Video Frame Selection via Scene Analysis and Recommendation
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Solution Overview
Problem
Current methods for extracting still images from video files are inefficient, requiring users to manually sift through numerous frames, which is time-consuming and labor-intensive, especially when capturing high-resolution videos at 30 frames per second.
Innovation Solution
An electronic device equipped with a scene analysis engine (SAE) and a scene recommendation engine (SRE) analyzes video frames, assigns score data based on characteristics like brightness, motion, and object detection, and selectively recommends frames for extraction as still images, allowing users to quickly identify and capture meaningful frames during video playback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video capturing is performed at high frame rate (30 FPS), then the ability to capture fine-scale momentary changes is improved, but the time required to manually select and extract meaningful still images increases significantly
Solution Approach 1:
The system performs self-service by automatically analyzing video frames and selecting meaningful still images without requiring manual user intervention. The scene analysis engine autonomously evaluates frames based on multiple criteria (motion detection, object recognition, scene composition) and generates a ranked list of candidate frames, allowing the system to serve its own frame selection needs rather than relying on external manual input.
Solution Approach 2:
The system applies preliminary action by pre-analyzing video frames during or immediately after video capture to identify and rank potential still image candidates before the user needs to select them. By performing scene analysis, motion detection, and frame scoring in advance, the system prepares a curated list of meaningful frames, eliminating the need for users to manually review all 1800 frames from a one-minute video.
2Loss of information
If all video frames are reviewed manually to select still images, then the completeness of frame selection is improved, but the labor intensity and time consumption increase
Solution Approach 1:
The system segments the frame selection task by dividing video frames into categories based on their characteristics. The scene analysis engine evaluates frames using multiple independent criteria (motion magnitude, object presence, scene composition, exposure quality) and segments frames into ranked groups. This segmentation allows the system to identify meaningful frames without requiring complete manual review of every single frame, maintaining selection quality while reducing operational burden.
Solution Approach 2:
The system implements feedback by continuously evaluating frame quality metrics and using this information to refine frame selection. The scene analysis engine provides feedback signals about frame characteristics (motion detection results, object recognition outcomes, exposure assessments) that guide the automatic selection process. This feedback mechanism ensures that meaningful frames are identified accurately without requiring exhaustive manual inspection.
3Productivity
If automatic frame selection algorithms are implemented, then the time required for still image extraction is reduced, but the accuracy of selecting meaningful frames may deteriorate
Solution Approach 1:
The system merges multiple analysis functions into a unified scene analysis engine that simultaneously performs motion detection, object recognition, scene composition evaluation, and exposure assessment. By combining these diverse analytical capabilities into a single integrated system, the patent achieves both high processing speed and high frame selection accuracy. The merged engine evaluates frames based on multiple criteria and generates a comprehensive quality score, ensuring that automatic selection maintains precision while improving productivity.
Solution Approach 2:
The system applies parameter changes by adjusting the weighting and thresholds of various frame evaluation criteria based on the specific video content and user needs. The scene analysis engine can dynamically modify parameters such as motion detection sensitivity, object importance weights, and exposure quality thresholds to optimize frame selection for different scenarios. This parameter adaptability allows the automatic selection algorithm to maintain high accuracy across diverse video types while preserving fast processing speeds.
Data Source
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AI summary
A method and an electronic device are provided for generating a still image from a video file. The electronic device includes an image sensor, a display, and a controller configured to generate a video file from an image signal input through the image sensor, assign frame characteristics to each frame included in the video file, selectively display at least one of the frames on the display, receive a selection of a frame among the displayed at least one of the frames, and generate a still image from the selected frame.