AI Video Frame Selection for High-Quality Image Generation
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Solution Overview
Problem
Existing technologies face challenges in generating high-quality images of individuals that meet specific criteria, such as natural-looking, professional, or cheerful, in a time-efficient manner, especially during video conferences.
Innovation Solution
A system that uses pre-trained AI/ML models to identify video frames from a video conference that match specified features, allowing for the automatic generation of images. The client device downloads the model data, identifies suitable frames, and transmits timestamps to a server, which selects the best frames based on AI techniques and generates images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If professional photography is used to capture high-quality images, then image quality meets specific criteria, but time consumption and cost increase significantly
Solution Approach 1:
The patent replaces the mechanical photography system with an AI-based automated system. The image selection engine uses machine learning models to automatically analyze video frames and identify images meeting specified criteria, eliminating the need for professional photographers and manual image selection processes.
Solution Approach 2:
The system enables self-service by automatically selecting and generating high-quality images from video conference data without human intervention. The image selection engine autonomously evaluates frames based on predefined criteria and generates suitable images for various purposes.
2Productivity
If manual image selection from video frames is performed, then images can be obtained, but the process is time-consuming and inefficient
Solution Approach 1:
The patent replaces manual image selection with an automated AI system. The image selection engine continuously analyzes video frames in real-time, automatically identifying and selecting frames that meet specified criteria, thereby dramatically improving productivity and eliminating time-consuming manual processes.
Solution Approach 2:
The system maintains continuous operation by processing video frames in real-time throughout the video conference. The image selection engine continuously evaluates incoming frames without interruption, ensuring that suitable images are captured at the optimal moments without manual intervention.
3Extent of automation
If AI/ML models are used to automatically identify video frames with specified features, then image generation becomes efficient and automatic, but system complexity increases
Solution Approach 1:
The patent divides the complex AI system into distinct functional modules: the image selection engine for frame analysis, the machine learning model for feature evaluation, and the image generation component. This segmentation manages complexity by organizing the automated system into manageable, independent units with clear interfaces.
4Speed
If pre-trained AI models are downloaded and used locally, then processing speed improves, but device storage requirements increase
Solution Approach 1:
The system performs preliminary action by downloading and storing pre-trained AI models in advance of actual image selection tasks. This allows the models to be readily available in local memory during video conferences, enabling fast real-time processing without the need for cloud-based inference, thus achieving high processing speed with acceptable storage requirements.
Data Source
AI summary
A server receives an identifier of a video frame of a video conference from a client device. The server obtains a time-contiguous set of video frames based on the identifier. The server computes, for each frame in at least a subset of the time-contiguous set of video frames, a score corresponding to a likelihood of having a specified feature. The server determines, based on the computed scores, a frame having a highest likelihood of having the specified feature. The server generates, for storage in a data repository, an image based on the determined frame.