Virtual Meeting Background Adjustment for Contextual Discrepancies
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Solution Overview
Problem
Conventional conferencing applications require users to manually adjust background images and camera settings to achieve aesthetically pleasing video streams, which is inconvenient and burdensome due to mismatches in size, lighting, and scenery between the user's image and the background.
Innovation Solution
A method that captures a user's image and identifies contextual discrepancies between the user's image and the background, dynamically adjusting aspects of the background or user's image to negate these discrepancies, such as resizing, changing lighting, or repositioning, to ensure a more realistic and visually appealing presentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If users manually adjust background images and camera settings, then the video stream quality can be improved, but the operation convenience deteriorates
Solution Approach 1:
The system automatically detects contextual discrepancies between the user's image and background image, and autonomously adjusts background parameters (size, lighting, scenery) without requiring manual user intervention. The conferencing application performs self-adjustment based on real-time analysis of the user's video feed and background compatibility metrics.
Solution Approach 2:
The system dynamically modifies multiple parameters of the background image including size scaling, lighting intensity, color temperature, and scenery selection based on the detected contextual discrepancies. These parameter adjustments are automatically applied to resolve mismatches between the user's appearance and the background environment.
2Manufacturing precision
If the background image is superimposed with the user's image, then the video stream can be enhanced, but contextual discrepancies arise
Solution Approach 1:
The system continuously monitors the superimposition of the user's image over the background image and detects contextual discrepancies in real-time. Based on this feedback, the system automatically adjusts background parameters to eliminate inconsistencies in lighting, size, and scenery, ensuring contextual harmony between foreground and background elements.
Solution Approach 2:
The system proactively identifies potential contextual discrepancies before they become noticeable to users. By detecting mismatches in lighting conditions, size proportions, and scenery compatibility in advance, the system pre-adjusts background parameters to prevent visual inconsistencies from appearing in the final video stream.
Data Source
AI summary
One embodiment provides a method, including: capturing, using a camera sensor of an information handling device, an image associated with a user; identifying, in a conferencing application, a background image for a video stream of the user; determining, based on comparing the background image to the image of the user, a contextual discrepancy for a superimposition of the image of the user over the background image; and adjusting, based on the determining, one or more aspects of the background image or one or more aspects of the image of the user to negate the contextual discrepancy. Other aspects are described and claimed.