3D Point Cloud Attribute Encoding with Threshold-Based Tree Prediction

The patent applies different encoding strategies to different regions of the N-ary tree based on local characteristics. Specifically, it identifies leaf nodes with valid children and applies prediction encoding only to those nodes that meet certain criteria (valid node count thresholds), while using other encoding methods for remaining nodes. This localized application of prediction processes optimizes coding efficiency for suitable nodes while avoiding unnecessary complexity for others.

The patent dynamically selects encoding methods based on the actual data characteristics during the encoding process. It calculates valid node counts for different regions and adaptively chooses between prediction-based encoding and alternative encoding methods depending on whether the valid node count exceeds predetermined thresholds. This dynamic adaptation allows the system to optimize for coding efficiency when appropriate while reducing complexity when not beneficial.

The patent applies prediction decoding selectively only to nodes that were encoded using prediction processes. During decoding, it identifies which nodes require prediction-based reconstruction by checking the encoded data markers, and applies the prediction process only to those specific nodes rather than all nodes. This localized approach maintains high compression ratios for suitable data while reducing unnecessary decoding operations.

The encoded data includes metadata or markers that automatically indicate which nodes were encoded using prediction processes. During decoding, the system uses these self-identifying markers to determine which nodes require prediction-based reconstruction, eliminating the need for complex external control logic. The data itself provides the information needed to guide the decoding process efficiently.