3D Data Compression via Coupling Coefficients and Base Data Selection
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Solution Overview
Problem
Existing methods for compressing three-dimensional shape data result in low data compression ratios, deteriorated resolution, and inability to increase compression ratio with more data sets, as they uniformly compress arbitrary data sets without considering content-specific optimization.
Innovation Solution
A data compression method that determines corresponding points between input data and reference data, computes coupling coefficients for synthesizing data using a base data group, and adjusts the base data group for improved accuracy, allowing for high compression ratios without reducing resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional vertex deletion methods are used to compress three-dimensional shape data, then the data amount is reduced, but the resolution and manufacturing precision deteriorate
Solution Approach 1:
The patent creates a compressed representation (coupling coefficient) that copies only the essential differences between the input data and base data, rather than copying or deleting actual vertex data. This allows high compression ratios while preserving resolution because the full-precision base data is retained and only small coefficient adjustments are stored.
Solution Approach 2:
The patent transforms the data representation from storing absolute vertex coordinates to storing relative coupling coefficients that parameterize the transformation from base data to target data. This parameter change enables high compression ratios while maintaining manufacturing precision because the base data retains full precision and only small coefficient variations are compressed.
2Productivity
If uniform compression methods are applied to arbitrary data sets, then processing simplicity is maintained, but compression ratio and adaptability remain low
Solution Approach 1:
The patent performs preliminary selection of base data sets that match the input data characteristics before compression. This preliminary action enables higher compression ratios by ensuring the base data is similar to the target data, reducing the coupling coefficients needed. The system prepares multiple base data sets in advance and selects the most appropriate ones for each compression task.
Solution Approach 2:
The patent dynamically adapts the compression process by selecting different base data sets based on the characteristics of the input data. Rather than using a fixed uniform compression method, the system adjusts its approach by choosing the most suitable base data, thereby improving compression ratio while managing complexity through intelligent adaptation.
3Measurement precision
If base data sets are increased to improve compression accuracy, then restoration precision improves, but data storage requirements increase
Solution Approach 1:
The patent stores only the essential coupling coefficients rather than copying or storing complete base data sets. This allows the system to maintain multiple base data sets for high restoration precision while minimizing storage requirements, as only small coefficient values (not entire data sets) need to be stored for each compressed object.
Solution Approach 2:
The patent changes the storage parameter from storing complete base data sets to storing only coupling coefficients that parameterize the relationship between base data and target data. This parameter transformation enables high restoration precision with multiple base data sets while dramatically reducing storage requirements, as coefficients occupy minimal space compared to full data sets.
Data Source
AI summary
Three-dimensional data is compressed at a high compression ratio without deteriorating resolution and accuracy, by computing a coupling coefficient from input three-dimensional data and a three-dimensional base data group obtained from a plurality of objects and outputting the coupling coefficient as compressed data. Specifically, the three-dimensional data is input to corresponding point determination means. The corresponding point determination means generates three-dimensional data to be synthesized in which vertexes of the three-dimensional data are made to correspond to vertexes of three-dimensional reference data serving as a reference to determine association relationship between vertexes. Coefficient computation means computes a coupling coefficient for coupling a three-dimensional base data group used for synthesis of three-dimensional data to synthesize three-dimensional data to be synthesized, and outputs the computed coupling coefficient as the compressed data of the three-dimensional data.