DEM Compression Using Terrain Prediction Error Encoding
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Solution Overview
Problem
Existing compression techniques for digital elevation model (DEM) data sets fail to account for the specific characteristics of the terrain, leading to inefficient storage, transmission, and processing, especially in scenarios with limited network bandwidth and storage capacity.
Innovation Solution
A system and method that predicts pixel values based on adjacent values in the DEM data set, records prediction errors, and encodes these errors into colors to compress the data, taking into account the inherent features of the terrain, such as slopes and elevation changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional image compression algorithms are used on DEM data, then compression can be achieved, but the algorithms are computationally intense and require mass memory storage
Solution Approach 1:
The patent changes the fundamental parameter being compressed from raw elevation values to prediction errors. By transforming the data representation from absolute values to differential values relative to predictions, the compression achieves better efficiency with reduced computational overhead, as the prediction error values have smaller ranges and higher redundancy.
Solution Approach 2:
The patent performs preliminary prediction of pixel values using terrain characteristics (slopes, aspects, curvature) before compression. By pre-calculating expected values based on geological and topographical patterns, the system prepares the data in a form that requires less processing during actual compression, reducing real-time computational intensity.
2Quantity of substance
If conventional image compression algorithms are used on DEM data, then compression can be achieved, but mass memory storage is required
Solution Approach 1:
The patent extracts and utilizes specific terrain characteristics (slopes, aspects, curvature, hydrology) as separate predictive factors. By separating the prediction process into distinct geological and topographical components, the system can compress each aspect independently, reducing the overall memory footprint compared to treating all pixel relationships uniformly.
Solution Approach 2:
The patent applies different prediction strategies tailored to local terrain characteristics. Different algorithms or parameters are used for different terrain types (mountains, plains, valleys, water bodies), allowing optimal compression for each local region while minimizing overall memory requirements through localized optimization rather than uniform processing.
3Adaptability or versatility
If conventional compression methods are used, then general compression is achieved, but they cannot provide tailored compression for specific types of DEM data set
Solution Approach 1:
The patent implements dynamic adaptation of compression parameters based on detected terrain characteristics. The system automatically adjusts prediction models and compression settings according to the specific terrain type being processed (mountainous, flat, coastal, etc.), providing tailored compression without requiring manual configuration or complex rule-based systems.
Solution Approach 2:
The compression system performs self-analysis of terrain characteristics and automatically selects optimal compression parameters. By embedding terrain detection and classification capabilities within the compression pipeline, the system serves itself by identifying and adapting to the specific properties of each DEM dataset without external intervention, achieving versatility through self-directed optimization.
Data Source
AI summary
The present disclosure provides methods and systems for compressing map data. Provided here is a system for reducing network traffic by compressing map data, comprising: a data set receiver configured to obtain first two or more values associated with first two or more pixels; a prediction difference recorder configured to: (a) predict a next value associated with a next pixel based on the first two or more values, wherein the next pixel is immediately adjacent to one of the first two or more pixels; (b) predict a further next value associated with a further next pixel based on previous two or more values associated with previous two or more pixels, wherein the further next pixel is immediately adjacent to one of the previous two or more pixels; and (c) record an error in the predicted next value and in the predicted further next value; a render engine configured to encode the recorded error into colors.