AI-Based Image Sequence Summarization for Electronic Devices
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Solution Overview
Problem
Current technologies lack an efficient method for automatically summarizing vast amounts of image data, such as those from digital broadcasts, to provide users with concise and relevant summaries without manual intervention.
Innovation Solution
An electronic device equipped with a processor and memory uses a trained artificial intelligence algorithm to divide original image data into sequences, select key frames based on pre-classified highlight image groups, and generate summary image data, ensuring important scenes are naturally connected and representative of the original content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual selection and summarization of image data is performed, then the accuracy and relevance of the summary can be ensured, but the time consumption and operational complexity increase significantly
Solution Approach 1:
The system enables automatic summarization by having the electronic device perform selection and compilation of image sequences autonomously using AI algorithms, eliminating the need for manual user operation while maintaining summary quality
Solution Approach 2:
Manual mechanical selection operations are replaced with an automated AI-based image sequence selection algorithm that analyzes and selects important image sequences automatically, substituting human operation with intelligent automation
2Loss of information
If all original image data is transmitted and displayed, then complete information is provided to users, but information overload occurs and user experience deteriorates
Solution Approach 1:
The system extracts only the most important and representative image sequences from the complete original image data, separating essential information from redundant content to provide a condensed summary that maintains key information while improving usability
Solution Approach 2:
The original image data is divided into multiple image sequences, and the AI algorithm selectively identifies and extracts important segments for the summary, organizing the content into manageable and meaningful portions
3Productivity
If a simple selection method is used for image sequences, then processing speed increases, but the quality and representativeness of the summary decreases
Solution Approach 1:
Simple mechanical selection methods are replaced with an AI-based intelligent selection algorithm that automatically analyzes image sequences and identifies important content, achieving both speed and quality through automation
Solution Approach 2:
The system changes the selection criterion from simple random or sequential selection to AI-based importance scoring, transforming the selection parameter to prioritize both efficiency and summary quality simultaneously
Data Source
AI summary
Provided are an electronic device and an operation method thereof. The electronic device includes a memory that stores one or more instructions, and a processor that executes the one or more instructions stored in the memory, wherein the processor is configured to execute the one or more instructions to: divide original image data into a plurality of image sequences; determine a predetermined number of image sequences among the plurality of image sequences as an input image group, select one of the image sequences included in the input image group and add the selected image sequence to the highlight image group based on one or more image sequences pre-classified as a highlight image group, by using a trained model trained using an artificial intelligence algorithm; and generate summary image data extracted from the original image data, by using the image sequence included in the highlight image group.