Scene-Based Video Editing Recommendations via Visual Analysis
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Solution Overview
Problem
Conventional video editing systems require manual division of videos into scenes and application of separate edits, which is time-consuming and requires specialized skills, as edits that improve one scene may not necessarily enhance other scenes with different visual characteristics.
Innovation Solution
A video editing recommendation system automatically divides videos into scenes based on visual characteristics and uses machine learning to determine scene-specific editing settings, analyzing representative frames to recommend and apply optimal editing presets, thereby improving the visual quality of the output video without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual scene division and editing is performed, then editing precision can be controlled per scene, but the editing time and operational complexity increase significantly
Solution Approach 1:
The system automatically segments the video into multiple scenes based on visual characteristic changes, then applies scene-specific editing presets to each segment. This automated segmentation eliminates the need for manual scene division while maintaining the precision of per-scene editing control.
Solution Approach 2:
The system performs self-service by automatically analyzing video content, identifying scenes, selecting appropriate editing presets for each scene, and applying the edits without requiring manual user intervention for each step, thereby dramatically reducing editing time while maintaining precision.
2Manufacturing precision
If manual editing with trial and error is performed, then editing quality can be optimized, but the operational complexity and skill requirement increase
Solution Approach 1:
The system replaces the manual mechanical editing process with an automated machine learning-based system that analyzes video content and applies appropriate editing presets, eliminating the need for specialized editing skills and complex manual operations while maintaining high editing quality.
Solution Approach 2:
The system automatically adjusts editing parameters such as exposure, contrast, saturation, and other visual properties based on the specific characteristics of each scene, replacing manual parameter tuning with automated parameter optimization that achieves high quality without requiring user expertise.
3Ease of operation
If single editing settings are applied to entire video, then the editing process is simplified, but the visual quality consistency across different scenes deteriorates
Solution Approach 1:
The system applies the principle of local quality by selecting and applying different editing presets tailored to the specific visual characteristics of each individual scene, ensuring that each scene receives optimized editing treatment rather than a uniform approach, thereby maintaining visual quality consistency across diverse scenes.
Data Source
AI summary
Embodiments are disclosed for determining scene-based editing recommendations for video content. A method of determining scene-based editing recommendations for video content includes receiving an input video comprising video content, dividing the input video into the plurality of scenes based on the video content, identifying a representative frame for each scene, determining a plurality of editing settings for each representative frame, determining editing settings for each scene based on an effectiveness score, and generating an output video using the input video and the editing settings for each scene.