Automated Digital Composite Placement in Video Media
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Solution Overview
Problem
Existing digital media delivery systems, particularly over-the-top (OTT) platforms, face challenges in scaling personal media overlays and composites due to the need for manual artist intervention and individual customization, making it difficult to efficiently apply custom composites across widely distributed content.
Innovation Solution
An automated system utilizing an Automated Placement Opportunity Identification (APOI) engine and Placement Insertion Interface (PII) system, which employs neural networks for identifying placement opportunities and integrating creative graphics into digital media, allowing for programmatic compositing and previewing of overlays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual artist intervention is used to create personal composites, then customization quality is improved, but scalability and productivity deteriorate
Solution Approach 1:
The system uses templates that capture the essence of professionally designed composites and automatically copies/adapts them for individual consumers. The template system stores parameterized composite designs that can be rapidly instantiated and customized for different users without requiring manual artist intervention for each composite, thus maintaining quality while enabling scale.
Solution Approach 2:
The automated composite generation system allows the platform to serve itself by automatically creating and applying composites using consumer data and templates, eliminating the need for manual artist intervention. The system self-manages the composite creation process through automated data processing and template application.
2Adaptability or versatility
If individual customization is applied to each consumer, then consumer experience is improved, but system complexity and operational difficulty worsen
Solution Approach 1:
The system segments the composite creation process into distinct components: consumer data collection, data processing, template selection, parameter adjustment, and composite generation. This segmentation allows each component to be independently managed and optimized, reducing overall system complexity while maintaining personalization capabilities.
Solution Approach 2:
The system uses parameterized templates where customization is achieved by changing specific parameters (colors, positions, sizes, text content) rather than redesigning entire composites. This parameter-based approach simplifies the complexity by providing a standardized framework that adapts to individual consumers through controlled parameter variations.
3Measurement precision
If manual identification of composite opportunities is performed, then placement accuracy is improved, but time consumption and productivity worsen
Solution Approach 1:
The system replaces the mechanical process of manual visual inspection and identification with automated computer-based image processing and machine learning algorithms. These automated systems analyze video content to identify suitable placement opportunities, maintaining accuracy while dramatically reducing time consumption compared to manual methods.
Solution Approach 2:
The system performs preliminary analysis of video content to pre-identify suitable placement opportunities before composite generation begins. By pre-processing the video to detect objects, surfaces, and contextual elements that are suitable for composites, the system prepares placement candidates in advance, reducing real-time processing requirements and maintaining accuracy.
4Productivity
If automated compositing is implemented, then productivity and scalability are improved, but manufacturing precision and quality control worsen
Solution Approach 1:
By copying proven template designs and applying them systematically, the system ensures consistent quality across automatically generated composites. The templates encode best practices and design principles that are replicated across all composites, maintaining a baseline quality standard while enabling high-speed automated generation.
Solution Approach 2:
The system incorporates feedback mechanisms where composite performance data is collected and used to refine templates and adjustment algorithms. This feedback loop allows the automated system to learn from results and improve quality over time, compensating for the lack of manual quality control through data-driven optimization.
Data Source
AI summary
A system and method for inserting a composited image or otherwise generated graphic into a selected video by way of a programmatic process. According to some embodiments, a system may comprise an Automated Placement Opportunity Identification (APOI) engine, a Placement Insertion Interface (PII) engine, a preview system, and an automated compositing service. The system finalizes a graphic composite into a video and provides a user with a preview for final export or further manipulation.