Edge Selection for Progressive Mesh Compression
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Solution Overview
Problem
Current progressive mesh compression methods do not effectively balance quality and encoding cost, leading to suboptimal compression rates due to the lack of consideration for both error metric values and encoding costs in edge selection.
Innovation Solution
The method determines priority values for edges based on both error metric values and estimated encoding costs, selecting edges for collapse to generate vertex split information and entropy encoding it for improved compression, allowing for better rate distortion balance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional progressive mesh compression methods are used that only consider error metric values for edge selection, then the quality of the compressed mesh is maintained, but the compression rate is suboptimal due to ignoring encoding costs
Solution Approach 1:
The patent changes the selection criteria parameter from only error metric values to a combined priority value that incorporates both error metric values and encoding costs. This parameter change enables the system to simultaneously optimize for both mesh quality and compression rate by selecting edges based on a composite metric that balances geometric fidelity with encoding efficiency.
2Measurement precision
If edges are selected for collapse based only on error metric values, then the geometric accuracy is preserved, but the encoding efficiency is reduced due to higher encoding costs
Solution Approach 1:
The patent introduces a new parameter (priority value) that combines error metric values and encoding costs, transforming the edge selection process from quality-only optimization to a balanced optimization that considers both geometric accuracy and encoding efficiency.
Solution Approach 2:
The patent implements a feedback mechanism where the encoder estimates encoding costs for different edge collapse operations and uses this information to adjust edge selection priorities. This feedback loop allows the system to adaptively choose edges that minimize total encoding cost while maintaining acceptable geometric accuracy.
3Productivity
If more edges are collapsed to achieve higher compression rates, then the compression efficiency improves, but the mesh quality degrades due to increased error accumulation
Solution Approach 1:
By changing the selection parameter to include encoding costs, the system can identify edges that offer better compression efficiency with minimal quality loss, enabling more aggressive collapse operations while controlling error accumulation.
Solution Approach 2:
The patent allows for selective application of edge collapse operations based on priority values, enabling the system to perform partial collapses on high-priority edges while skipping low-priority edges that would cause excessive quality degradation, thus achieving balanced compression.
Data Source
AI summary
An encoder includes a processor and a memory. The encoder may perform a method of progressive compression. In one example implementation, the method may include determining a priority value for each edge of a plurality of edges, the priority value of an edge of the plurality of edges determined based on an error metric value and an estimated encoding cost associated with the edge. The method may further include determining a set of edges for collapse, the set of edges determined from the plurality of edges based on the priority values and collapsing the set of edges and generating vertex split information. In some implementations, the method may include entropy encoding the vertex split information.