Unified Context Array for Point Cloud Coding
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Solution Overview
Problem
Predictive tree-based coding for point clouds requires a large number of contexts, which is computationally expensive and inefficient, especially in applications like autonomous driving and geographic information systems where data compression and decompression are critical.
Innovation Solution
The method reduces the number of contexts associated with syntax elements for predictive tree-based coding of point clouds by modifying the context array size, specifically reducing the total number of contexts required to encode syntax elements like ptn_residual_abs_log2[k], allowing for more efficient decoding and compression processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If predictive tree-based coding is used for point cloud compression, then compression efficiency is improved, but the number of contexts required increases leading to higher computational complexity
Solution Approach 1:
The patent merges multiple context arrays into a unified context model. Instead of maintaining separate context arrays for different syntax elements (ptn_residual_abs_log2[k], ptn_pred_mode, etc.), the invention combines them into a single shared context array that is updated collectively during encoding and decoding, thereby reducing the total number of contexts while preserving compression efficiency.
Solution Approach 2:
The patent makes the context array universal by having it serve multiple syntax elements simultaneously. A single context array is used to encode various predictive tree syntax elements including ptn_residual_abs_log2[k], ptn_pred_mode, and other geometric parameters, allowing one context structure to perform multiple functions that previously required separate dedicated arrays.
2Measurement precision
If a large number of contexts are used for encoding syntax elements, then encoding precision is improved, but decoding speed decreases due to increased computational load
Solution Approach 1:
The patent combines multiple context update operations into a single unified update process. Instead of updating separate context arrays independently for each syntax element, the invention performs a consolidated context update that maintains encoding precision while reducing the total number of computational steps required during decoding.
Solution Approach 2:
The patent changes the parameter of context array size from multiple large arrays to a single smaller unified array. This parameter change reduces the computational burden during decoding while maintaining the precision needed for accurate reconstruction of point cloud geometry through optimized context selection and update strategies.
3Device complexity
If the context array size is reduced, then computational complexity is decreased, but the accuracy of predictive coding may be compromised
Solution Approach 1:
The patent optimizes the parameters of the unified context array including its size, initialization values, and update rules. By carefully tuning these parameters, the invention achieves the desired balance between reduced computational complexity and maintained coding accuracy, ensuring that the smaller context array still provides sufficient statistical modeling for accurate predictive coding.
Solution Approach 2:
The patent implements feedback mechanisms in the unified context model where context updates are continuously adjusted based on the actual encoding performance and residual patterns. This feedback allows the system to adapt the context values dynamically, maintaining high coding accuracy even with a reduced number of contexts by learning from the data being encoded.
Data Source
AI summary
A method, computer program, and computer system is provided for decoding point cloud data. Data corresponding to a point cloud is received. A number of contexts associated with the received data is reduced based on reducing a size of an array corresponding to syntax elements for predictive tree-based coding of the point cloud. The data corresponding to the point cloud is decoded based on the reduced number of contexts.