Predictor Index Signaling in Geometry-Based Point Cloud Compression
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Solution Overview
Problem
Current point cloud compression techniques face computational complexity due to the coupling of predictor index parsing and attribute value reconstruction, which increases processing overhead and limits efficiency in decoding and encoding processes.
Innovation Solution
The proposed solution decouples the parsing of the predictor index from the reconstruction of attribute values in point cloud compression, allowing for independent signaling of jointly coded values and residual values, reducing computational complexity and enabling more efficient parsing and reconstruction processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If predictor index parsing is coupled with attribute value reconstruction, then parsing can be performed in context of full reconstruction, but computational complexity increases and processing efficiency decreases
Solution Approach 1:
The patent segments the parsing process into two independent parts: (1) parsing predictor indexes using only previously decoded predictor values, and (2) reconstructing attribute values using the parsed predictor indexes. This segmentation allows the parsing stage to operate independently without requiring full reconstruction context, thereby reducing computational complexity while maintaining parsing accuracy.
Solution Approach 2:
The patent performs preliminary parsing of predictor indexes before completing the attribute value reconstruction process. By parsing predictor indexes first using available predictor values from previous points, the system prepares the necessary indexing information in advance, which then guides the subsequent attribute reconstruction phase. This preliminary action reduces the computational burden during the main reconstruction process.
2Measurement precision
If predictor index parsing waits for full attribute reconstruction, then parsing decisions can be more accurate, but processing speed decreases
Solution Approach 1:
The patent performs preliminary parsing of predictor indexes using predictor values that are already available from previously decoded points. This preliminary parsing action occurs before the current attribute value reconstruction is complete, allowing the system to prepare parsing decisions in advance without waiting for full reconstruction context, thereby improving processing speed while maintaining sufficient parsing accuracy.
Solution Approach 2:
The patent segments the processing into independent parsing and reconstruction phases. The parsing phase uses only the predictor values from previous points (which are already reconstructed), separating this from the current attribute reconstruction phase. This segmentation enables faster processing by avoiding the need to wait for complete reconstruction before making parsing decisions.
3Productivity
If joint coding of residual values and predictor index is implemented, then data compression efficiency improves, but decoding complexity increases
Solution Approach 1:
The patent segments the decoding process into two independent stages: first decoding predictor indexes from the jointly coded bitstream using relatively simple parsing logic, and then using these indexes to select predictors for attribute reconstruction. This segmentation maintains compression efficiency through joint coding while reducing decoding complexity by breaking down the overall process into manageable, independent steps that can be executed sequentially.
Solution Approach 2:
The patent introduces predictor indexes as an intermediary element between the jointly coded bitstream and the final attribute reconstruction. The jointly coded data first yields predictor indexes, which then serve as intermediaries to select appropriate predictors from the predictor list. This intermediary step simplifies the decoding process by providing a clear two-stage pathway: decode indexes, then use indexes for reconstruction.
Data Source
AI summary
A method for decoding point cloud data comprises, based on a comparison of a maximum difference value and a threshold, applying an inverse function to a set of one or more jointly coded values to recover (i) residual values for attribute values of a current point of point cloud data and (ii) a predictor index that indicates a predictor in a predictor list, wherein predictors in the predictor list are based on attribute values of one or more neighbor points; determining predicted attribute values based on the predictor index; and reconstructing the attribute values of the current point based on the residual values and the predicted attribute values.