Point Cloud Encoder Dynamic Quantization for Compression Efficiency
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Solution Overview
Problem
The existing methods for compressing point cloud data, particularly in video production, face challenges due to the large number of points, leading to inefficient transmission and poor quantization effects.
Innovation Solution
A method and system for point cloud encoding and decoding that improves the quantization effect by acquiring attribute information, processing it to obtain residual values, and quantizing these values using a target quantization mode that includes multiple quantization modes such as setting quantization parameter increments, weighting residual values, and performing lossless encoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If point cloud data is compressed by quantizing attribute information, then transmission efficiency is improved, but quantization effect deteriorates
Solution Approach 1:
The patent applies dynamic quantization by adjusting quantization parameters based on the characteristics of different point cloud regions. The encoder dynamically selects quantization steps and modes according to local variance and importance metrics, allowing finer quantization for important regions and coarser quantization for less important regions, thus improving overall quantization effect while maintaining transmission efficiency
Solution Approach 2:
The patent implements local quality enhancement by applying different quantization strategies to different spatial regions of the point cloud. Important regions (e.g., regions with high curvature, edges, or semantic significance) receive lower quantization steps and higher priority encoding, while less important regions use higher quantization steps, achieving better local quantization effects where needed
2Manufacturing precision
If quantization parameter increment is set for points in point cloud, then quantization effect is improved, but device complexity increases
Solution Approach 1:
The patent segments the point cloud into multiple regions or groups of points, applying different quantization parameter increments to each segment. This segmentation allows the system to manage complexity by processing smaller subsets independently while achieving improved overall quantization effect through localized parameter adjustments
Solution Approach 2:
The patent changes quantization parameters dynamically based on point cloud characteristics. By adjusting quantization steps, precision levels, and encoding modes according to local variance and importance metrics, the system improves quantization effect without requiring uniformly high complexity across all points
3Manufacturing precision
If residual values of points are weighted, then quantization effect is improved, but loss of information increases
Solution Approach 1:
The patent employs feedback mechanisms where the encoder evaluates the impact of weighting on reconstruction quality and adjusts weighting factors accordingly. By monitoring distortion metrics and information loss, the system optimizes weighting to improve quantization effect while minimizing unnecessary information loss through iterative refinement
Data Source
AI summary
A point cloud encoding method, including: obtaining attribute information about the attributes of a current point in a point cloud; processing the attribute information of the current point to obtain a residual value of the attribute information of the current point; using a target quantization manner, quantizing the residual value of the attribute information of the current point to obtain a quantized residual value of the attribute information of the current point; the target quantization manner comprises at least two of the following quantization manners: a first quantization manner, a second quantization manner, and a third quantization manner.