Video Frame Slicing for Conference Resource Optimization
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Solution Overview
Problem
Existing multimedia multi-user collaboration applications (MMCA) for video conferencing processes video frames of all participants equally, leading to high computational burden and resource consumption, especially when only a few participants are actively engaged, as they perform intensive video processing on all participants' devices.
Innovation Solution
An information handling system that uses a time-of-flight sensor to create a human object presence (HOP) heat map, slicing video frames and encoding them differently based on participant presence, with high-quality encoding for active participants and lower computational burden for background portions, reducing resource consumption by selectively applying encoding and decoding algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If video frames of all participants are processed equally with intensive video processing, then image quality of active participants is maintained, but computational burden and resource consumption increase significantly
Solution Approach 1:
The patent applies different processing quality levels to different spatial regions of the video frame. Active participant regions receive high-quality processing while background and inactive participant regions receive lower-quality processing, thereby maintaining image quality for important content while reducing overall computational burden.
Solution Approach 2:
The video frame is segmented into multiple regions based on participant activity status. The system identifies and separates active participants from background/inactive participants, allowing differential processing strategies to be applied to each segment, thus reducing resource consumption while preserving quality where needed.
2Manufacturing precision
If intensive video processing is applied to all participants, then video quality is maintained, but processing time and latency increase
Solution Approach 1:
The system applies high-quality video processing only to regions containing active participants while using lower-quality processing for background regions. This localized quality approach maintains video quality for important content while significantly reducing the total processing time required.
3Ease of operation
If video processing resources are allocated equally to all participants, then fairness is maintained, but resource efficiency decreases when only a few participants are active
Solution Approach 1:
The system dynamically adjusts resource allocation based on real-time detection of participant activity status. When only a few participants are actively engaged, resources are concentrated on those active participants. The fairness mechanism ensures that resource distribution adapts to current usage patterns rather than remaining static.
4Manufacturing precision
If high-quality encoding is applied to all video frames, then transmission quality is maintained, but network bandwidth consumption and processing power increase
Solution Approach 1:
The encoding process applies high-quality compression algorithms only to video frame regions containing active participants, while using more aggressive compression for background regions. This maintains transmission quality for important content while reducing the overall processing power and energy required for encoding.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach reduces processing resources used during video conferencing by prioritizing high-quality image transmission of active participants, decreasing latency and improving user experience by optimizing video frame processing and transmission.
Implementation Method 1
receive a time-of-flight (TOF) sensor data or other distance sensor data descriptive of the distance between a remote participant's body and a video camera
Data Source
AI summary
A sink information handling system executing a multimedia multi-user collaboration application (MMCA) may comprise a network interface device to receive a plurality of video frame slices of a video frame of a remote participant user of a source information handling system participating in a video conference session, the network interface device to receive an identification of a subset of the plurality of video frame slices having an assigned probability of presence of the remote participant's body that exceeds a threshold probability, based on a human object presence (HOP) heat map, as a HOP video frame slice group, the processor to decode data within the HOP video frame slice group, and a digital display device to display the video frame, as transformed by the audio/visual processing instruction algorithm.