizes better for G-PCC attribute restoration.

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    Area of Science:

    • Computer Vision
    • Signal Processing
    • Holographic Media

    Background:

    • Geometry-based Point Cloud Compression (G-PCC) is crucial for holographic media but introduces visual artifacts.
    • These artifacts degrade the Quality of Experience (QoE) by affecting attributes like RGB colors.

    Purpose of the Study:

    • To investigate and compare post-processing filters for restoring G-PCC compressed point cloud attributes.
    • To evaluate the performance, complexity, and generalization capabilities of data-driven and rule-based restoration methods.

    Main Methods:

    • Developed NeuralSAO, a learning-based method using multiscale features for quantization error compensation.
    • Proposed NeuralBF, a bilateral filter with learnable parameters for noise reduction, considering geometric and photometric contributions.
    • Compared NeuralSAO and NeuralBF on G-PCC compressed point cloud attributes.

    Main Results:

    • NeuralSAO achieved state-of-the-art quality improvement, reducing Bjøntegaard delta rate (BD-BR) by 20% on solid point clouds.
    • NeuralBF, while achieving half the gains of NeuralSAO, demonstrated a lightweight design and impressive generalization.
    • NeuralSAO proved computationally intensive and prone to generalization issues.

    Conclusions:

    • Comparative analysis highlights the trade-offs between large-scale data-driven (NeuralSAO) and small-scale rule-based (NeuralBF) filters for point cloud attribute restoration.
    • Understanding filter capacity is key for optimizing G-PCC quality restoration in networked holographic services.