Intelligent Session Management for Video Conferencing Quality
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Solution Overview
Problem
Existing multimedia multi-user collaboration applications (MMCA) face challenges in maintaining video quality during large-scale video conferences, as most perform uniform video processing on all participants, leading to video degradation due to factors like blur, compression, and color vector artifacts, which can be fleeting or ongoing, and require manual intervention by IT managers.
Innovation Solution
An intelligent collaboration contextual session management system (ICCSMS) using a trained neural network that analyzes video frames for blur, compression, and color vector artifacts, and provides real-time processing instructions to remediate these issues by adjusting processing resources, camera settings, and encryption/decryption parameters, and can generate service tickets for IT management when threshold levels are exceeded.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If uniform video processing is applied to all participants, then processing simplicity is maintained, but video quality degrades due to blur, compression, and color vector artifacts
Solution Approach 1:
The system applies different processing methods to different video frames based on their specific characteristics. The neural network analyzes each frame to determine the type of degradation present (blur, compression artifacts, or color vector artifacts) and selects appropriate remediation techniques, rather than applying uniform processing to all frames. This localized approach improves video quality while managing processing complexity through intelligent selection.
Solution Approach 2:
The system dynamically adjusts processing parameters based on the detected video degradation type. When blur is detected, the system applies deblurring algorithms; when compression artifacts are present, it uses artifact reduction techniques; when color vector artifacts occur, it adjusts color processing parameters. These parameter changes are made in real-time based on neural network analysis of each video frame.
2Reliability
If manual intervention by IT managers is used to address video degradation, then serviceability is maintained, but response time increases and productivity decreases
Solution Approach 1:
The system automatically detects video degradation issues and applies appropriate remediation without requiring manual intervention from IT managers. The neural network continuously monitors video frames, identifies degradation types, and triggers automated processing adjustments to correct the issues. This self-service capability maintains serviceability while dramatically improving response time and productivity.
Solution Approach 2:
The system implements a feedback loop where the neural network continuously analyzes video output quality and adjusts processing parameters in real-time based on detected degradation. When video quality metrics indicate problems (blur, compression artifacts, color vector artifacts), the system automatically modifies processing settings and continues monitoring to ensure improvement, creating a closed-loop control system that maintains video quality without manual intervention.
3Reliability
If real-time video analysis is performed on all frames, then video quality improvement is achieved, but processing resource consumption increases
Solution Approach 1:
The system performs full neural network analysis only on video frames that exhibit degradation symptoms. The initial filtering stage quickly identifies frames with quality issues, and only those frames undergo comprehensive neural network analysis and remediation. This partial action approach maintains video quality improvement while reducing overall processing resource consumption by avoiding unnecessary analysis of already-acceptable frames.
Data Source
AI summary
An information handling system executing a multimedia multi-user collaboration application (MMCA) including a memory; a power management unit; a camera to capture video of a user participating in a video conference session; a processor configured to execute code instructions of a trained intelligent collaboration contextual session management system (ICCSMS) neural network to receive as input: computations from an execution of a blur AV detection processing instruction module by the processor descriptive of a blur in an image frame received at a multimedia framework pipeline and infrastructure platform (MFPIP); computations from an execution of a compression artifact AV detection processing instruction module by the processor descriptive of compression artifacts present in the image frame received at the MFPIP; and computations from an execution of a color vector AV detection processing instruction module by the processor descriptive of color vector artifacts present in the image frame received at the MFPIP; the trained ICCSMS to provide, as output, processing instructions to remediate the occurrence of blur, compression, and color vector artifacts in subsequently-received image frames.