Point Cloud Color Decoding Using Kalman-Filtered Chroma
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Solution Overview
Problem
The challenge in point cloud compression lies in improving the prediction effect of color information during the encoding process.
Innovation Solution
A point cloud decoding method that utilizes a Kalman filtering algorithm to enhance the reconstruction accuracy of attribute information, specifically converting initial chroma values into final values through filtering, and a point cloud encoding method that processes position and attribute information to obtain residual values for lossless encoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional prediction methods are used for color information, then the encoding process is simple, but the prediction effect and reconstruction accuracy are insufficient
Solution Approach 1:
The patent transforms the initial reconstructed chroma value through mathematical operations (conversion to luma and chroma components, Kalman filtering) to improve reconstruction accuracy. This parameter transformation approach enhances prediction quality without fundamentally changing the encoding framework
Solution Approach 2:
The patent employs Kalman filtering which is a feedback-based algorithm that uses past predictions and current observations to refine the chroma value. This feedback mechanism continuously improves prediction accuracy by incorporating historical data and adjusting based on prediction errors
2Measurement precision
If more points are obtained with higher accuracy and speed, then the point cloud quality improves, but the challenge for computer storage and transmission increases
Solution Approach 1:
The patent extracts only the essential residual information after prediction rather than storing complete point cloud data. By separating and encoding only the difference between predicted and actual values, the method significantly reduces data volume while preserving accuracy
Solution Approach 2:
The patent discards redundant information that can be predicted from spatial relationships, and recovers the essential variations through residual encoding. This allows efficient storage and transmission by keeping only the non-redundant information
Data Source
AI summary
A point cloud decoding method, a point cloud encoding method, and a decoder are provided in implementation of the disclosure. The decoding method includes the following. A bitstream of a point cloud is parsed to obtain an initial reconstructed value of attribute information of a target point in the point cloud. The initial reconstructed value is converted into an initial luma value and an initial chroma value. A final chroma value is obtained by filtering the initial chroma value with a Kalman filtering algorithm. A final reconstructed value of the attribute information of the target point is obtained based on the final chroma value and the initial luma value. A decoded point cloud is obtained according to the final reconstructed value of the attribute information of the target point.