Patch-Based Video Fingerprinting for Compression-Robust Identification
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Solution Overview
Problem
Existing video content identification systems face challenges in efficiently and accurately identifying video content across various devices and formats, particularly in real-time environments with varying compression artifacts and aspect ratios.
Innovation Solution
The system generates query fingerprints of video frames using integral image techniques and Haar-like features, which are matched against a database of reference fingerprints to identify video content, utilizing patch selection, value calculation, and distance measurements to ensure robustness and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional video content identification methods are used, then the system can identify video content, but the identification accuracy decreases when compression artifacts and aspect ratio changes are present
Solution Approach 1:
The video frame is divided into multiple patches, and fingerprints are extracted from each patch independently. This segmentation approach makes the identification system more robust to local variations caused by compression artifacts and aspect ratio changes, as the global fingerprint is composed of multiple local features that can tolerate individual variations.
Solution Approach 2:
The system transforms video frames into a feature space using integral images and Haar-like features, changing the representation parameters from raw pixel values to extracted features. This parameter transformation makes the fingerprint extraction invariant to certain transformations like aspect ratio changes and compression artifacts.
2Reliability
If comprehensive video analysis is performed to ensure accurate identification, then identification reliability improves, but processing time increases
Solution Approach 1:
The system pre-computes integral images from the video frames before extracting fingerprints. This preliminary action creates a data structure that enables efficient fingerprint extraction, reducing the processing time required for comprehensive video analysis while maintaining identification reliability.
Solution Approach 2:
The system replaces complex mechanical video analysis with a streamlined computational approach using integral images and Haar-like features. This substitution maintains high identification reliability while significantly reducing processing time through mathematically efficient operations.
3Measurement precision
If detailed fingerprint extraction is used to improve identification accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The system replaces complex fingerprint extraction mechanisms with a simplified computational approach based on integral images and Haar-like features. This substitution achieves high measurement precision through mathematically elegant operations while reducing device complexity by avoiding elaborate processing pipelines.
Data Source
AI summary
A query fingerprint of a set of frames of video content captured at a client device may be generated. Multiple patches of the set of frames of video content may be selected and a value calculated for each of the selected multiple patches. The value for each patch may be indicated as a single bit along with an additional 1-bit value to indicate whether the patch value is weak. A database of known reference fingerprints may be queried using the generated query fingerprint. Matches between the query fingerprint and the reference fingerprints may be identified. Weak bits may be given reduced weight in identifying the match of fingerprints. Based on the matches, an identifier for the video content may be returned to the client device. The client device may use the received identifier to access the supplemental content.