Image Cropping Suggestion System Using Composition Quality Scoring
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Solution Overview
Problem
Conventional image cropping techniques are time-consuming and often result in visually unpleasing images, especially for users unfamiliar with photography rules or lacking a good 'eye' for composition, leading to suboptimal cropping outcomes.
Innovation Solution
An image cropping suggestion system that scores candidate croppings based on parameters such as composition quality, content preservation, and boundary simplicity, using machine-learning techniques to analyze visually pleasing images and suggest the most effective croppings to users through a user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual cropping is performed by users unfamiliar with photography rules, then the cropping process is simple and quick, but the visual quality and composition of the cropped image deteriorates
Solution Approach 1:
The system enables self-service by automatically performing the cropping operation based on pre-established visual characteristics and photography rules. The machine learning model analyzes the original image and autonomously determines the optimal cropping region, eliminating the need for user expertise while maintaining high composition quality.
Solution Approach 2:
The patent replaces the mechanical manual cropping process with an automated machine learning-based system. The machine learning model substitutes human judgment and manual selection with algorithmic analysis of visual characteristics, achieving both ease of operation and high composition quality simultaneously.
2Manufacturing precision
If multiple photos are manually cropped to improve selection, then the composition quality may improve, but the time required for processing increases
Solution Approach 1:
The system performs preliminary action by pre-calculating and pre-selecting the optimal cropping regions based on machine learning analysis of visual characteristics. Instead of requiring users to manually review multiple crops, the system has already identified and prepared the best cropping options, significantly reducing processing time while maintaining high composition quality.
Solution Approach 2:
The patent replaces time-consuming manual review and selection of multiple cropped photos with automated machine learning-based selection. The system efficiently identifies and presents the best cropping options algorithmically, eliminating the need for users to manually evaluate multiple candidates and substantially reducing the time required for the cropping process.
3Manufacturing precision
If automated cropping algorithms are used, then the composition quality improves, but the system complexity increases
Solution Approach 1:
The system uses copying by leveraging pre-trained machine learning models that have learned optimal cropping patterns from extensive training data. Instead of implementing complex real-time analysis algorithms, the system copies and applies pre-established visual characteristics and cropping rules, simplifying the actual execution while maintaining high composition quality.
Solution Approach 2:
The patent applies parameter changes by adjusting the output parameters of the machine learning model to match different photography styles and requirements. The system can modify cropping parameters such as aspect ratio, composition style, and visual characteristics to suit various needs, achieving high composition quality through parameter optimization rather than system complexity.
Data Source
AI summary
Image cropping suggestion is described. In one or more implementations, multiple croppings of a scene are scored based on parameters that indicate visual characteristics established for visually pleasing croppings. The parameters may include a parameter that indicates composition quality of a candidate cropping, for example. The parameters may also include a parameter that indicates whether content appearing in the scene is preserved and a parameter that indicates simplicity of a boundary of a candidate cropping. Based on the scores, image croppings may be chosen, e.g., to present the chosen image croppings to a user for selection. To choose the croppings, they may be ranked according to the score and chosen such that consecutively ranked croppings are not chosen. Alternately or in addition, image croppings may be chosen that are visually different according to scores which indicate those croppings have different visual characteristics.