Point Cloud Compression Using Global Motion Matrix
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Solution Overview
Problem
Existing technologies for compressing point cloud frames do not efficiently utilize global motion matrices, particularly when local motion compression is involved, leading to suboptimal compression efficiency.
Innovation Solution
A method that adjusts the application of a global motion matrix based on whether local motion compression is performed, including generating and correcting the global motion matrix using quantization coefficients and correction coefficients, and applying it differently depending on the presence of global motion characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a global motion matrix is applied to all point cloud data regardless of local motion compression, then the compression process is simplified, but compression efficiency deteriorates
Solution Approach 1:
The patent applies dynamic adaptation by adjusting the global motion matrix application strategy based on the presence or absence of local motion compression. When local motion compression is performed, the global motion matrix is applied to all points; when local motion compression is not performed, the global motion matrix is applied selectively only to points exhibiting global motion characteristics. This dynamic adjustment optimizes compression efficiency while adapting to different processing scenarios.
Solution Approach 2:
The patent implements local quality differentiation by applying the global motion matrix differently to different portions of the point cloud data based on local motion characteristics. Specifically, when local motion compression is absent, only points with global motion characteristics receive the global motion matrix application, while other points are processed differently. This localized approach improves compression efficiency by avoiding unnecessary processing steps.
2Measurement precision
If the global motion matrix is corrected using quantization coefficients, then compression accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent modifies the global motion matrix by applying corrections based on quantization coefficients that were used during point cloud quantization. This parameter adjustment compensates for errors introduced by quantization, thereby improving compression accuracy. The correction process adjusts the motion matrix parameters to better reflect the actual point cloud transformations, ensuring more accurate compression results.
3Reliability
If local motion compression is always applied, then local motion details are preserved, but overall compression efficiency deteriorates due to redundant processing
Solution Approach 1:
The patent applies partial action by conditionally applying local motion compression only where necessary. Instead of uniformly applying local motion compression to all point cloud data, the system selectively applies it based on the presence of local motion characteristics. This partial application approach preserves local motion details where needed while avoiding redundant processing elsewhere, thereby improving overall compression efficiency.
Data Source
AI summary
Disclosed herein is a method for compressing point cloud data. The method includes quantizing input point cloud data, generating a global motion matrix based on the quantized point cloud data, applying the global motion matrix based on whether local motion compression is performed, and compressing data to which the global motion matrix is applied.