Progressive Mesh Compression Using Vertex Split Connectivity Prediction
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Solution Overview
Problem
Existing progressive mesh compression methods face inefficiencies in balancing quality and cost, particularly in edge collapse operations, due to inadequate consideration of error metrics and encoding costs, leading to suboptimal compression rates and potential distortion.
Innovation Solution
A method for progressive mesh compression that includes determining priority values for edges based on error metrics and estimated encoding costs, predicting vertex split connectivity by identifying partitioning vertices, and entropy encoding vertex split information to improve compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing progressive mesh compression methods are used, then mesh geometry can be encoded in stages with coarser LODs first, but compression efficiency is suboptimal due to inadequate consideration of error metrics and encoding costs
Solution Approach 1:
The patent changes the parameters used for edge selection from simple heuristics to a comprehensive priority value that incorporates both error metrics (quality) and encoding costs (bit rate). This dual-parameter approach optimizes the compression efficiency by selecting edges that provide the best trade-off between quality preservation and compression ratio.
Solution Approach 2:
The patent implements a feedback mechanism where the encoder communicates priority values and edge selection criteria to the decoder, enabling the decoder to replicate the same edge collapse decisions. This feedback loop ensures that both encoder and decoder operate with the same quality-cost balance, improving overall compression efficiency.
2Manufacturing precision
If edge collapse operations are performed without considering encoding costs, then quality can be maintained, but compression rate deteriorates due to suboptimal edge selection
Solution Approach 1:
The patent introduces encoding cost as a new parameter in the edge selection process. By calculating priority values that include both error metrics and encoding costs, the system can select edges that minimize bit rate while maintaining acceptable quality, thereby improving compression rate without sacrificing mesh quality.
Solution Approach 2:
The patent applies different selection criteria to different regions of the mesh based on local characteristics. Edges in visually important regions are selected based on higher quality thresholds, while edges in less important regions can be collapsed more aggressively to improve compression rate. This local quality approach optimizes the overall compression rate while maintaining perceptual quality.
3Ease of operation
If vertex split connectivity information is encoded without prediction, then decoding is simple, but compression efficiency deteriorates due to redundant information transmission
Solution Approach 1:
The patent performs preliminary action by predicting vertex split connectivity before encoding. The encoder predicts which vertices will be connected after edge collapse and communicates only the differences between predicted and actual connectivity. This preliminary prediction reduces the amount of information that needs to be transmitted, improving compression efficiency while maintaining decoding simplicity.
Solution Approach 2:
The patent uses copying by having the decoder replicate the edge collapse process using the same priority value calculations and selection criteria as the encoder. This copying approach allows the decoder to reconstruct the mesh without receiving detailed connectivity information, significantly improving compression efficiency while keeping decoding simple.
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.