Vector Control Point Reduction for Zero-Error Image Simplification
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Solution Overview
Problem
Vector graphics images become overly complex due to excessive control points and segments, leading to increased memory consumption, computational power requirements, bandwidth issues, and reduced user experience, especially when used in online content and printing.
Innovation Solution
The method involves lossless removal of redundant control points and segments based on chain-to-chain and segment-to-segment distance computations, using local Gauss-Newton optimization and a priority queue to ensure the smallest possible zero-error representation of the image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vector graphics images include intricate details and many control points to maintain image quality, then image quality is preserved, but memory consumption and file size increase
Solution Approach 1:
The patent extracts and removes redundant control points from vector graphics images while preserving the essential image quality. By identifying and eliminating unnecessary control points that do not contribute to the visual representation, the system reduces memory consumption and file size while maintaining the original image fidelity.
Solution Approach 2:
The patent changes the parameter of control point density by dynamically determining which control points to remove based on geometric analysis. The system adjusts the number and position of control points to achieve optimal balance between image quality and memory efficiency, transforming the original dense control point structure into a simplified version.
2Manufacturing precision
If vector graphics images include many control points and segments to represent complex shapes, then shape accuracy is improved, but computational power requirements increase
Solution Approach 1:
The patent extracts redundant control points that do not contribute to shape accuracy, reducing the computational burden. By removing unnecessary control points through geometric analysis and distance computations, the system maintains shape fidelity while significantly lowering the computational power required for rendering and processing.
Solution Approach 2:
The patent applies partial action by selectively removing only the redundant portion of control points rather than reducing all control points uniformly. This approach maintains sufficient control points to preserve shape accuracy while eliminating excess computational requirements associated with unnecessary control points.
3Measurement precision
If vector graphics images include excessive control points and segments, then detailed representation is achieved, but bandwidth requirements increase
Solution Approach 1:
The patent extracts and removes redundant control points from vector graphics images, reducing the amount of data that needs to be transmitted over the network. By eliminating unnecessary control points while preserving essential image details, the system decreases bandwidth requirements for transmitting vector graphics content.
4Manufacturing precision
If vector graphics images include many control points for precise rendering, then rendering accuracy is improved, but user experience deteriorates due to processing time
Solution Approach 1:
The patent extracts redundant control points that contribute to processing time without adding value to rendering accuracy. By removing these unnecessary control points through automated analysis, the system reduces processing time and improves user experience while maintaining the required rendering precision.
Data Source
AI summary
Various disclosed embodiments are directed to the lossless removal of redundant control points and/or segments based on chain-to-chain and/or segment-to-segment distance computations. Additionally or alternatively, such removal may be based on identifying all possible lossless removal operations to ensure that the smallest possible zero-error (or near zero-error) representation of a given image. Subsequent lossy operations may be computed via local Gauss-Newton optimization and processing a priority queue.