Electronic Device Neural Network Image Merging
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Solution Overview
Problem
Users face inconvenience and effort in merging images from multiple devices to create high-quality content, as existing methods lack automation and often result in suboptimal aesthetic quality.
Innovation Solution
An electronic device equipped with a camera, communication unit, and processor that uses neural network models to automatically extract and merge sections with good compositions from multiple images by synchronizing video recording and analyzing image scores, allowing for efficient and high-quality image merging upon user command.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a user directly identifies desired sections from a plurality of images and merges them, then the user can control the merging process, but it requires a lot of effort and time for the user to master the application and the aesthetic quality is not guaranteed
Solution Approach 1:
The system performs automatic image merging without requiring user intervention. The processor automatically identifies sections with good compositions from multiple images using neural network models and merges them according to predetermined algorithms, allowing the system to serve itself rather than requiring user mastery of complex operations
Solution Approach 2:
The manual mechanical process of user identification and merging is replaced by an automated neural network-based system. The neural network model automatically evaluates composition quality and the processor automatically performs merging operations, substituting human manual operations with automated intelligent systems
2Manufacturing precision
If a user directly merges images, then the user has control over the process, but the aesthetic quality of the merged image is not guaranteed
Solution Approach 1:
The system uses neural network models to evaluate the composition quality of images and sections automatically. The processor receives feedback from the neural network regarding which sections have good compositions and uses this feedback to automatically select and merge appropriate sections, ensuring high aesthetic quality without requiring user judgment
Solution Approach 2:
The system changes the evaluation parameter from subjective user judgment to objective neural network-based composition scoring. By using predetermined evaluation criteria and neural network models to score sections, the system automatically identifies and merges sections with the highest composition quality, guaranteeing aesthetic standards
3Quantity of substance
If the system processes and merges all images from multiple devices, then complete data is available for merging, but data transmission and storage requirements increase
Solution Approach 1:
The system extracts only the necessary portions of images for merging. Instead of processing and transmitting complete images, the processor identifies and extracts only the sections with good compositions using neural network models, transmitting and merging only these extracted sections rather than entire images
Solution Approach 2:
The system segments images into multiple sections and evaluates each section independently using neural network models. By dividing images into sections and selectively processing only those with good compositions, the system reduces the total amount of data that needs to be transmitted and stored while maintaining merging quality
Data Source
AI summary
An electronic device and a control method thereof are provided. The control method of an electronic device includes the steps of when a user command for initiating video recording is received, transmitting, to an external device connected to the electronic device, a first signal including a request for initiating video recording, acquiring a first image according to the user command, acquiring a first score for composition of the first image by using a trained neural network model, when a second signal including a second score for composition of a second image acquired by the external device is received according to the first signal, identifying, from the second image, at least one merging section to be merged into the first image, on the basis of the first score and the second score, transmitting, to the external device, a third signal including a request for image data corresponding to the at least one merging section, and when a fourth signal including the image data is received according to the third signal, acquiring a third image by merging an image corresponding to the at least one merging section into the first image on the basis of the image data.