Electronic Ink Stroke Compression via Point Refinement
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Solution Overview
Problem
Existing electronic ink compression methods for mobile devices result in poor quality decompressed images due to the need for significant down-sampling, which strains CPU resources and conserves battery energy but degrades image quality.
Innovation Solution
A system and method that refines and modifies source points by iteratively removing and shifting points within a designated threshold, using upsampling processes to reduce data storage while maintaining image quality, involving a refinement module and a modification module to encode and decode electronic ink data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If down-sampling is applied to compress electronic ink data for mobile devices, then data storage requirements and CPU resource usage are reduced, but the quality of decompressed images deteriorates
Solution Approach 1:
The patent applies preliminary action by performing refinement and modification on the source trajectory points before compression. The refinement module removes redundant points and the modification module adjusts remaining points to better represent the original stroke, ensuring that even after aggressive down-sampling, the decompressed image maintains high quality. This preprocessing ensures that the reduced dataset contains only the most essential information needed for accurate reconstruction.
Solution Approach 2:
The patent changes parameters by transforming the raw source trajectory into a refined and modified representation with optimized coordinates. The refinement process changes the number of points (reducing them), while the modification process changes the positional parameters of remaining points to maximize their representational value. This parameter transformation allows achieving high compression ratios without sacrificing image quality.
2Use of energy by moving object
If down-sampling is applied to conserve battery energy on mobile devices, then energy consumption is reduced, but the representation accuracy of electronic ink strokes deteriorates
Solution Approach 1:
The system performs refinement and modification as preliminary actions before compression, ensuring that the reduced set of points contains maximized information density. By preprocessing the trajectory to eliminate redundancy and optimize point placement, the system achieves accurate stroke representation with fewer points, thereby reducing the computational energy required during decompression while maintaining fidelity.
Solution Approach 2:
The refinement and modification modules change the parameters of source trajectory points by removing redundant ones and adjusting coordinates of essential points. This parameter optimization ensures that each stored point contributes maximally to stroke accuracy, allowing the system to achieve high representation accuracy with reduced data, thus lowering energy consumption during storage and processing.
3Quantity of substance
If the number of source points is reduced to decrease data storage, then storage requirements are reduced, but the quality of recreated strokes deteriorates
Solution Approach 1:
The patent applies preliminary action by refining and modifying the source trajectory before reduction. The refinement module preemptively removes points that would be redundant even after compression, while the modification module adjusts remaining points to better capture stroke characteristics. This ensures that when points are reduced for storage, the remaining points are optimally positioned to preserve reconstruction quality.
Solution Approach 2:
The system changes parameters by transforming raw source points into refined and modified points with optimized coordinates. This parameter transformation ensures that fewer points can represent the stroke more accurately, as each modified point is positioned to maximize its contribution to reconstruction fidelity, thereby maintaining quality despite reduced point count.
Data Source
AI summary
A set of source points that represent a stroke input of a user is identified. The set of source points may be refined and/or modified. The set of refined/modified source points may then be stored in memory for decoding and recreation of a stroke representation. Additionally, one or both of refining and modifying the source points may be performed through one or more upsampling processes.