Vertex Position Coding for Dynamic Mesh Compression
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Solution Overview
Problem
Existing mesh compression standards struggle to efficiently compress dynamic meshes with time-varying connectivity information and attribute maps, particularly in real-time applications.
Innovation Solution
The proposed method employs a vertex position coding technique using parallelogram predictions, where candidate predictions are determined based on triangles in the mesh, and a prediction index is used to select the prediction, which is then encoded along with residual information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing mesh compression standards are used, then mesh data can be compressed, but the compression efficiency is insufficient for dynamic meshes with time-varying connectivity information
Solution Approach 1:
The patent applies dynamics by making the prediction process adaptive to time-varying mesh connectivity. The system dynamically selects from multiple candidate predictions (including parallelogram predictions from different triangles) based on current mesh state, allowing the compression method to adapt to changing connectivity information in dynamic meshes rather than relying on static compression assumptions
Solution Approach 2:
The patent changes parameters by introducing multiple candidate predictions with different prediction modes (parallelogram predictions from different triangles). The system varies the prediction approach by selecting different candidate predictions based on the number of available candidates and threshold comparisons, enabling efficient compression across different mesh configurations and time states
2Measurement precision
If multiple candidate predictions are used for vertex position coding, then prediction accuracy improves, but computational complexity increases
Solution Approach 1:
The patent applies partial action by not always using all available candidate predictions. Instead, it selectively uses a subset of candidates based on threshold comparisons (e.g., when the number of candidates is greater than or equal to a threshold, a predetermined prediction index is used; otherwise, the candidate with minimum prediction difference is selected). This partial selection approach maintains prediction accuracy while reducing unnecessary computational complexity
Solution Approach 2:
The patent changes the parameter of prediction selection by using a predetermined prediction index when the number of candidates meets a threshold condition. This parameter change (switching between different selection strategies based on candidate count) optimizes the balance between prediction accuracy and computational complexity by avoiding exhaustive search when sufficient candidates are available
Data Source
AI summary
In a method, a plurality of candidate predictions of a current vertex is determined. The current vertex is included in a plurality of vertices of a mesh. The plurality of candidate predictions is determined based on a plurality of triangles in the mesh. Each of the plurality of predictions corresponds to a respective candidate prediction. A prediction of the current vertex is determined from the plurality of candidate predictions. The prediction is identified by a prediction index that is predetermined based on a number of the plurality of candidate predictions of the current vertex being equal to or larger than a threshold value. Prediction information of the current vertex is encoded based on the determined prediction. The prediction information includes the prediction index identifying the prediction of the current vertex.