Point Cloud Attribute Coding with Selective Residual Quantization
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Solution Overview
Problem
Point cloud prediction in existing technologies is inaccurate, leading to reduced effectiveness in encoding and decoding processes.
Innovation Solution
A method involving lossless encoding of residual values for certain points and adaptive quantization of residual values for others based on quantization weights and step sizes, improving prediction accuracy without significantly increasing bitstream size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If quantization is applied to all residual values to reduce bitstream size, then transmission efficiency improves, but prediction accuracy deteriorates
Solution Approach 1:
The patent applies different encoding strategies to different points based on their importance: important points (with smaller quantization weights) undergo lossless encoding to preserve prediction accuracy, while less important points (with larger quantization weights) undergo quantization to reduce bitstream size. This local differentiation resolves the contradiction by optimizing each point's encoding according to its specific contribution to prediction accuracy.
Solution Approach 2:
The patent dynamically adjusts the quantization weight for each point based on its importance to prediction accuracy. By changing the quantization parameter (weight) according to the point's significance, the system achieves optimal balance between bitstream compression and prediction accuracy preservation for different points.
2Measurement precision
If lossless encoding is applied to all points to maximize prediction accuracy, then prediction accuracy improves, but bitstream size increases
Solution Approach 1:
The patent selectively applies lossless encoding only to important points that significantly contribute to prediction accuracy, while applying quantization to less important points. This localized application of lossless encoding preserves necessary prediction accuracy while avoiding the bitstream size increase that would result from applying lossless encoding to all points.
Solution Approach 2:
The patent applies lossless encoding partially - only to the extent necessary for important points - rather than excessively applying it to all points. This partial action achieves sufficient prediction accuracy while minimizing bitstream size.
Data Source
AI summary
A point cloud encoding method is provided. The method includes the following. A residual value of attribute information of a current point in a point cloud is determined according to a prediction value of the attribute information of the current point. An index of the current point is determined. Lossless encoding is performed on the residual value of the attribute information of the current point, when the index of the current point belongs to an index range of a first point. A quantized residual value of the attribute information of the current point is obtained by quantizing the residual value of the attribute information of the current point based on a quantization weight of the current point and a quantization step size of the current point, when the index of the current point is beyond the index range of the first point, and the quantized residual value is encoded.