Point Cloud Color Prediction Model for G-PCC Encoding Efficiency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In Geometry-based Point Cloud Compression (G-PCC) encoding, the correlation between three colour components (Y, U, V) prior to LOD partitioning and lifting transform increases encoded bits, leading to poor encoding efficiency.
Innovation Solution
A prediction method is applied to determine a spatial block for each encoding/decoding point, constructing a prediction model to characterize the relationship between colour components, calculating a prediction value, and using the difference as a residual for LOD partitioning and lifting transform, thereby reducing correlation and encoding bit overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If LOD partitioning and lifting transform are performed directly on colour components without removing correlation, then the encoding process is simple, but the encoded bit quantity increases and encoding efficiency deteriorates
Solution Approach 1:
The patent applies preliminary action by performing correlation removal through prediction before the LOD partitioning and lifting transform. The prediction model estimates one colour component from another, and the residual (difference between actual and predicted values) is then encoded. This preliminary processing step eliminates the harmful correlation effect before the main encoding operations, thereby reducing the encoded bit quantity while maintaining process simplicity.
2Quantity of substance
If correlation between colour components is removed using prediction model, then encoding bit overhead is reduced, but device complexity increases
Solution Approach 1:
The patent applies parameter changes by using different prediction models (e.g., linear prediction, adaptive prediction) and adjusting prediction parameters such as prediction coefficients and spatial block sizes. These parameter adjustments allow the system to optimize the balance between correlation removal effectiveness and computational complexity, reducing encoding bit overhead while controlling device complexity through parameter optimization rather than structural complexity.
3Measurement precision
If prediction model is constructed for each spatial block, then correlation removal accuracy is improved, but processing time increases
Solution Approach 1:
The patent applies segmentation by dividing the colour component data into multiple spatial blocks and constructing prediction models for each block separately. This segmentation allows the prediction to capture local correlation characteristics more accurately, improving correlation removal precision. The processing time increase is managed by processing blocks independently and in parallel, where possible, and by using efficient block-based algorithms that reduce overall computational overhead compared to global prediction approaches.
Data Source
AI summary
A prediction method, an encoder, a decoder, and a computer storage medium are provided. The prediction method applied to an encoder includes: determining a spatial block in which an encoding point is located; constructing a prediction model according to the spatial block; acquiring a value of a first colour component and a value of a second colour component of the encoding point; obtaining a prediction value of the second colour component of the encoding point by using the prediction model and the value of the first colour component; calculating a difference between the value of the second colour component and the prediction value of the second colour component, and using the obtained difference as a residual of the encoding point; and performing Level of Detail (LOD) partitioning and lifting transform based on the residual of the encoding point.