Digital Design Resizing Model Selection with Structural Graphs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional graphic design systems suffer from functional inaccuracy, operational inflexibility, and computational inefficiency, often generating modified digital design documents with overlapping or conflicting visual elements, lacking flexibility in adjusting to unique design contexts, and requiring significant user interactions and computing resources.
Innovation Solution
The digital design system constructs digital design graphs to generate structural representations, utilizing machine learning models to select resizing strategies based on feature representations, and employs a design critic to evaluate and iteratively improve the resizing process, ensuring accuracy and flexibility while reducing user interactions and computing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional resizing methods are used, then the resizing process is simple, but the accuracy and visual quality of the resized document deteriorates due to overlapping or conflicting visual elements
Solution Approach 1:
The system segments the resizing task into multiple candidate resizing models (e.g., different layout preservation strategies, scaling approaches), each handling specific aspects of the resizing problem. The machine learning model then selects the most appropriate segment (resizing model) based on the input document characteristics, improving accuracy without requiring a single complex resizing algorithm to handle all cases.
Solution Approach 2:
The system changes parameters by using a machine learning model to dynamically select resizing models based on learned patterns from training data. Instead of using fixed resizing parameters, the system adapts the selection of resizing models according to the specific characteristics of each document, thereby improving resizing accuracy while managing complexity through data-driven parameter adaptation.
2Manufacturing precision
If multiple resizing models are evaluated, then the quality of resized document improves, but the computing resources and time required increases
Solution Approach 1:
The system performs preliminary action by training a machine learning model in advance to learn the characteristics and performance of multiple resizing models. During actual resizing operations, the pre-trained model quickly selects the most suitable resizing model without needing to evaluate all candidates, thereby maintaining high resizing quality while improving processing efficiency through advance preparation.
Solution Approach 2:
The system implements feedback by using the machine learning model to learn from training data about which resizing models perform best under different conditions. This feedback mechanism allows the system to make informed selections of resizing models, achieving high-quality results without exhaustively evaluating all possible models, thus balancing quality and efficiency.
3Adaptability or versatility
If conventional design systems are used, then the operation process is straightforward, but the adaptability to unique design contexts deteriorates due to operational inflexibility
Solution Approach 1:
The system applies self-service by enabling the machine learning model to automatically select appropriate resizing models based on the unique characteristics of each design document. The system adapts to different design contexts autonomously without requiring user intervention or complex configuration, thereby improving adaptability while maintaining ease of operation through automated decision-making.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer-readable media that generates a design representation to further construct a digital design multigraph and generate a structural representation for a digital design document from the digital design multigraph. For instance, the disclosed systems generate a design representation of a digital design document that includes design properties with multiple digital design elements. In particular, the disclosed systems construct a digital design (multi-)graph from the design representation by generating nodes to represent digital design elements and edges based on relationships between these elements. In addition, the disclosed systems generate a structural representation based on the digital design multigraph for downstream applications. For instance, downstream applications include utilizing the structural representation to select a resizing model from a plurality of resizing models and resizing a digital design document using the structural representation.