Interframe Point Cloud Attribute Coding Adaptation
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Solution Overview
Problem
Current graph-based point cloud compression (G-PCC) methods face inefficiencies in interframe prediction due to sparse point cloud samples, which compromise compression efficiency, especially when there are not enough neighbors for reliable motion estimation and compensation.
Innovation Solution
A method and apparatus for interframe point cloud attribute coding that calculates a motion estimation unreliability measure based on the ratio of point cloud samples between frames, allowing for skipping or performing motion compensation depending on this measure, and employing spatio-temporal prediction using nearest neighbor samples from other frames to improve prediction performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If motion compensation is performed using current G-PCC methods, then prediction can be achieved, but compression efficiency deteriorates when point cloud samples are sparse and motion estimation unreliability is high
Solution Approach 1:
The patent applies dynamics by making the motion compensation process adaptive rather than static. The system dynamically adjusts between performing motion compensation and skipping it based on real-time assessment of motion estimation unreliability. When unreliability exceeds a threshold, the system switches to skipping motion compensation, thereby adapting to varying data conditions and avoiding degradation in compression efficiency while maintaining prediction accuracy when conditions are favorable.
Solution Approach 2:
The system employs self-service through automated detection and decision-making. The encoder automatically calculates motion estimation unreliability using the formula involving N_matched samples, compares it against a threshold, and autonomously decides whether to perform or skip motion compensation without external intervention. This self-managing mechanism optimizes compression efficiency by avoiding unnecessary processing when motion estimation is unreliable.
2Productivity
If motion compensation is skipped when motion estimation unreliability is high, then compression efficiency is maintained, but prediction accuracy may deteriorate
Solution Approach 1:
The patent implements feedback through the motion estimation unreliability calculation mechanism. The system continuously computes unreliability based on the ratio of matched samples (N_matched) to total samples, using the formula that incorporates this ratio. This feedback loop provides real-time information about the quality of motion estimation, enabling the system to make informed decisions about whether to perform or skip motion compensation, thereby balancing compression efficiency with prediction accuracy.
Solution Approach 2:
The system applies parameter changes by varying the operational mode (perform vs. skip motion compensation) based on the motion estimation unreliability parameter. When the unreliability parameter exceeds a predefined threshold, the system changes its behavior from performing motion compensation to skipping it. This parameter-driven approach allows dynamic optimization of compression efficiency while maintaining prediction accuracy when conditions are favorable.
3Reliability
If spatio-temporal prediction using interframe samples is employed, then prediction performance improves in sparse scenarios, but computational complexity increases
Solution Approach 1:
The patent applies partial action by selectively using interframe samples only when necessary. Instead of always performing full spatio-temporal prediction, the system calculates motion estimation unreliability and only employs interframe sample-based prediction when unreliability is high and motion compensation is skipped. This partial application of the complex prediction method reduces overall computational complexity while maintaining improved prediction performance in the specific scenarios where it is most needed.
Data Source
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AI summary
A method of interframe point cloud attribute coding is performed by at least one processor and includes obtaining, as a motion estimation unreliability measure of motion estimation of a target frame, a value inversely proportional to a ratio of a number of first point cloud samples of the target frame respectively with second point cloud samples of an interframe reference frame, to a number of point cloud samples in the target frame. The method further includes identifying whether the obtained motion estimation unreliability measure is greater than a predetermined threshold, based on the obtained motion estimation unreliability measure being identified to be greater than the predetermined threshold, skipping motion compensation of the target frame, and based on the obtained motion estimation unreliability measure being identified to be less than or equal to the predetermined threshold, performing the motion compensation of the target frame.