Point Cloud Coding Node Attribute Prediction Across Partition Depths

The patent applies local quality by differentiating prediction strategies based on spatial location and partition depth. Different weights are assigned to neighboring nodes at different depths, and the prediction process adapts to the specific spatial context of each node being predicted. This allows the system to capture local spatial relationships while accounting for the hierarchical structure of the point cloud partitioning.

The patent segments the prediction process into multiple stages by first identifying neighboring nodes at different partition depths, then selectively using their attribute information based on predefined rules. The segmentation of the prediction task into depth-based components allows efficient compression while maintaining accuracy through structured approach.

The patent performs preliminary action by pre-identifying and pre-calculating the set of neighboring nodes at different partition depths before the actual prediction process. This preliminary structure definition allows the prediction to proceed more efficiently by avoiding complex real-time calculations, thus reducing overall computational burden while maintaining effectiveness.

The patent changes parameters by introducing weight coefficients that can be adjusted based on the relative importance of different neighboring nodes. This parameter adjustment mechanism allows the system to optimize the balance between prediction accuracy and computational complexity by emphasizing more informative neighbors while downweighting less relevant ones.