Local Video Feedback for Videoconferencing Quality Matching
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Solution Overview
Problem
Current videoconferencing systems fail to provide accurate feedback to the local party about the video quality experienced by the remote party, leading to miscommunication and disruptive situations due to network artifacts, as the local party is unaware of the video distortion or loss seen by the remote party.
Innovation Solution
A video-feedback mechanism that predicts and degrades the local video quality to match the remote video quality, using network condition statistics and remote system information to automatically adjust the local video display, ensuring continuous feedback and adaptation to network conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the local video is displayed in high quality, then the local party can see themselves clearly, but the remote party experiences video degradation due to network conditions
Solution Approach 1:
The system implements a feedback mechanism where network condition statistics and remote system information are continuously monitored and fed back to the local video processor. This feedback loop enables the local party to see a predicted version of how their video appears to the remote party, allowing them to adjust their behavior and communication style accordingly.
Solution Approach 2:
The invention creates a copy of the local video that is degraded to match the remote video quality. This predicted video is generated by applying degradation algorithms that simulate network artifacts, packet loss, and compression effects. The degraded copy is then displayed alongside or instead of the original high-quality local video, giving the local party accurate feedback about remote reception.
2Ease of operation
If network conditions are poor, then remote video quality degrades with artifacts, but the local party remains unaware of the degradation
Solution Approach 1:
The system continuously monitors network conditions and provides real-time feedback to the local party through the predicted video display. This feedback mechanism makes the invisible network degradation visible to the local party, allowing them to understand why the remote video quality varies and adjust their communication accordingly.
Solution Approach 2:
The system visually represents network quality degradation through changes in the predicted video appearance. As network conditions worsen, the predicted video shows increasing artifacts, blockiness, and quality degradation that mirror what the remote party experiences, making the abstract concept of network quality tangible and visible.
3Loss of information
If symbols or text messages are used for feedback, then network condition information is provided, but the actual video degradation is not captured
Solution Approach 1:
Instead of using abstract symbols or text to represent network conditions, the system creates a visual copy of the local video that is degraded to match the remote reception quality. This visual copy accurately represents the actual video degradation including artifacts, blockiness, and quality loss, providing intuitive and precise feedback about remote video quality.
Solution Approach 2:
The invention inverts the traditional feedback approach by instead of showing the local party how the remote party sees them through abstract indicators, it directly presents a visual representation of the degraded video quality. This inversion makes the feedback more intuitive and accurate by showing the actual visual impact of network degradation.
Data Source
AI summary
A system and method enabling a local party to see how a remote party is viewing him/her during a videoconference is provided. This is accomplished by predicting and changing the local video to a similar video quality level as the video quality displayed with which the local video is displayed on a remote display. This process occurs without any input from the parties/user(s). Instead the prediction and changing of the local video occurs in an automatic fashion and continuously.