Virtualized Content Tagging via Scene Metric Analysis
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Solution Overview
Problem
The existing manual process of tagging multimedia content is limited, restricting searchability and the types of tags that can be applied, and the quality of source content often does not meet the demands of advanced destination devices, such as HD and beyond-HD capable devices.
Innovation Solution
A content distribution platform that uses context abstraction to transform source content into a virtualized computational space, allowing for high-fidelity rendering on any device by decoupling source content quality from destination device capabilities, and enabling augmentation and re-imagining of content based on rendering capabilities and user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual tagging process is used for multimedia content, then tagging can be performed, but the types of tags are limited and searchability is restricted
Solution Approach 1:
The system enables automatic tagging by allowing the multimedia content to tag itself through machine learning analysis. The content's visual, audio, and textual features are automatically processed to generate relevant tags without human intervention, thus improving versatility while reducing manual effort
Solution Approach 2:
The manual mechanical tagging process is replaced with an automated computational system using machine learning algorithms. This substitution enables the system to generate diverse tags automatically by analyzing content features, thereby increasing tag variety and searchability while eliminating manual labor
2Manufacturing precision
If source content quality is maintained as-is, then original content is preserved, but it does not meet the demands of advanced destination devices such as HD and beyond-HD capable devices
Solution Approach 1:
The content distribution system segments the content into multiple quality versions or representations suitable for different destination devices. By dividing the content into adaptable formats, the system can deliver appropriate quality levels to various devices without compromising the original source content
Solution Approach 2:
The system dynamically adapts content quality based on the capabilities of the destination device. Content is transformed and rendered in real-time to match the specific requirements of each device, enabling high-definition output on advanced devices while maintaining compatibility with lower-end devices
3Productivity
If traditional content distribution is used, then content can be delivered, but communication bandwidth is excessive and efficiency is low
Solution Approach 1:
Instead of transmitting the entire original content to each destination device, the system transmits only the essential content representation or metadata. The actual content is then reconstructed or rendered locally at the destination, significantly reducing communication bandwidth while maintaining distribution efficiency
Data Source
AI summary
Techniques for tagging a scene are disclosed. In some embodiments, information about a scene is inferred by at least in part automatically interpreting a metric associated with the scene that is determined by analyzing data comprising a primitive form of the scene, wherein analyzed data comprising the primitive form of the scene comprises positions of three-dimensional objects in the scene. The scene is tagged with a tag comprising the inferred information.