Watermark Decoding Selective Component Accumulation
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Solution Overview
Problem
Existing multimedia signal processing technologies face challenges in achieving fine granularity in media content identification while maintaining robustness and minimizing perceptibility, particularly under noisy conditions, as well as managing the computational cost of fingerprint databases.
Innovation Solution
A combination of watermarking and fingerprinting techniques is employed, where watermarks provide fine granularity and robustness through aggregation, while fingerprinting aids in localization and synchronization, reducing the need for explicit synchronization signals and improving robustness by identifying media signals for watermark recovery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a unique watermark payload is embedded in each frame to achieve fine granularity, then the ability to identify individual frames is improved, but watermark robustness drops under noisy conditions
Solution Approach 1:
The watermark payload is segmented into multiple components: a common payload repeated across frames and unique payload components that vary per frame. This segmentation allows the common payload to be aggregated for robustness while the unique components provide frame-level granularity identification.
Solution Approach 2:
The patent merges watermarking and fingerprinting techniques into a hybrid system. The watermark provides robustness through aggregation across frames, while the fingerprinting component provides fine-grained identification. This combination resolves the contradiction by leveraging the strengths of both approaches.
2Reliability
If the same watermark payload is repeatedly embedded in each frame to improve robustness through aggregation, then watermark robustness is improved, but the ability to distinguish between different portions of video signal is lost
Solution Approach 1:
The watermark payload is divided into common and unique components. The common payload is repeated across frames to enable aggregation and improve robustness, while the unique payload components are embedded in specific frames to provide granularity and distinguishability of different video portions.
3Measurement precision
If fingerprinting is used to achieve fine granularity by extracting unique fingerprints for each portion of media signal, then granularity is improved, but the size of fingerprint database and computational cost increase
Solution Approach 1:
Instead of creating unique fingerprints for every possible portion of media signal, the system uses selective accumulation of watermark components from a subset of frames. This partial action approach achieves sufficient granularity for identification while significantly reducing the database size and computational requirements compared to comprehensive fingerprinting.
4Measurement precision
If watermarking is used to serialize media content with distinct payloads, then the ability to distinguish identical copies is improved, but perceptibility of the watermark signal increases
Solution Approach 1:
The watermark signal is designed with local quality variations: in some regions frames contain unique payload components for serialization, while in other regions only common payload components are embedded. This allows the system to achieve serialization capability where needed while maintaining imperceptibility in regions where unique watermarks would be too visible.
Data Source
AI summary
A method of digital watermark decoding in which watermark signal components, which are embedded with different levels of redundancy in a host content signal, are selectively accumulated. In the process of decoding embedded data, components embedded with more redundancy are weighted appropriately to improve recovery of embedded data. Components embedded with less redundancy may be more reliably recovered as well by leveraging knowledge of related components encoded more robustly. Missing, weak or error prone components may be recovered based on relationship with more reliable components, and in particular, based on relationship with components embedded with higher redundancy and decoded with higher reliability. These techniques are exploited through error correction coding schemes, including convolutional codes, and explicit and implicit weighted repetition coding schemes.