Automated Raster Image Vectorization Using Gradient Meshes
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Solution Overview
Problem
Existing vectorization techniques fail to efficiently convert raster images into scalable vector formats that maintain quality and are easily editable, often requiring manual intervention or resulting in dense vector primitives that are difficult to edit.
Innovation Solution
The method involves identifying edges in a raster image, generating Bezier curves, creating a curvilinear triangulation, and constructing a gradient mesh with control points to produce a vector representation that preserves natural shapes and colors, allowing for intuitive editing and scalable rendering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Length of stationary object
If raster image is enlarged, then image size increases, but visual quality deteriorates due to interpolation artifacts
Solution Approach 1:
The patent creates a vector copy of the raster image by extracting edges and reconstructing the image using mathematical curves (Bezier curves) and gradient meshes. This vector representation can be scaled indefinitely without quality loss, resolving the contradiction between image size enlargement and visual quality preservation.
2Ease of operation
If manual vectorization is performed, then editing ease improves, but time consumption increases
Solution Approach 1:
The system performs automated vectorization by itself without requiring manual intervention. The algorithm automatically detects edges, fits Bezier curves, determines gradient meshes, and generates editable vector primitives, achieving both time efficiency and editing ease simultaneously.
3Manufacturing precision
If dense vector primitives are generated, then image detail is preserved, but editability deteriorates
Solution Approach 1:
The patent segments the vector representation into meaningful groups: edge curves defining boundaries, gradient meshes defining color regions, and control points defining geometry. This segmentation allows editors to modify specific aspects (edges, colors, shapes) independently while preserving overall image detail.
Data Source
AI summary
A method includes accessing a digital image in a raster representation and identifying edges in the digital image. A set of curves are generated by fitting each of the edges with a corresponding curve. A curvilinear triangulation is generated to include the set of curves. The method further includes generating a gradient mesh based on the curvilinear triangulation, where the gradient mesh includes polygons of the curvilinear triangulation along with control points based on the curvilinear triangulation. A respective color is determined for each control point of the gradient mesh, based on pixel colors of pixels of the raster representation corresponding to the control points in the gradient mesh. The method further includes generating a vector representation of the digital image, where the vector representation includes the gradient mesh with colors of the control points.