AI Media Editing with Edge Computing and Script Metadata
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Solution Overview
Problem
The production of media content involves multiple steps and human aspects, such as emotion and gesture, which are difficult to evaluate until presented, and technical aspects like camera focus and framing are not fully assessed until media assets are processed, leading to inefficiencies in editing and review processes.
Innovation Solution
A system utilizing multi-access edge computing (MEC) and Artificial Intelligence/Machine Learning (AI/ML) for prioritized editing of media assets from cameras, where metadata and script annotations are used to process and edit a media composition as a first cut, allowing for real-time review and feedback without altering set conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If media assets are processed and evaluated after production, then comprehensive assessment of emotional and technical quality is possible, but production time and efficiency are reduced
Solution Approach 1:
The system performs preliminary processing of media assets during or immediately after capture, extracting metadata and generating initial quality assessments before the full production workflow begins. This allows directors and script supervisors to evaluate takes in real-time without waiting for post-production processing
Solution Approach 2:
The patent replaces manual human evaluation of media assets with an automated machine learning system that analyzes visual and audio parameters. This substitution enables rapid, consistent quality assessment without the time constraints of human review while maintaining objective measurement of technical aspects like focus, framing, and exposure
2Manufacturing precision
If multiple media assets are reviewed and evaluated manually, then comprehensive selection is possible, but the editing process becomes complex and time-consuming
Solution Approach 1:
The system enables script supervisors and directors to self-service the evaluation process by providing them with automated quality metrics and rankings directly at the production location. The machine learning system serves itself by continuously processing incoming media assets and updating quality assessments without requiring post-production intervention
Solution Approach 2:
The patent introduces an intermediary automated evaluation system between the camera capture process and the final editing decision. This intermediary layer processes media assets, extracts relevant quality parameters, and presents simplified recommendations to directors, reducing the complexity of manual review while improving selection consistency
3Productivity
If media assets are processed in a cloud environment with AI/ML, then real-time feedback and prioritized editing is enabled, but system complexity and computational requirements increase
Solution Approach 1:
The patent moves the processing dimension from traditional post-production facilities to a cloud-based AI/ML environment accessible during production. This dimensional shift allows media assets to be processed remotely with advanced computational models while providing real-time feedback to the production team through network connections
Solution Approach 2:
The system employs a universal machine learning model that can evaluate multiple types of media assets (video, audio, metadata) simultaneously and provide comprehensive quality assessments. This multi-functional approach consolidates various evaluation tasks into a single integrated system rather than requiring separate specialized tools
Data Source
AI summary
Systems and methods for editing a media composition from media assets are provided. An editing device receives a media asset associated with a scene to be rendered in a media composition. The editing device receives a script including script elements that index script sections associated with the scene and metadata. The editing device edits the media composition with segments of the media asset based on a comparison of the segments, the script elements, and the metadata.