Video Editing Device Using Deep Learning Feature Analysis
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Solution Overview
Problem
Current video editing technologies require specialized knowledge and complex program understanding, making it difficult for ordinary people to edit videos effectively.
Innovation Solution
A video editing device that utilizes a deep learning network to analyze features of a reference video and apply them to a target video, enabling automatic or manual editing based on user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional video editing methods are used, then video editing can be performed with precise control over editing parameters, but the operation becomes complex and requires specialized knowledge
Solution Approach 1:
The system enables automatic video editing by having the editing device itself analyze the target video and automatically generate edited video based on analysis results, eliminating the need for users to manually control complex editing parameters. The editing device serves itself by performing both analysis and editing functions autonomously.
Solution Approach 2:
The patent replaces manual mechanical editing operations with deep learning-based automatic editing. The controller uses neural networks to automatically perform scene analysis, effect application, and video rendering, substituting the mechanical interaction between user and editing software with an intelligent automated system.
2Ease of operation
If automatic video editing using deep learning is implemented, then ease of operation is improved, but the device complexity increases due to the deep learning network requirements
Solution Approach 1:
The system segments the video editing function into two independent parts: a deep learning-based video analysis network and a video editing network. The analysis network processes the target video to extract features and generate analysis results, which are then used by the editing network to produce the final edited video. This segmentation allows the complex deep learning functionality to be isolated and managed separately from the user interface.
3Measurement precision
If deep learning network is used for video analysis, then the accuracy of feature extraction is improved, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary action by having the video analysis network analyze the target video and generate comprehensive analysis results before the actual editing process begins. This preliminary analysis includes extracting scene information, objects, and other features that will guide the subsequent editing operations, allowing the editing network to work efficiently with pre-processed information rather than performing analysis during editing.
Data Source
AI summary
The present disclosure provides a video editing device comprising: a communication unit which communicates with the outside; an input/output unit which receives a user input and outputs a video editing result; and a control unit, wherein the control unit: obtains a reference video and a target video; analyzes the features of the obtained reference video and the features of the obtained target video; edits the target video on the basis of the analyzed features of the reference video; and outputs the edited target video via the input/output unit.