Media Segment Identification via Audio-Linked Image 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 using landmark-linked-image-comparisons, reducing computational expense by comparing reduced grayscale images associated with audio peaks, and using a media segment database for matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pixel by pixel image comparison is used to identify media segments, then measurement precision is improved, but computational cost increases significantly
Solution Approach 1:
The patent divides the image comparison task into segments by using audio peaks as temporal landmarks to identify specific video frames for comparison. Instead of comparing all pixels continuously, the system segments the media content into discrete segments anchored by audio events, comparing only representative frames at these landmarks. This segmentation dramatically reduces computational cost while maintaining identification accuracy.
Solution Approach 2:
The patent extracts only the essential features needed for identification by comparing reduced grayscale images rather than full-color pixel data. The system extracts key visual information at audio landmark points and uses this extracted data for database matching, eliminating the need to process entire high-definition frames. This extraction approach preserves measurement precision while reducing computational burden.
2Measurement precision
If full-resolution color images are compared for media segment identification, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent changes the parameters of image representation by converting full-resolution color images to reduced grayscale images. This parameter transformation maintains the essential visual information needed for identification while significantly simplifying the data structure. The system processes grayscale intensity values rather than full-color pixel arrays, reducing device complexity while preserving sufficient measurement precision for media segment identification.
3Reliability
If audio-visual landmark-linked image comparison is used, then reliability is improved, but ease of operation decreases
Solution Approach 1:
The patent merges audio and visual analysis by linking video frame comparison to audio peak detection. The system combines temporal audio landmarks with visual image data to create a multi-modal identification approach. This merging improves reliability by using multiple independent features (audio timing and visual content) that cross-validate each other, making the system more robust to alterations in either modality alone.
Solution Approach 2:
The patent introduces audio peaks as an intermediary element that mediates between the audio stream and video frame selection. Rather than directly comparing all video frames, the system uses detected audio peaks as intermediary markers to select which video frames should be compared. This intermediary approach improves reliability by providing temporal anchoring points that are independent of video content alterations.
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 an audio landmark. The audio landmark is an audio peak that exceeds a predetermined threshold. Two digital images are then obtained, one associated directly with the audio landmark, and one obtained a predetermined landmark time removed from the first image. The two images are then used to provide a characteristic landmark-linked-image-comparison. The pair of images are reduced in pixel size and converted to gray scale. Corresponding pixels are compared to form a numeric comparison. One image is mirrored before comparison to reduce the possibility of null comparisons.