Progressive Mesh Compression With Encoding-Cost-Aware Edge Selection
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Solution Overview
Problem
Existing progressive mesh compression methods do not efficiently balance quality and cost, as they do not consider both error metric values and encoding costs when selecting edges for collapse, leading to suboptimal compression rates.
Innovation Solution
An encoding cost aware edge selection mechanism that determines priority values for edges based on both error metric values and estimated encoding costs, prioritizing edges with lower residual values for better entropy encoding and improved compression rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If edge selection is based only on error metric values, then quality is maintained, but encoding cost increases and compression rate deteriorates
Solution Approach 1:
The patent changes the selection criterion from using only error metric values to using a combined parameter that includes both error metric values and encoding costs. This allows the system to optimize for both quality and compression efficiency simultaneously by evaluating edges based on multiple parameters rather than a single metric.
Solution Approach 2:
The patent introduces a dynamic edge selection process where the priority of edges is determined adaptively based on both error metrics and encoding costs. The selection mechanism dynamically adjusts which edges to collapse by considering the combined impact on quality and compression rate, rather than following a static error-metric-only approach.
2Productivity
If more edges are selected for collapse, then compression rate improves, but quality deteriorates due to higher error metric values
Solution Approach 1:
The patent modifies the edge selection parameters to include both error metric values and encoding costs in the priority calculation. This dual-parameter approach enables the system to achieve higher compression rates by selecting edges that offer better overall value, rather than simply selecting edges with lowest error metrics, thus improving compression without excessive quality loss.
Solution Approach 2:
The patent applies partial collapse actions by selectively choosing edges based on combined criteria rather than collapsing all edges or following a rigid error-metric-based selection. This allows for optimized partial collapse that achieves good compression rates while maintaining acceptable quality levels.
3Productivity
If encoding cost is considered in edge selection, then compression rate improves, but device complexity increases due to additional calculations
Solution Approach 1:
The patent extends the selection parameters to include encoding cost estimates alongside error metric values. While this increases the complexity of the selection mechanism, it enables significantly improved compression rates by making more informed edge selection decisions that account for both quality and efficiency metrics.
Data Source
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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.