Inter-Channel Prediction for Point Cloud Attribute Coding
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Solution Overview
Problem
Current graph-based point cloud compression (G-PCC) methods face challenges in efficiently coding point cloud attributes, particularly due to redundancy among channels and the lack of orthonormality in color space conversion, which affects coding efficiency and quality.
Innovation Solution
The proposed method employs inter-channel prediction and transform techniques, specifically using a YCoCg-R transform and inter-channel residual prediction to decorrelate channels, maintaining near-lossless reconstruction and controlling signal fidelity in the original RGB color space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional color space conversion methods are used for point cloud attribute coding, then the coding process is simplified, but channel redundancy remains and coding efficiency deteriorates
Solution Approach 1:
The coding process is segmented into two distinct stages: first performing prediction transform on the original RGB attributes, then performing color space conversion on the prediction residuals. This segmentation allows each stage to optimize for its specific purpose, improving overall coding efficiency while maintaining process simplicity.
Solution Approach 2:
The prediction transform is performed as a preliminary action before color space conversion. By removing redundancy through prediction first, the subsequent color space conversion operates on already-decorrelated data, maximizing coding efficiency without complicating the overall process.
2Ease of manufacture
If color space conversion is performed before prediction, then the transformation is straightforward, but the lack of orthonormality degrades reconstruction quality
Solution Approach 1:
The conventional order of operations is inverted: instead of converting color space first and then predicting, the method performs prediction transform first on the original RGB attributes, then applies color space conversion to the residuals. This inversion preserves reconstruction quality by maintaining orthonormality in the prediction stage.
3Device complexity
If inter-channel prediction is not used, then the coding process is simpler, but channel redundancy increases and compression efficiency deteriorates
Solution Approach 1:
The attribute coding is segmented into prediction transform and color space conversion stages, with inter-channel prediction applied in the first stage. This segmentation introduces sophisticated redundancy removal without overwhelming complexity, as each stage has a specific function.
Solution Approach 2:
The method changes the parameter representation by performing prediction in the original RGB color space rather than in a converted color space. This parameter change enables effective inter-channel prediction while avoiding the orthonormality issues of traditional approaches.
Data Source
AI summary
A method of interframe point cloud attribute coding is performed by at least one processor and includes coding a first color attribute of a point of a point cloud to obtain a first reconstructed residual, coding a second color attribute of the point to obtain a second reconstructed residual, and determining a quantization index of the second reconstructed residual, based on the first reconstructed residual and the second reconstructed residual. The method further includes updating the second reconstructed residual, based on the quantization index and the first reconstructed residual.