Point Cloud Attribute Coding Using YCoCg-R Transform
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Solution Overview
Problem
Current point cloud compression technologies face challenges in efficiently encoding and decoding color attributes due to redundancy among channels and the lack of orthonormality in color space conversion, which affects coding efficiency and quality, especially in lossless transforms.
Innovation Solution
The method involves using a YCoCg-R transform as a lossless in-loop transform for prediction residuals in Differential Pulse Code Modulation (DPCM) to decorrelate inter-channel dependencies and introducing additional prediction steps to predict residual values of other channels, maintaining near-lossless and lossless reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional color space conversion is used for point cloud attribute coding, then compression efficiency may be improved through decorrelation, but orthonormality is lost affecting reconstruction quality
Solution Approach 1:
The patent applies YCoCg-R transform instead of traditional YCbCr or RGB transforms. This parameter change in the color space conversion method provides both decorrelation for compression efficiency and near-lossless reconstruction capability, resolving the contradiction between compression efficiency and reconstruction quality
Solution Approach 2:
The patent introduces adaptive prediction mechanisms that dynamically adjust prediction strength and methodology based on local characteristics. The inter-channel prediction adapts to local variations in the point cloud data, maintaining high reconstruction quality while achieving compression through dynamic decorrelation
2Productivity
If inter-channel prediction is applied to reduce redundancy, then coding efficiency improves, but complexity of the encoding/decoding process increases
Solution Approach 1:
The patent segments the prediction process into distinct stages: intra-channel prediction first, then inter-channel prediction. This segmentation allows the decoder to reconstruct channels in a structured sequence (Y channel first, then C0 and Cg channels), reducing complexity by breaking down the overall complex operation into manageable segments
Solution Approach 2:
The patent performs preliminary prediction of residual values before final reconstruction. By predicting residual values from already decoded channels and applying these predictions early in the decoding process, the system reduces the complexity of subsequent processing steps while maintaining coding efficiency
3Manufacturing precision
If lossless transform is used to maintain fidelity, then reconstruction quality improves, but compression efficiency decreases due to increased data
Solution Approach 1:
The patent uses YCoCg-R transform parameters that enable near-lossless reconstruction while maintaining good compression characteristics. The specific parameter selection in the transform allows preserving important visual information while discarding less significant data, achieving both fidelity and compression efficiency
Solution Approach 2:
The patent applies different prediction and transform strategies to different regions of the point cloud data based on local characteristics. In regions requiring high fidelity, lossless transforms are applied, while in other regions, more aggressive compression is used, achieving overall balance between fidelity and compression efficiency
Data Source
AI summary
A method of interframe point cloud attribute decoding of video data is performed by at least one processor and includes: obtaining a first reconstructed residual; obtaining a quantization index of a second reconstructed residual; obtaining the second reconstructed residual, based on the obtained quantization index of the second reconstructed residual and the obtained first reconstructed residual; and obtaining a color attribute of a point of a point cloud by decoding the obtained second reconstructed residual or decoding a transform residual based on the obtained second reconstructed residual. The first reconstructed residual and the second reconstructed residual are each a reconstructed residual of a respective channel of the color attribute.