Selective Predictor for 2D Geometry Compression
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Solution Overview
Problem
Current vector data compression algorithms in Geographic Information Systems (GIS) achieve low compression rates due to the simplicity of prediction methods, which result in large correction vectors and inefficient data encoding.
Innovation Solution
A selective predictor is introduced that calculates multiple prediction vectors based on several previous vertices and selects the closest one to minimize prediction error, reducing correction vectors and improving encoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If simple prediction encoding is used, then implementation simplicity is improved, but compression rate deteriorates
Solution Approach 1:
The prediction process is segmented into multiple prediction modes (e.g., horizontal prediction, vertical prediction, diagonal prediction) rather than using a single simple prediction method. Each mode segments the prediction approach to handle different geometric patterns in the data, allowing the system to select the most appropriate prediction mode for each vertex, thereby improving compression rate while maintaining manageable implementation complexity through modular design.
Solution Approach 2:
The prediction method transitions from a static simple prediction approach to a dynamic adaptive prediction approach. The system dynamically selects among multiple prediction modes based on the actual data patterns observed during encoding, allowing the prediction strategy to adapt to different geometric configurations in the spatial data, thus improving compression efficiency without requiring overly complex fixed structures.
2Loss of substance
If multiple prediction vectors are calculated and selected, then compression rate is improved, but device complexity increases
Solution Approach 1:
Instead of calculating all possible prediction vectors exhaustively, the system implements a partial action approach by evaluating only a selected subset of prediction modes that are most likely to provide good compression results. This reduces the computational burden and device complexity while still achieving improved compression rates compared to simple prediction, by focusing resources on the most promising prediction strategies rather than exhaustively exploring all possibilities.
Data Source
AI summary
A selective predictor utilizes a selective prediction method for coordinate data encoding. The selective predictor may calculate multiple prediction vectors for each vertex in the shape traversal order. Then, the selective predictor selects the vector that is closest to the vertex to be estimated. In determining the multiple prediction vectors, the selective predictor considers the position of several previous vertices in the traversal order. The selective predictor outputs a correction vector for the selected prediction vector and identification information indicting the selected prediction vector.