Media Stream Time Mapping for Frame-Accurate Content Revision
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Solution Overview
Problem
Media clients struggle to perform frame-accurate content revisions in media streams due to lack of context for the revision time point, especially when generating query fingerprints at a low rate, leading to inaccurate synchronous lock between true-time and client-time.
Innovation Solution
Implement fingerprint matching with interpolation to establish a precise mapping between true-time and client-time by using multiple detected matching points and performing linear regression to improve the synchronization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by stationary object
If fingerprint matching is performed at a low rate to conserve resources, then device complexity and energy consumption are reduced, but measurement precision and synchronization accuracy deteriorate
Solution Approach 1:
The system performs preliminary fingerprint matching at a low rate to identify approximate matching points, then uses interpolation to predict the precise timing of content revisions. This preliminary action allows the system to establish a rough time mapping without requiring continuous high-rate fingerprinting, thereby reducing energy consumption while maintaining sufficient synchronization accuracy through mathematical interpolation between the sparsely sampled matching points.
Solution Approach 2:
The system introduces interpolation as an intermediary mechanism between low-rate fingerprint matching and precise timing determination. By using linear regression and time mapping interpolation, the system bridges the gap between sparse fingerprint data points and the continuous time synchronization required for frame-accurate content revision, enabling accurate timing prediction without requiring dense fingerprint sampling.
2Productivity
If fingerprint matching is performed at a low rate, then processing speed and resource usage are improved, but the accuracy of determining the revision time point deteriorates
Solution Approach 1:
The system performs preliminary fingerprint matching at a low rate to identify approximate matching points, then uses interpolation to predict the precise timing of content revisions. This preliminary action allows the system to establish a rough time mapping without requiring continuous high-rate fingerprinting, thereby reducing energy consumption while maintaining sufficient synchronization accuracy through mathematical interpolation between the sparsely sampled matching points.
Solution Approach 2:
The system introduces interpolation as an intermediary mechanism between low-rate fingerprint matching and precise timing determination. By using linear regression and time mapping interpolation, the system bridges the gap between sparse fingerprint data points and the continuous time synchronization required for frame-accurate content revision, enabling accurate timing prediction without requiring dense fingerprint sampling.
3Adaptability or versatility
If the client lacks context for the revision time point, then adaptability to different media streams is improved, but the ability to perform frame-accurate revisions deteriorates
Solution Approach 1:
The system introduces interpolation as an intermediary mechanism between low-rate fingerprint matching and precise timing determination. By using linear regression and time mapping interpolation, the system bridges the gap between sparse fingerprint data points and the continuous time synchronization required for frame-accurate content revision, enabling accurate timing prediction without requiring dense fingerprint sampling.
Solution Approach 2:
The system changes the parameter representation from absolute time values to relative time offsets derived from fingerprint matching. By calculating time offsets between corresponding fingerprints in the reference and received streams, and using these offsets to predict revision timing, the system achieves frame-accurate synchronization without requiring context about the absolute timing or structure of the media stream, thereby maintaining adaptability across different streams.
Data Source
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AI summary
A computing system identifies multiple matching points between (i) query fingerprints representing a media stream being received by a client and (ii) reference fingerprints, each identified matching point defining a respective match between a query fingerprint timestamped with client time defined according to a clock of the client and a reference fingerprint timestamped with true time defined according to a timeline within a known media stream. Further, the computing system performs linear regression based on the timestamps of the matching points, to establish a mapping between true time and client time. The computing system then uses the mapping to determine a client-time point at winch the client should perform a content revision or other action with respect to the media stream being received by the client. And the computing system causes the client to perform the content revision or other action at the determined client-time point.