Scene Boundary Matching for Modified Video Content Validation
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Solution Overview
Problem
Conventional content validation processes fail to accurately match modified content, leading to a high number of false positives due to intentional alterations such as added graphics or video transformations.
Innovation Solution
A scene-based matching approach that iteratively modifies and compares scene boundaries, combining multiple scenes to improve matching accuracy, using a recursive soft-matching process to handle alterations and transformations in generated video.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional content validation processes are used, then the validation process is simple, but matching accuracy deteriorates due to intentional modifications
Solution Approach 1:
The patent segments video content into discrete scenes and analyzes scene boundaries as distinct units. By dividing the video stream into manageable scene segments and comparing scene boundary characteristics, the system achieves accurate matching even when content is modified, while maintaining a structured validation process that doesn't require analyzing every frame.
Solution Approach 2:
The patent transitions from frame-level validation to scene-level validation, adding a temporal dimension to the analysis. By examining scene boundaries and their characteristics across time rather than individual frames, the system achieves more robust matching that is invariant to modifications while maintaining process efficiency.
2Reliability
If scene boundaries are strictly compared, then validation is straightforward, but false positives increase due to transformations in generated video
Solution Approach 1:
The patent applies different validation criteria to different parts of the scene boundary structure. Rather than requiring exact matching of all boundary characteristics, the system identifies and compares key local features of scene boundaries that are invariant to modifications, allowing tolerance for transformations while maintaining overall validation accuracy.
Solution Approach 2:
The patent changes the parameters used for scene boundary comparison from strict temporal alignment to characteristic-based matching. By analyzing scene boundary characteristics such as visual content changes and temporal patterns rather than exact timing, the system achieves reliable validation that is resistant to false positives from video transformations.
3Measurement precision
If multiple scenes are merged to improve matching, then matching accuracy improves, but processing complexity increases
Solution Approach 1:
The patent merges adjacent scenes into combined scene units when their boundaries are close in time, creating larger temporal windows for comparison. This merging strategy improves matching accuracy by accommodating slight timing variations and transformations, while the automated merging logic keeps processing complexity manageable through systematic scene combination rules.
Data Source
AI summary
Methods and systems are described for managing content. A content stream may be generated based on source content. Scenes identified in the content stream may be compared with scenes in the source content. An iterative matching process may be used to modify scene boundaries that may be compared to the scene boundaries of the content stream.