Content Streaming Scene Analysis Using Preprocessed Scene Libraries
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Solution Overview
Problem
Existing content streaming systems lack real-time scene analysis and efficient methods for ranking and storing user-desired scenes based on user preferences and interactions.
Innovation Solution
A method and apparatus for analyzing scenes in a content streaming system, involving real-time scene analysis using image processing and AI models, determining scene change points based on frame similarity, and storing scenes in a library, with features for ranking and sharing based on user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time scene analysis is performed using image processing and AI models, then scene analysis accuracy and user preference identification are improved, but processing time and computational resources are increased
Solution Approach 1:
The system performs preliminary scene analysis and stores analyzed scenes in a scene library before user requests are made. When a user requests scene analysis, the system can retrieve pre-analyzed scenes from the library, reducing real-time processing requirements and improving response speed while maintaining analysis accuracy.
Solution Approach 2:
The system creates a scene library that stores analyzed scene data as copies or representations of the actual content. This allows the system to work with pre-processed scene information rather than analyzing raw content in real-time, reducing computational burden while preserving analysis precision.
2Adaptability or versatility
If scenes are stored in a scene library for later retrieval and ranking, then user preference-based content delivery is improved, but storage requirements and system complexity are increased
Solution Approach 1:
The scene library serves multiple functions: storing analyzed scenes, enabling user preference identification, supporting scene ranking, and facilitating content delivery. By consolidating these functions into a single multi-functional system component, the patent reduces overall system complexity while improving adaptability to user preferences.
Solution Approach 2:
The system uses user interactions with stored scenes to generate feedback signals that refine future scene selection and ranking. This feedback mechanism allows the system to adapt to user preferences dynamically while using the same standardized scene library structure, avoiding the need for multiple specialized systems.
3Measurement precision
If scene ranking is performed based on user interactions and preferences, then content delivery relevance is improved, but computational processing and time consumption are increased
Solution Approach 1:
The system performs preliminary scene analysis and stores scenes in the library before ranking operations are needed. When ranking is required, the system works with pre-analyzed scene data from the library rather than performing comprehensive analysis and ranking in real-time, improving processing efficiency while maintaining relevance accuracy.
Solution Approach 2:
The system performs scene analysis to a sufficient degree to enable effective ranking and content delivery, rather than attempting exhaustive analysis. By applying partial action at the appropriate stage, the system achieves adequate ranking precision without the computational cost of complete analysis, improving overall productivity.
Data Source
AI summary
Disclosed herein are a method for analyzing a scene in a content streaming system and an apparatus thereof, and the method for analyzing a scene in a content streaming system may include receiving a request for generating a bookmark scene, analyzing a target scene in the content image by image processing based on the request to generate the bookmark scene, and storing the generated bookmark scene in a scene library.