Burn-in Overlay Detection in Video Imagery
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Solution Overview
Problem
Existing video processing systems struggle to efficiently and automatically identify and remove burn-in overlays from video imagery, leading to adverse impacts on video exploitation and delaying the release of video data in real-time.
Innovation Solution
A system comprising a processor coupled to memory, which receives an image sequence and applies a mask to each image, using edge detection and machine learning algorithms to detect and classify feature vectors indicative of overlays, thereby initiating a redaction process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If constant redaction is applied to every image in the video sequence, then overlays are removed, but video exploitation performance deteriorates due to false redaction of legitimate image content
Solution Approach 1:
The system dynamically adjusts the redaction process by analyzing each frame individually to determine whether overlays are present. Instead of applying constant redaction to all frames, the system adaptively applies redaction only when and where overlays are detected, using techniques such as motion detection, edge analysis, and machine learning classifiers to make frame-by-frame decisions about redaction necessity
Solution Approach 2:
The system applies redaction locally only to specific regions where overlays are detected rather than uniformly across the entire video sequence. By identifying the precise location and extent of overlay content in each frame, the system redacts only those specific areas, preserving the quality and usability of the rest of the video content for exploitation purposes
2Reliability
If overlay detection and removal is performed on every still-image in real-time, then sensitive information is protected, but processing time and computational resources are excessively consumed
Solution Approach 1:
The system performs preliminary analysis on video frames to quickly identify potential overlay regions before applying full detection and rediction algorithms. By using initial screening techniques such as motion detection, color thresholding, or region-of-interest identification, the system prepares the data structure and identifies candidate areas in advance, reducing the computational burden of subsequent detailed analysis and enabling real-time processing
Solution Approach 2:
The system applies a tiered detection approach where a simplified overlay detection algorithm is applied to all frames, and more computationally intensive analysis is applied only to frames where overlays are suspected. This partial application of full detection resources—focusing computational power only where needed—reduces overall processing time while maintaining detection reliability
3Measurement precision
If comprehensive overlay detection algorithms are applied to identify and remove overlays, then overlay removal accuracy is improved, but device complexity and processing resources increase
Solution Approach 1:
The overlay detection system is divided into multiple independent modules or stages, each responsible for a specific aspect of detection: motion analysis, edge detection, color space transformation, pattern recognition, and classification. This segmentation allows each module to be optimized independently and enables parallel processing, improving detection accuracy while managing system complexity through modular architecture
Data Source
AI summary
Systems, methods and computer systems for the automatic determination of presence or absence of burn-in overlay data are provided. The systems, methods, and computer systems implement mask generation, edge detection, feature vector generation methods that are combined with machine learning classifiers to rapidly and automatically determine the presence or absence of burn-in overlays in the image for the purpose of removal or other forms to obfuscate burn-in overlay data so as to maintain confidential or classified information while allowing for the release of remaining image data.