Region of Interest Camera Parameter Adjustment
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Solution Overview
Problem
Conventional videoconferencing systems face challenges in determining optimal camera image control parameters, particularly under artificial lighting, leading to poor video conference experiences due to manual adjustments and inadequate auto modes that often ignore subjects of interest.
Innovation Solution
The implementation of deep learning-based systems and methods to detect and track subjects of interest by determining regions of interest within video frames, evaluating image quality, and adjusting camera parameters such as focus, exposure, and aperture to prioritize and enhance the depiction of these subjects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual adjustment of camera parameters is used, then image quality can be optimized, but operation complexity and time consumption increase
Solution Approach 1:
The system automatically detects subjects of interest and adjusts camera parameters without requiring manual intervention. The processor autonomously evaluates image quality metrics and modifies capture settings to optimize subject rendering, enabling the camera subsystem to self-optimize based on real-time scene analysis.
Solution Approach 2:
The system continuously monitors image quality metrics for regions containing subjects of interest and uses this feedback to dynamically adjust camera parameters. The closed-loop control ensures that parameter adjustments are made based on actual image quality outcomes, maintaining optimal subject rendering throughout the video conference.
2Ease of operation
If auto mode is used for camera parameters, then ease of operation is improved, but image quality deteriorates because subjects of interest are ignored
Solution Approach 1:
The system applies different processing priorities to different regions of the image frame. Subjects of interest are identified and given higher priority for parameter optimization, while other regions receive standard processing. This localized approach ensures that automatic mode focuses computational resources on improving subject quality rather than treating the entire frame uniformly.
Solution Approach 2:
The image frame is segmented into regions of interest containing subjects and other background regions. The system separately evaluates image quality metrics for subject regions and applies parameter adjustments specifically targeted at improving subject rendering, rather than optimizing the entire frame equally.
3Manufacturing precision
If camera parameters are adjusted for entire frame, then overall image quality improves, but subject-specific quality cannot be optimized
Solution Approach 1:
The system evaluates image quality metrics specifically for regions containing subjects of interest rather than averaging across the entire frame. This localized quality assessment enables parameter adjustments that are optimized for subject rendering, ensuring that subject-specific quality requirements are met even if other frame regions receive different treatment.
Data Source
AI summary
A teleconferencing system, comprising: a camera, wherein the camera is configured to capture an image data stream comprising one or more image data frames in accordance with one or more image-capture parameters; and a processor coupled to the camera, wherein the processor is configured to: receive the image data stream from the camera; determine a region of interest within an image data frame of the image data stream; identify a sub-portion of the image data frame corresponding to the region of interest; evaluate a quality of image data corresponding to the sub-portion; determine that the quality of the image data corresponding to the sub-portion is below one or more predetermined quality thresholds; update one or more of the image-capture parameters of the camera, responsive to determining that the quality of the image data corresponding to the sub-portion is below the one or more predetermined quality thresholds.