Vertex Split Connectivity Prediction for Progressive Mesh Compression
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Solution Overview
Problem
Existing progressive mesh compression techniques face inefficiencies in balancing quality and cost, particularly in edge collapse operations, as they do not effectively consider residual values and encoding costs, leading to suboptimal compression rates and distortion.
Innovation Solution
The proposed method determines priority values for edges based on error metric values and estimated encoding costs, using entropy encoding and predicting vertex split connectivity to improve edge selection and compression efficiency, incorporating full-edge and half-edge collapse operations to generate levels of detail.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing progressive mesh compression techniques are used without considering residual values and encoding costs, then the compression process is simpler, but the compression rate and quality are suboptimal
Solution Approach 1:
The patent changes the parameters used for edge selection from simple geometric criteria to a composite metric that includes residual values and encoding costs. This allows the system to achieve better compression rates by selecting edges that minimize both geometric error and bitstream cost, rather than relying on simpler heuristics.
Solution Approach 2:
The patent performs preliminary calculations of residual values and encoding costs for all candidate edges before the actual compression process. This pre-computation allows the encoder to make informed decisions about which edges to collapse at each level of detail, improving overall compression efficiency without adding complexity during the main encoding loop.
2Manufacturing precision
If vertex split connectivity information is not predicted, then the encoding process is faster, but the decoding efficiency and quality reconstruction are reduced
Solution Approach 1:
The patent implements a feedback mechanism where the encoder predicts vertex split connectivity information and sends it to the decoder. The decoder uses this predicted information to quickly reconstruct the mesh geometry at each level of detail, reducing decoding time while maintaining high reconstruction precision. The prediction is based on the local geometry and connectivity patterns observed during encoding.
3Reliability
If full-edge and half-edge collapse operations are not differentiated, then the encoding process is simpler, but the quality-cost balance is suboptimal
Solution Approach 1:
The patent applies different collapse operations (full-edge vs. half-edge) based on local geometric properties and connectivity requirements. By analyzing the local structure around each candidate edge, the system selects the appropriate operation type to maintain geometry quality while considering the encoding cost. This local adaptation allows better quality-cost balance without requiring a complete redesign of the encoding framework.
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 identifying a pair of partitioning vertices to be connected to a split vertex associated with a collapse of an edge, creating the split vertex by collapsing the edge, encoding partitioning vertex information associated with the pair of partitioning vertices, the encoding of the partitioning vertex information based on an ordering of vertices of an umbrella of the split vertex, and the ordering of vertices of the umbrella determined based on a geometric shape and connectivity of the umbrella, and generating vertex split information that includes the partitioning vertex information. In another example implementation, the method may include entropy encoding the vertex split information prior to being transmitted.