Chapter Detection in Multimedia Streams via Multi-Airing Alignment
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Solution Overview
Problem
Existing methods for detecting chapter boundaries in multimedia streams are not robust across all types of programming, are inefficient, and require manual updates or human intervention, limiting their applicability to live video streams and interactive navigation.
Innovation Solution
A method that aligns multimedia streams between airings to identify consistent primary programming and varying commercials, using text, image, and audio data comparisons to automatically detect chapter boundaries without manual intervention, employing finite-state automata to differentiate between chapters and commercials.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If video streams are processed linearly without chapter detection, then the consumption is simple, but the ability to navigate and discover video content is limited
Solution Approach 1:
The patent segments video streams into chapters by detecting chapter boundaries through alignment of multiple airings. This segmentation enables both simple linear consumption (by maintaining the overall stream structure) and interactive navigation (by providing accessible chapter markers for jumping to specific segments), thus resolving the contradiction between simplicity and navigability.
2Measurement precision
If existing chapter detection methods are used, then they work for specific programming types, but they fail to accurately detect chapters across all types of programming
Solution Approach 1:
The patent creates a universal chapter detection system that works across all programming genres by aligning multiple airings of the same content. Instead of creating genre-specific detection algorithms, the system uses the redundancy of repeated airings to identify chapter boundaries through temporal alignment, making it universally applicable to news, dramas, reality shows, and other genres without requiring genre-specific customization.
Solution Approach 2:
The patent uses copies of the same video content (multiple airings) to detect chapter boundaries. By comparing and aligning these copies, the system can identify consistent temporal patterns that mark chapter transitions, leveraging the copied content to achieve accurate detection across diverse programming types without needing genre-specific training data or rules.
3Measurement precision
If manual updates and human intervention are used for chapter detection, then the detection can be accurate for specific cases, but the process is labor intensive and brittle
Solution Approach 1:
The patent implements a self-service chapter detection system that automatically processes multiple video airings to identify chapter boundaries without human intervention. The system aligns the airings temporally, detects consistent patterns, and generates chapter markers autonomously, eliminating the need for manual updating while maintaining high accuracy across different programming types.
Solution Approach 2:
The patent performs preliminary alignment of multiple video airings before chapter detection. By pre-processing the airings to establish temporal correspondence, the system prepares the data in advance, making the subsequent chapter boundary detection automatic and accurate without requiring manual intervention during the actual detection process.
4Reliability
If complex analysis methods are used for chapter detection, then the detection can be robust, but the computational complexity increases
Solution Approach 1:
The patent uses simple temporal alignment of copied video streams (multiple airings) to detect chapter boundaries. Instead of employing complex video or audio analysis algorithms, the system leverages the redundancy of repeated airings and compares their temporal structures, achieving robust detection with computationally efficient operations that focus on alignment and pattern recognition rather than complex content analysis.
Data Source
AI summary
There is described a multimedia processing method comprising: identifying a start and end time ranges in an input multimedia stream; comparing said ranges to an alternate multimedia stream of the same program for regions of similarity; and defining time ranges as representing chapter regions responsive to the content of both streams within the time ranges having high similarities.