Rule-Based Video Importance Analysis Engine
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Consumers face challenges in editing and sharing casual videos recorded on smartphones due to the tedious process of sifting through unorganized and unranked video content, as existing thumbnail representations do not adequately convey the video's interesting moments.
Innovation Solution
A rule-based video analysis engine ranks video sections and files based on importance by evaluating subjective and objective qualities such as face recognition, exposure quality, and camera motion, generating importance scores to facilitate editing and sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If consumers manually review and edit video content, then they can select interesting moments, but the process becomes tedious and time-consuming
Solution Approach 1:
The video processing system automatically analyzes and ranks video sections without requiring manual consumer intervention. The system evaluates video content using multiple rules (face detection, motion analysis, exposure quality) and self-generates importance scores, eliminating the need for consumers to manually review each video moment while maintaining accurate content evaluation.
Solution Approach 2:
The patent replaces manual mechanical review processes with automated computational analysis. Instead of consumers watching and evaluating video content manually, the system uses computer vision algorithms, motion detection, and rule-based evaluation to automatically assess video sections, substituting human time investment with machine processing efficiency.
2Loss of information
If thumbnail images are provided for video representation, then users can preview content, but the thumbnails do not provide sufficient clues to video content
Solution Approach 1:
The system generates different thumbnail representations for different video sections based on their importance and content characteristics. Rather than using a single generic thumbnail, the system creates specialized thumbnails for important moments (detected through face recognition, motion analysis, and exposure evaluation), providing locally optimized visual clues that accurately represent the specific video content.
Solution Approach 2:
The patent employs visual enhancements in thumbnail generation, including color adjustments and highlighting techniques to make important video moments more visually distinguishable. By modifying thumbnail appearance based on detected important features (such as enhancing regions with detected faces or motion), the system provides richer visual information without requiring users to watch the entire video.
3Quantity of substance
If consumers capture more videos over time, then they have more content to share, but they have difficulty remembering video content
Solution Approach 1:
The system performs preliminary analysis of video content immediately upon capture, automatically evaluating and ranking video sections before the user needs to review them. By pre-computing importance scores and organizing video content based on detected features (faces, motion, exposure quality), the system preserves video content information in an accessible format, eliminating the need for users to remember video details later.
Solution Approach 2:
The patent introduces an intermediary automated analysis system between video capture and user review. This intermediary process continuously evaluates video content, generates importance rankings, and creates organized representations (thumbnails, metadata), serving as a mediator that preserves video information and presents it to users in an easily navigable format without requiring user memory.
4Productivity
If automated analysis is applied to video content, then video sections can be ranked by importance, but the analysis complexity increases
Solution Approach 1:
The patent divides video analysis into multiple independent rule-based modules (face detection rules, motion analysis rules, exposure quality rules). Each rule operates independently on different aspects of video content, allowing the system to process videos through parallel rule evaluations rather than a single complex algorithm. This segmentation improves processing efficiency while managing system complexity through modular design.
Data Source
AI summary
The importance of video sections of a video file may be determined from features of the video file. The video file may be decoded to obtain video frames and audio data associated with the video frames. Feature scores for each video frame may be obtained by analyzing features of the video frame or the audio data associated with the video frame based on a local rule, a global rule, or both. The feature scores are further combined to derive a frame importance score for the video frame. Based on the feature scores of the video frames in the video file, the video file may be segmented into video sections of different section importance values.