SOI-Based Video Stream Resolution Selection for Bandwidth Efficiency
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Solution Overview
Problem
Video conferencing systems inefficiently use bandwidth and degrade video quality due to the need to digitally zoom in on subjects of interest at varying distances, leading to unnecessary upscaling and downscaling operations.
Innovation Solution
Optimize video stream resolution at capture based on the size of the subject-of-interest (SOI) using size detection techniques like head size detection, dynamically adjusting resolution to minimize upscaling and downscaling, thereby preserving quality and reducing bandwidth usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution video streams are transmitted for all subjects, then video quality is improved, but bandwidth consumption increases
Solution Approach 1:
The patent applies local quality by transmitting high-resolution video streams only for subjects of interest (SOIs) that meet specific size criteria, while transmitting lower-resolution streams for other subjects. This selective approach ensures that bandwidth is consumed only where high quality is necessary, resolving the contradiction between video quality and bandwidth consumption.
Solution Approach 2:
The system dynamically changes the resolution parameter of video streams based on the detected size of subjects in the scene. By adjusting the resolution parameter according to subject size and importance, the system optimizes the balance between video quality and bandwidth consumption for different regions of the video feed.
2Stability of the object's composition
If digital zooming is applied to subjects at varying distances, then uniform resolution is achieved, but video quality degrades due to upscaling and downscaling
Solution Approach 1:
The patent implements dynamic resolution selection where the resolution of each subject's video stream is adjusted in real-time based on the subject's detected size in the scene. This dynamic approach eliminates the need for uniform digital zooming and subsequent upscaling/downscaling operations, preserving video quality while adapting to varying subject distances.
Solution Approach 2:
Instead of applying uniform resolution to all subjects through digital zooming, the system applies different resolution levels to different subjects based on their individual characteristics and distances. This local quality approach prevents the quality degradation associated with blanket upscaling and downscaling operations.
3Ease of operation
If uniform resolution is transmitted for all subjects, then ease of transmission is improved, but bandwidth is wasted on subjects that don't require high resolution
Solution Approach 1:
The system transmits video streams at different resolution levels tailored to each subject's requirements. Subjects that are smaller or less important receive lower-resolution streams, while prominent subjects receive high-resolution streams. This eliminates bandwidth waste while maintaining transmission simplicity through automated resolution assignment.
Solution Approach 2:
The resolution parameter of each subject's video stream is dynamically changed based on detected subject size and importance. This parameter adaptation allows the system to optimize bandwidth usage automatically, transmitting only the necessary amount of data for each subject without manual intervention.
Data Source
AI summary
The system may determine the size of the SOI from an uncropped, non-zoomed-in image (e.g., a video stream or static image). Based upon the size of the image, the system can determine the optimal resolution for each SOI video stream. This approach minimizes the need for upscaling and downscaling operations, thereby preserving video quality and reducing bandwidth usage.