Point Cloud Attribute Coding With Cross-Component Residuals
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Solution Overview
Problem
Existing point cloud compression technologies fail to effectively utilize inter-component correlations, leading to inefficiencies in coding multiple attribute channels, particularly in RGB domains, and require expensive nearest neighbor searches in 3D space.
Innovation Solution
Implement cross-component prediction schemes, such as linear models, to predict attribute values using reconstructed residuals and model parameters, reducing the need for nearest neighbor searches and enhancing coding efficiency by leveraging correlations among different attribute components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If cross-component prediction schemes are implemented to leverage inter-component correlations, then coding efficiency is improved, but device complexity increases due to additional prediction models and parameters
Solution Approach 1:
The prediction model is segmented into multiple components: a first prediction model for predicting a first prediction residual and a second prediction model for predicting a second prediction residual. Each model processes specific attribute components independently, allowing the system to leverage inter-component correlations while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent applies preliminary action by using previously reconstructed prediction residuals as input features for current predictions. The first prediction residual is reconstructed and then used as input to the second prediction model, creating a sequential dependency that improves coding efficiency by utilizing already-processed information.
2Loss of time
If cross-component prediction is used to reduce nearest neighbor searches, then computational overhead is reduced, but loss of information may increase due to prediction errors
Solution Approach 1:
The patent implements feedback mechanisms where the reconstructed first prediction residual is fed back into the prediction process as input for the second prediction model. This feedback loop allows the system to continuously refine predictions using previously reconstructed information, balancing computational efficiency with information accuracy.
Solution Approach 2:
The system dynamically adjusts prediction parameters based on the specific attribute components being processed. Different prediction models are applied to different components (e.g., RGB components), allowing the system to optimize for each component's specific characteristics while maintaining overall efficiency through parameter adaptation.
Data Source
AI summary
A method, computer program, and computer system is provided for point cloud coding. The method includes receiving, from a bitstream, data corresponding to a point cloud; obtaining from the data a first prediction residual of a first component from among a plurality of components of an attribute associated with the point cloud; reconstructing the first prediction residual; determining a predicted second prediction residual based on the reconstructed first prediction residual and at least one model parameter; obtaining a second prediction residual of a second component from among the plurality of components based on the predicted second prediction residual; reconstructing the second prediction residual; and decoding the data corresponding to the point cloud based on the reconstructed first prediction residual and the reconstructed second prediction residual.