Digital Video Edit Profile Automation via Metadata Analysis
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Solution Overview
Problem
The inefficiencies in managing and editing digital video libraries, particularly due to the resource-intensive nature of manual review and automated video processing technologies, which burden processors, memory, and battery life of computing devices when dealing with high-resolution video data.
Innovation Solution
A system that processes metadata from digital videos to provide suggested edits, including effects such as slow motion, zoom, and music tracks, which can be applied to specific moments in the video, allowing users to select and modify these edits through a graphical interface, thereby optimizing video editing processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of videos is performed to identify interesting scenes, then user preference accuracy is improved, but time consumption and resource usage increase
Solution Approach 1:
The system performs preliminary automated analysis of video content using metadata and machine learning models to pre-identify potentially interesting scenes before user review. This preliminary action filters out obviously uninteresting segments, reducing the time users need to spend manually reviewing videos while maintaining accurate identification of user-preferred content.
Solution Approach 2:
An intermediary automated recommendation system acts as a mediator between the video library and the user. This system processes video metadata, applies machine learning models to predict user interest, and presents curated recommendations, thereby reducing direct user exposure to the full video library and minimizing time consumption while preserving preference accuracy.
2Loss of time
If automated video processing technologies are used to identify interesting scenes, then time consumption is reduced, but resource intensity increases
Solution Approach 1:
The automated processing system segments video analysis into multiple stages: initial metadata-based filtering, followed by selective application of computationally intensive machine learning models only to segments that pass the initial filter. This segmentation reduces overall resource intensity by avoiding full-processing of all video content while maintaining time efficiency through automated multi-stage analysis.
Solution Approach 2:
The system applies partial automated processing by using lightweight metadata analysis for all videos and reserving resource-intensive machine learning model execution only for videos that show promise of user interest based on initial filtering. This partial action approach reduces overall resource consumption while still achieving time savings compared to full manual review.
3Manufacturing precision
If users manually edit videos with multiple effects through trial-and-error, then editing precision is improved, but time consumption and resource usage increase
Solution Approach 1:
The system performs preliminary automated effect recommendations by analyzing video content metadata and pre-suggesting appropriate effects and their parameters. This preliminary action provides users with curated editing options that require minimal adjustment, reducing the trial-and-error process while maintaining editing precision through user-approved recommendations.
Solution Approach 2:
The editing system provides self-service through automated effect recommendations that users can accept with minimal intervention. The system autonomously analyzes video characteristics, generates effect suggestions, and allows users to apply these recommendations with a single action, thereby achieving editing precision without time-consuming manual trial-and-error while reducing resource usage compared to extensive manual editing operations.
Data Source
AI summary
Implementations are directed to providing an edit profile including one or more suggested edits to a digital video, actions including receiving metadata associated with the digital video, the metadata including data representative of one or more of movement and an environment associated with recording of the digital video, processing the metadata to provide a suggested edit profile including at least one set of effects, the at least one set of effects including one or more effects configured to be applied to at least a portion of the digital video, providing a respective graphical representation of individual effect of the one or more effects within an effect interface, and receiving, through the effect interface, a user selection of a set of effects of the suggested edit profile, and in response, storing, in computer-readable memory, an edit profile comprising the set of effects for application to the digital video.