Media Segment Identification via Audio-Visual Cross-Comparison
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Solution Overview
Problem
Existing methods for identifying media segments in real-time digital media content streams are computationally expensive and prone to errors due to alterations such as resizing or color filtering, especially in high-definition broadcasts.
Innovation Solution
A system and method that combines audio and video analysis to generate a unique identifier for media segments by reducing images to a manageable size and comparing corresponding pixels, using landmark times and image-cross-comparisons to efficiently match segments against a database of previously identified media.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pixel by pixel comparison is used to match images, then measurement precision is improved, but computational complexity increases significantly
Solution Approach 1:
The patent divides the image into multiple blocks or regions, and performs comparison operations on these segmented parts rather than on the entire image at once. This segmentation approach reduces the computational complexity of pixel-by-pixel comparison while maintaining matching accuracy through hierarchical processing.
Solution Approach 2:
The patent extracts key features or representative pixels from image blocks rather than comparing all pixels. By taking out only the essential comparison elements (such as landmark pixels or feature points), the system achieves accurate image matching with significantly reduced computational requirements.
2Reliability
If full image comparison is performed in real-time broadcast, then reliability of identification is improved, but processing speed decreases
Solution Approach 1:
The patent performs preliminary processing of images by pre-computing features, extracting key blocks, or creating simplified representations before the actual comparison operation. This preliminary action ensures that when real-time comparison is needed, the processing is already optimized, maintaining both reliability and speed.
Solution Approach 2:
The patent applies partial comparison by focusing only on critical image regions or using a two-stage approach where a coarse comparison is performed first, followed by detailed comparison only for candidate matches. This partial action approach maintains identification reliability while dramatically improving processing speed for real-time applications.
3Adaptability or versatility
If image resizing or color filtering is applied to the broadcast stream, then adaptability is improved, but measurement precision deteriorates
Solution Approach 1:
The patent employs parameter changes by transforming images into feature spaces or using scale-invariant feature detection methods. These parameter transformations allow the system to adapt to different image resolutions and color formats while maintaining matching precision through invariant feature comparison.
Solution Approach 2:
The patent creates a universal comparison framework that can handle multiple image formats, resolutions, and color spaces through a common feature extraction and comparison mechanism. This multi-functional approach allows the system to adapt to various broadcast stream formats without sacrificing measurement precision.
Data Source
AI summary
A system and method for identifying media segments using audio augmented image cross-comparison is disclosed, in which a media segment identifying system analyses both audio and video content, producing a unique identifier to compare with previously identified media segments in a media segment database. The characteristic landmark-linked-image-comparisons are constructed by first identifying pairs of audio landmarks separated by a characteristic, or landmark, time. Digital images associated the audio landmarks are then compared, with the combination providing a characteristic landmark-linked-image-comparison. The audio landmarks are audio peaks that exceed predetermined thresholds. A landmark-time is the time between adjacent pairs of audio peaks. The pair of images associated with the audio peaks are reduced in pixel size and converted to gray scale. Corresponding pixels are compared to form a numeric comparison. One image may be mirrored before comparison to reduce the possibility of null comparisons.