High-Definition Video Segmentation With Browser GPU Inference
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Solution Overview
Problem
Existing web-based video conferencing systems face challenges in achieving high-quality real-time image segmentation due to computational limitations and network constraints, resulting in low-quality image processing and user experience.
Innovation Solution
Implementing a machine-learned image segmentation model on user computing devices, leveraging GPU inferencing through WebGL or WebGPU, to generate high-definition segmentation masks for objects in video streams, allowing for real-time processing and augmented image generation with virtual backgrounds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine-learned image segmentation models are implemented in web browsers for real-time segmentation, then segmentation quality and real-time processing capability are improved, but computational resources and system complexity increase
Solution Approach 1:
The patent replaces traditional server-based image processing with client-side GPU-accelerated machine learning models. WebGL and WebGPU technologies enable direct GPU access from browsers, substituting the need for complex server infrastructure with distributed client computing. This achieves high-quality segmentation while reducing centralized system complexity.
Solution Approach 2:
The system enables user computing devices to autonomously perform image segmentation using locally downloaded machine-learned models. The web application automatically handles model deployment, GPU resource allocation, and processing without requiring manual configuration or complex setup. Users simply need to provide video input, and the system self-manages the computational pipeline.
2Manufacturing precision
If high-definition video segmentation is processed in real-time, then video conferencing quality is improved, but network bandwidth and processing time requirements increase
Solution Approach 1:
The patent segments the video processing task into discrete frames, processing each frame independently through the machine-learned segmentation model. This frame-by-frame approach enables real-time processing while maintaining high definition quality. The segmentation allows parallel processing of multiple frames and efficient network transmission of only essential segmentation data rather than entire video streams.
Solution Approach 2:
The system performs preliminary model download and initialization before actual video processing begins. Machine-learned models are cached on user devices beforehand, so when video conferencing starts, no additional data transmission or model loading is required during real-time processing. This preliminary preparation eliminates delays during critical video transmission periods.
3Productivity
If machine-learned segmentation models are deployed on user computing devices, then processing speed is improved, but computational costs and hardware requirements increase
Solution Approach 1:
The patent creates a universal web-based platform that can run on diverse user computing devices without requiring device-specific optimization. The same machine-learned model works across different browsers and hardware configurations through standardized WebGL/WebGPU APIs. This universality allows efficient processing on a wide range of devices while distributing computational load across the network rather than requiring expensive specialized hardware at each endpoint.
Data Source
AI summary
Systems and methods for image segmentation can include downloading a machine-learned image segmentation model to be utilized while in the web browser. For example, a user can access a web service, which can initiate the download of a software package including the machine-learned image segmentation model. The image segmentation model can then be utilized for segmenting image data obtained with a user computing device.