Intelligent Video Editing System for Frame Selection and Scene Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current video editing software is inefficient for users who need to edit large numbers of videos, as it requires manual frame-by-frame adjustment, leading to a heavy workload.
Innovation Solution
An intelligent video editing method that performs image extraction at predetermined intervals, compares consecutive frames to identify target frames within a scene, and uses neural networks for classification and scoring, with a user interface for selecting and reporting errors, and modules for tracking preferences and filtering advertisements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual frame-by-frame adjustment is used in general video editing software, then users can precisely control video editing, but the workload becomes quite heavy for users who need to edit large numbers of videos
Solution Approach 1:
The system automatically performs frame extraction, scene detection, and frame selection without requiring manual user intervention. The neural networks autonomously evaluate frames and make selections based on learned patterns, allowing the system to serve itself in the editing process while maintaining high precision in frame selection.
Solution Approach 2:
The patent replaces the mechanical manual operation of pressing arrow keys to navigate frames with an automated intelligent system. Neural networks substitute for human operators in performing frame evaluation, scene detection, and selection decisions, transforming a manual mechanical process into an automated intelligent process that handles large volumes of video efficiently.
2Adaptability or versatility
If image extraction operations are performed on the entire image sequence, then all frames are available for editing, but the amount of data to be processed is large
Solution Approach 1:
The system extracts only the essential frames that represent different scenes rather than processing the entire image sequence. By taking out key frames through intelligent scene detection and evaluation, the system reduces the data volume significantly while preserving the essential content needed for video editing.
Solution Approach 2:
Different parts of the video are treated differently based on their content characteristics. The system applies scene detection and frame evaluation selectively to identify and extract frames with higher importance, giving different quality levels of processing to different portions of the video based on their contribution to the overall content.
3Measurement precision
If scene detection is performed by comparing all consecutive frames, then accurate scene boundaries are identified, but the processing time increases significantly
Solution Approach 1:
The system performs preliminary frame extraction at predetermined intervals before conducting detailed scene detection. By pre-selecting candidate frames that are likely to represent scene changes, the system reduces the number of comparisons needed while maintaining accurate scene boundary detection. This preliminary action filters out frames that are unlikely to be scene boundaries.
Solution Approach 2:
Instead of comparing all consecutive frames, the system performs scene detection on a subset of extracted frames at predetermined intervals. This partial action approach processes fewer frames while still achieving accurate scene detection by strategically selecting which frames to evaluate for scene boundaries.
Data Source
AI summary
An intelligent video editing method including: receiving and decoding an image data stream from a source storage device to generate an image sequence; performing image extraction operations on the image sequence at intervals of a predetermined time to obtain a plurality of image shots; selecting a frame out of each aforementioned image shot as a candidate frame, and performing a comparison operation on any two consecutive aforementioned candidate frames to derive a group of aforementioned candidate frames belonging to a same scene, and selecting one aforementioned candidate frame from the group as a target frame; and performing an AI evaluation operation on each aforementioned target frame to classify and/or rate each aforementioned target frame.