G-PCC Planar Mode Buffer Simplification for Context Derivation
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Solution Overview
Problem
Conventional methods for determining context indices in Geometry Point Cloud Compression (G-PCC) require significant hardware resources due to the need to calculate Manhattan distances between nodes, leading to increased complexity and buffer size.
Innovation Solution
Storing only the maximum coordinate of a pair of coordinates for an applicable node and determining a context index based on a distance value relative to this maximum coordinate, reducing the need to store both coordinates and minimizing hardware requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If both coordinates of an applicable node are stored for context derivation, then measurement precision of distance is improved, but device complexity and buffer size increase
Solution Approach 1:
The patent extracts only the necessary coordinate information (maximum coordinate of the pair) from the full node coordinate data. By storing only the maximum coordinate rather than both coordinates, the solution maintains sufficient precision for distance calculations while significantly reducing buffer size and hardware complexity requirements.
Solution Approach 2:
The patent applies partial action by storing only the maximum coordinate (one value) instead of both coordinates (two values). This partial storage approach provides sufficient information for the intended purpose (distance calculation for context derivation) without the excessive storage requirements of keeping both coordinates, thus optimizing the trade-off between precision and complexity.
2Measurement precision
If Manhattan distance calculation is performed for context index determination, then context derivation accuracy is improved, but use of energy and hardware resources increase
Solution Approach 1:
The patent extracts only the maximum coordinate from the node coordinates, which is sufficient for calculating the distance value needed for context index determination. This extraction eliminates the need for complex Manhattan distance calculations involving both coordinates, thereby reducing hardware resource consumption and energy usage while maintaining adequate accuracy for context derivation.
Solution Approach 2:
The patent changes the parameter being stored from both coordinates to only the maximum coordinate. This parameter change simplifies the subsequent distance calculation, reducing the computational complexity and hardware resources required for context index determination while maintaining the necessary accuracy for effective entropy coding.
Data Source
AI summary
A method of encoding point cloud data comprises storing, in a buffer, a maximum coordinate of a pair of coordinates of an applicable node, wherein the applicable node is a most-recently encoded node with a same position as a current node along an applicable axis and the pair of coordinates are for axes different from the applicable axis; determining a context for a planar mode plane position of the current node, wherein determining the context for the planar mode plane position comprises determining, based on the maximum coordinate of the pair of coordinates of the applicable node, a distance value representing a distance between the current node and the applicable node; determining an increment value that indicates whether the distance value is greater than a threshold; and determining the context index based on the increment value; and entropy encoding the planar mode plane position using the determined context.