Moving Image Analysis Using Representative Frames for Reliable LLM Extraction
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Solution Overview
Problem
Existing methods for analyzing moving images struggle with uncertainty in content format and lack of reliability in extracting specific information due to unknown conditions, such as lighting and narrative, and large language models (LLMs) provide unreliable results.
Innovation Solution
A method involving preprocessing discrete representative image frames using digital image processing, followed by analysis with LLMs to enhance reliability in extracting specific information from moving images, utilizing event detection and feature detection to reduce computational burden and improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If large language models are used to process moving images directly, then the system can handle unstructured data and provide textual responses, but the reliability of the results is poor
Solution Approach 1:
The patent applies preliminary action by extracting and preprocessing representative frames from the moving image before feeding them to the LLM. The system selects discrete representative image frames at specific time points and preprocesses them (resizing, normalizing, denoising) to create a prepared dataset that the LLM can reliably process. This preliminary processing step transforms the unstructured moving image into a format optimized for LLM analysis, thereby improving response reliability while maintaining the ability to handle diverse content types.
2Loss of information
If the entire moving image is processed frame by frame, then complete information can be extracted, but the computational burden becomes excessive
Solution Approach 1:
The patent applies segmentation by dividing the moving image into discrete representative frames at specific time points rather than processing every frame continuously. The system segments the video stream into key moments based on temporal sampling and event detection, then processes only these segmented frames through the pipeline. This segmentation reduces the total computational load while preserving the essential information content of the moving image.
Solution Approach 2:
The patent extracts only the necessary representative frames from the complete moving image sequence. By using event detection algorithms and temporal sampling, the system identifies and extracts only the critical frames that contain meaningful information, discarding redundant frames. This extraction approach maintains information completeness for the purposes of analysis while significantly reducing computational requirements compared to processing the entire frame sequence.
3Measurement precision
If specialized image processing techniques are used, then processing accuracy is improved for predetermined tasks, but the system cannot adapt to unknown formats and conditions
Solution Approach 1:
The patent implements universality by creating a multi-functional processing pipeline that can handle various unknown content types, formats, and conditions. The system uses general-purpose components including event detection algorithms, temporal sampling mechanisms, and a preprocessed frame output format that works across different scenarios. The LLM component provides universal text generation capability that adapts to any input content. This universal pipeline maintains high processing accuracy across diverse and unknown conditions without requiring task-specific specialized algorithms.
Data Source
AI summary
A method for providing specific information regarding a moving image. The moving image is received. The moving image includes or is defined in terms of a set of consecutive image frames. A set of discrete representative image frames is selected among the set of consecutive image frames. The set of several discrete representative image frames is preprocessed to achieve a set of discrete preprocessed image frames. The set of several discrete preprocessed image frames is analyzed, using digital image and/or audio processing, to achieve metadata regarding the moving image, providing a first prompt to a first large language model (LLM), referencing the set of several discrete preprocessed image frames and the metadata. A first response is received from the first LLM. The first response is used to provide the specific information by querying, inspection or transformation of the first response.