Video Identification Using Event Metrics and Segmentation
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Solution Overview
Problem
Current methods for identifying video works are unreliable when dealing with indirect copies, as they often fail to detect scene change events and time variations caused by transformations such as reformatting, noise introduction, and playback rate changes, making it difficult to accurately identify modified or partial copies of video content.
Innovation Solution
A video identification system that compares metrics between events in unknown video data to known works, using techniques like regression analysis and error thresholding to determine matches, even in the presence of noise or errors, and can identify partial or modified copies by selecting relevant data portions for comparison.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If scene change events are detected to identify video works, then identification reliability is improved for direct copies, but identification reliability deteriorates for indirect copies due to transformations like reformatting, noise introduction, and playback rate changes
Solution Approach 1:
The patent segments the video data into multiple portions and selects only relevant portions for comparison. This segmentation allows the system to focus on stable, identifiable features while ignoring transformed or noisy sections, thereby maintaining identification reliability across direct and indirect copies
Solution Approach 2:
The patent changes the parameters used for identification from fixed scene change events to flexible metrics that can accommodate transformations. By using regression analysis and error thresholding on selected data portions, the system adapts to playback rate changes, reformatting, and noise introduction while maintaining identification accuracy
2Measurement precision
If complete video data is analyzed for identification, then identification accuracy is improved, but processing time increases and may hinder data transfers
Solution Approach 1:
The patent extracts only the necessary portions of video data for identification rather than analyzing complete video data. By selecting relevant portions based on stability and identifiability criteria, the system achieves high identification accuracy while minimizing processing time and avoiding hindrance to data transfers
Solution Approach 2:
The patent applies partial action by analyzing only sufficient portions of the video data needed for reliable identification. This selective approach provides the minimum necessary processing to achieve accurate identification without the excessive time consumption of complete data analysis
Data Source
AI summary
A processor receives a first list comprising a plurality of events from a portion of digital data of an unknown work and one or more metrics between each pair of adjacent events from the plurality of events. The processor compares the first list to a second list comprising events and metrics between events for a known work to determine a first quantity of hits and a second quantity of misses. The processor determines whether the first list matches the second list based on the first quantity of hits and the second quantity of misses. The processor determines that the unknown work is a copy of the known work responsive to determining that the first list matches the second list.