Quantization Parameter Scaling in Geometry-Based Point Cloud Compression
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Solution Overview
Problem
Existing point cloud compression technologies face inefficiencies in adapting to varying densities within point clouds, leading to suboptimal coding efficiency due to fixed quantization and scaling methods that do not account for region-specific point densities.
Innovation Solution
The implementation of techniques for quantization parameter (QP) selection and signaling that allow for multiple levels of granularity in quantization at the encoder and reciprocal scaling at the decoder, enabling adaptation to content-specific characteristics, particularly in regions with varying point densities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fixed quantization and scaling methods are used, then device complexity is reduced, but coding efficiency deteriorates due to inability to adapt to varying point densities
Solution Approach 1:
The patent implements dynamic quantization parameter (QP) selection that adapts to local point cloud density characteristics. The encoder determines different QP values for different regions based on point density, and the decoder applies corresponding reciprocal scaling factors. This dynamic adaptation allows the system to optimize coding efficiency for varying densities without requiring overly complex preprocessing, resolving the contradiction between coding efficiency and device complexity.
2Productivity
If region-specific quantization parameters are implemented, then coding efficiency for dense regions improves, but device complexity increases due to multiple QP management
Solution Approach 1:
The patent applies different quantization parameters to different regions of the point cloud based on local density characteristics. Dense regions use QP values optimized for their characteristics while sparse regions use different QP values. This local quality approach allows the system to improve coding efficiency specifically where needed (in dense regions) without uniformly increasing complexity across the entire system, as each region is handled independently with appropriate parameters.
Data Source
AI summary
A G-PCC encoder and G-PCC decoder may quantize and scale, respectively, a position of a child node. The G-PCC encoder may control the precision of the quantization and scaling using a quantization parameter (QP) value and a parameter value k, wherein the parameter value k specifies a number of QP points per doubling of a scaling step size to be used at the G-PCC decoder.