AI Media Comparison Engine for Frame Matching
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Solution Overview
Problem
Current media content matching solutions are manual, time-consuming, and inefficient, particularly when dealing with videos that have undergone various image edits such as VFX, crops, zooms, and resolution changes, which complicates tasks like re-telecasting, comparing master videos, and identifying redundancies.
Innovation Solution
A system and method utilizing artificial intelligence and vision algorithms to efficiently match frames in media assets by computing signatures for each frame, determining an optimal search space, and using a rate of information exchange (RIE) score to identify matching frames, even across different frame rates and with image manipulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual content matching is performed, then accuracy can be maintained through expert verification, but time consumption and resource usage increase substantially
Solution Approach 1:
The patent replaces manual mechanical verification processes with automated AI-based computer vision algorithms. The system uses neural networks and image recognition models to automatically compare video frames, detect content similarities, and identify matches without human intervention, thereby maintaining accuracy while dramatically reducing time consumption.
Solution Approach 2:
The patent introduces an AI-based media comparison engine as an intermediary between the video content and the matching process. This engine uses trained neural networks to act as a mediator that automatically analyzes video frames, extracts features, and determines content matches, eliminating the need for manual expert verification while preserving matching quality.
2Measurement precision
If frame-accurate matching is performed for subtitle retiming and DI validation, then precision is improved, but the complexity and cost of the process increase
Solution Approach 1:
The patent segments the video content into individual frames and processes them through a systematic AI-based comparison pipeline. The media comparison engine divides the complex task of frame-accurate matching into manageable steps: frame extraction, feature detection, similarity computation, and match verification, thereby reducing process complexity while maintaining frame-level precision.
Solution Approach 2:
The patent changes the parameters of the matching process by using AI-based parameter extraction and comparison. Instead of manual frame-by-frame analysis, the system automatically extracts relevant parameters such as content features, temporal information, and spatial characteristics, enabling frame-accurate matching with reduced complexity through automated parameter manipulation.
3Manufacturing precision
If high resolution video scanning is performed for re-mastering, then quality is improved, but costs and time consumption increase
Solution Approach 1:
The patent performs preliminary AI-based content analysis and matching before the actual high-resolution scanning process. The media comparison engine pre-identifies which portions of source content are present in the master video file using automated frame comparison, allowing only those specific portions to be scanned at high resolution, thereby reducing both time and cost while maintaining quality.
Solution Approach 2:
The patent extracts and identifies only the necessary portions of video content that require high-resolution scanning. By using AI-based matching to extract the specific segments that need re-mastering, the system avoids scanning entire video files, thereby reducing time consumption and costs while preserving the required video quality for the extracted portions.
4Productivity
If automated neural network solutions are used for frame matching, then speed is improved, but implementation costs and training data requirements increase
Solution Approach 1:
The patent employs lightweight, pre-trained neural network models that can be deployed without extensive custom training data requirements. The system uses efficient, computationally manageable AI models that provide sufficient matching speed while reducing implementation costs and avoiding the need for large-scale training datasets, making automated matching economically viable.
Data Source
AI summary
A system (700) including a media comparison engine (MCE) (507) and a method for determining matches between frames in media assets for automating downstream media workflows are provided. For a target master frame in each boundary of master shots of a master media asset (MMA), the MCE (507) performs a comparison with each source frame from each source media asset (SMA), in an optimal search space, using computed signatures; determines matches in the optimal search space; computes a rate of information exchange (RIE) score for each match based on the signature(s) and similarity scores of each source frame in a final search space; identifies a best match based on the RIE score of each match; performs a comparison of source frames subsequent to the best matching source frame with master frames subsequent to the target master frame.