Video Cross-Talk Reducer Using Forward Modeling
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Solution Overview
Problem
Visual-collaborative systems face challenges with video cross-talk, where content displayed for a local user is captured by a camera and delivered to a remote user, often due to unsynchronized media streams, and existing solutions like multiplexing have performance and cost limitations.
Innovation Solution
The implementation of a video cross-talk reducer that uses forward modeling to estimate and subtract video cross-talk signals from captured images without requiring optical hardware or synchronization hardware, employing algorithms to separate and remove cross-talk signals based on signal processing principles, allowing for effective reduction of video cross-talk in visual-collaborative systems using off-the-shelf devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If multiplexing (temporal, wavelength, polarization) is used to reduce video cross-talk, then video cross-talk is reduced, but performance and cost limitations arise
Solution Approach 1:
The patent replaces complex optical hardware systems (multiplexing devices) with signal processing algorithms. The forward model estimates cross-talk based on captured images and display content, then subtracts the estimated cross-talk signal, achieving cross-talk reduction through computational methods rather than mechanical/optical multiplexing systems.
Solution Approach 2:
The patent introduces a forward model as an intermediary computational component that estimates the cross-talk signal. This model acts as a mediator between the captured image and the final output, allowing cross-talk removal without direct hardware intervention. The forward model uses display content and captured image information to generate an estimate of the cross-talk component.
2Object-affected harmful factors
If optical hardware or synchronization hardware is used to reduce video cross-talk, then video cross-talk is reduced, but cost increases
Solution Approach 1:
The patent uses inexpensive, readily available off-the-shelf devices (standard cameras and displays) combined with computational algorithms, replacing expensive specialized hardware. The solution leverages commodity technology that is already widely available, eliminating the need for costly optical hardware or synchronization equipment.
Solution Approach 2:
The patent substitutes expensive hardware-based cross-talk reduction systems with software-based signal processing. The forward model and cross-talk subtraction algorithms run on standard computing platforms, replacing the need for specialized optical hardware and synchronization equipment, thereby dramatically reducing system cost.
3Device complexity
If media streams are not synchronized, then system complexity is reduced, but video cross-talk becomes more complex
Solution Approach 1:
The patent introduces dynamic temporal offset estimation that adapts to varying synchronization conditions. The forward model accounts for temporal offsets between display and capture streams, allowing the system to handle unsynchronized media streams dynamically. This enables cross-talk reduction even when synchronization varies over time, without requiring rigid synchronization protocols.
Data Source
AI summary
A synchronization relationship determiner comprising an input visual information signal receiver configured to receive an input visual information signal, and a capture signal receiver configured to receive a capture signal generated by a capture device. The synchronization relationship determiner is configured to determine a synchronization relationship between the input visual information signal and the capture signal. The synchronization relationship determination is signal based.