3D Point Cloud Encoding With Differential Quantization Parameters
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Solution Overview
Problem
Existing methods for encoding and decoding three-dimensional data, particularly point cloud data, lack efficiency in terms of coding, leading to inefficiencies in data transmission and processing.
Innovation Solution
A method involving quantizing geometry and attribute information of three-dimensional points using multiple quantization parameters, generating a bitstream that includes these quantized values and their differences, to improve coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If point cloud data is compressed using existing encoding methods, then data transmission efficiency is improved, but coding complexity increases and processing load increases
Solution Approach 1:
The patent applies parameter changes by using multiple quantization parameters (QP) to control the precision of different attribute information (x, y, z coordinates and color values). By dynamically adjusting QP values for different spatial regions and attribute types, the encoding achieves optimal balance between compression ratio and data quality, improving transmission efficiency while managing coding complexity through systematic parameter control
Solution Approach 2:
The patent implements local quality by applying different quantization parameters to different spatial regions and attribute types within the point cloud data. Important regions (e.g., foreground objects) use higher precision (lower QP) while less important regions (e.g., background) use lower precision (higher QP), achieving efficient compression without uniformly increasing coding complexity across the entire dataset
2Productivity
If point cloud data is compressed using existing encoding methods, then data transmission efficiency is improved, but processing load increases
Solution Approach 1:
The patent applies partial action by selectively processing different portions of point cloud data with different quantization levels. Instead of uniformly applying high-compression algorithms to all data, the system processes only necessary portions with appropriate precision, reducing overall processing load while maintaining transmission efficiency for the most important data elements
3Productivity
If multiple quantization parameters are used for geometry and attribute information, then coding efficiency is improved, but data structure complexity increases
Solution Approach 1:
The patent applies segmentation by dividing point cloud data into different spatial regions and attribute types, then applying appropriate quantization parameters to each segment. The data structure is organized into separate components (geometry information with x, y, z coordinates and attribute information with color values), allowing independent optimization of each segment while maintaining overall coding efficiency
Data Source
AI summary
A three-dimensional data encoding method includes: quantizing geometry information of each of three-dimensional points, using a first quantization parameter; quantizing a first luminance using a second quantization parameter and quantizing a first chrominance using a third quantization parameter, the first luminance and the first chrominance indicating a first color among attribute information of each of the three dimensional points; and generating a bitstream including the geometry information quantized, the first luminance quantized, the first chrominance quantized, the first quantization parameter, the second quantization parameter, and a first difference between the second quantization parameter and the third quantization parameter.