Raster to Vector Conversion via Triangular Merging
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Solution Overview
Problem
Existing raster to vector conversion methods do not guarantee satisfactory performance in terms of image quality and memory efficiency, particularly when resizing digital images.
Innovation Solution
A method that divides a digital image into polygons by generating base triangles and applies iterative merging based on similarity criteria, using data-dependent triangulation and adaptive similarity tests to optimize the conversion process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If triangulation techniques are used for raster to vector conversion, then the image can be divided into manageable regions, but the image quality and memory efficiency are not satisfactory
Solution Approach 1:
The image is divided into triangular regions through triangulation, creating a structured framework for subsequent processing. This segmentation allows the conversion process to work with manageable regions while maintaining overall image integrity.
Solution Approach 2:
Adjacent triangles that satisfy similarity criteria are merged into polygons through iterative operations. This merging process reduces the total number of regions while preserving image quality by combining only similar regions, thereby resolving the contradiction between structural manageability and image precision.
2Productivity
If existing raster to vector conversion methods are used, then conversion can be performed, but memory usage is high and image quality is compromised
Solution Approach 1:
The iterative merging of similar triangular regions into larger polygons significantly reduces the total number of regions required to represent the image. This consolidation decreases memory usage while maintaining conversion capability, as the merged polygons preserve essential image features through similarity-based selection criteria.
Solution Approach 2:
The similarity criteria used for merging triangles can be adjusted through parameters that control the trade-off between region count and image fidelity. By optimizing these parameters, the conversion process achieves efficient memory usage without sacrificing essential image quality, resolving the contradiction between productivity and resource consumption.
3Loss of information
If pixel matrix representation is used, then complete image information is preserved, but storage requires large memory and enlargement causes quality loss
Solution Approach 1:
The method extracts only the essential information needed to represent image regions, storing contour definitions and representative parameters rather than complete pixel data for every region. This extraction approach maintains sufficient image information for quality representation while dramatically reducing memory requirements compared to full pixel matrix storage.
Solution Approach 2:
The representation transitions from storing complete pixel matrices to storing condensed regional parameters including contour definitions and representative pixel values. This parameter transformation preserves essential image information while achieving significant memory efficiency, resolving the contradiction between information completeness and storage requirements.
4Quantity of substance
If vector representation is used, then memory storage is reduced and resizing is more efficient, but image quality may be approximated
Solution Approach 1:
By merging only adjacent triangles that satisfy similarity criteria, the method creates vector polygons that accurately represent uniform image regions. This selective merging maintains image quality by preserving boundaries between dissimilar regions while achieving memory efficiency through vector representation, resolving the contradiction between storage reduction and quality preservation.
Solution Approach 2:
The conversion transforms pixel-based representation into vector-based representation with adjustable precision parameters. The similarity criteria and merging thresholds can be tuned to control the approximation level, allowing optimization between memory efficiency and image quality based on specific application requirements.
Data Source
AI summary
A raster to vector conversion method of an initial digital image including a pixel matrix, includes generating a digital image divided into polygons by dividing the initial digital image into a plurality of base triangles and defining similarity criteria depending on at least one parameter. The conversion method also includes an iterative operation to process the digital image divided into polygons, selecting pairs of polygons adjacent to each other and to satisfy the similarity criteria and merging together the selected polygons.