Electronic Device Image Parameter Prioritization
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Solution Overview
Problem
Existing portable electronic device cameras struggle to provide images that reflect users' personal preferences, as auto-mode may deteriorate image quality, expert mode is cumbersome, and AI mode may not accurately apply optimal settings.
Innovation Solution
An electronic device and server system that identifies and learns user-preferred image capturing settings by generating and prioritizing multiple parameter sets, providing previews of corrected images based on these settings, and adjusting parameters to match user preferences using big data-based deep learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If auto-mode is used to simplify operation, then ease of operation is improved, but image quality deteriorates
Solution Approach 1:
The system performs preliminary learning of user preferences by analyzing previously captured images and their corresponding parameter settings. This accumulated knowledge is then used to automatically determine optimal parameters for new captures, combining the convenience of auto-mode with the quality of expert settings.
Solution Approach 2:
The system incorporates feedback loops where user selections of preferred images and parameters are analyzed to continuously refine the preference model. This feedback mechanism enables the auto-mode to adapt to individual user preferences over time, improving image quality while maintaining ease of operation.
2Manufacturing precision
If expert mode is used to improve image quality, then manufacturing precision is improved, but ease of operation deteriorates
Solution Approach 1:
The system performs self-service by automatically determining optimal capture parameters based on learned user preferences and current capturing conditions. This eliminates the need for users to manually adjust settings while maintaining high image quality consistent with expert-mode results.
Solution Approach 2:
The system dynamically changes parameter settings based on learned user preferences and analyzing current capturing conditions (lighting, subject type, environment). This allows the system to adapt parameters automatically, providing expert-quality images without requiring users to manually adjust settings.
3Ease of operation
If AI camera mode is used to automatically recommend settings, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The system performs preliminary learning by analyzing previously captured images and user selections to build a personalized preference model. This accumulated knowledge enables more accurate parameter recommendations for new captures, improving measurement precision while maintaining automated operation.
Solution Approach 2:
The system uses feedback from user selections and preferences to continuously refine the AI recommendation algorithm. This feedback loop enables the system to learn from actual user behavior and improve the accuracy of parameter settings, resolving the contradiction between automated operation and setting precision.
4Adaptability or versatility
If multiple parameter sets are generated and prioritized, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system segments the parameter optimization process into distinct stages: preference learning from historical data, real-time condition analysis, and parameter generation. This segmentation allows the system to handle multiple parameter sets systematically, improving adaptability while managing computational complexity through structured processing.
Data Source
AI summary
An electronic device is provided. The electronic device includes a camera, a display, a memory, a communication module, and a processor configured to identify a plurality of parameter sets related to image capturing from an external device using the communication module, provide, in a first preview, at least part of one or more images using the display during at least part of obtaining the one or more images using the camera, generate one or more first corrected images to which a first parameter set among the plurality of parameter sets is applied, using the one or more images, generate one or more second corrected images to which a second parameter set among the plurality of parameter sets is applied, using the one or more images, identify priority for the plurality of parameter sets, and provide, in a second preview, one or more among the one or more first corrected images and the one or more second corrected images according to the priority during at least part of providing the first preview.