AI Event Metadata Transmission for Low-Bandwidth Multimedia
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Solution Overview
Problem
Existing data transmission methods face challenges in efficiently transmitting large volumes of data, particularly video data, in low-bandwidth environments, leading to delays, network congestion, and inefficient resource allocation, especially in security and healthcare sectors, due to limited scalability, lack of customization, and inability to prioritize incident transmission based on severity and location.
Innovation Solution
A method and system utilizing AI models to generate event metadata from multimedia content, including audio or video streams, which includes frames, timestamps, and key information, and reconstruct relevant segments using image diffusion models, reducing the bandwidth required for transmission while maintaining quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If video data is transmitted over networks, then data transmission occurs, but bandwidth consumption increases and transmission delays occur
Solution Approach 1:
The system extracts only the essential event metadata (timestamps, key information, selected frames) from the complete video data, transmitting only this extracted information over the network. This extraction principle reduces bandwidth consumption while maintaining the ability to reconstruct and deliver relevant event segments efficiently.
Solution Approach 2:
The video data is segmented into discrete events with defined start and end timestamps. Each event is processed and transmitted as a separate unit with its metadata, allowing selective transmission of only relevant segments rather than continuous video streams, thereby reducing overall bandwidth consumption.
2Quantity of substance
If compression techniques are applied to reduce bandwidth, then bandwidth requirements decrease, but transmission quality and detail are lost
Solution Approach 1:
Instead of compressing the entire video stream, the system creates precise copies of only the relevant event segments using timestamp boundaries. These copied segments are transmitted in their original quality without lossy compression, while the copying process itself is highly efficient in terms of bandwidth usage.
Solution Approach 2:
The system performs preliminary identification and marking of event segments before transmission. By pre-identifying start and end timestamps and extracting only the necessary frames and metadata in advance, the system prepares optimized transmission packages that maintain quality while minimizing bandwidth requirements.
3Loss of information
If all video data is transmitted, then complete information is available, but network congestion and delays increase
Solution Approach 1:
The system extracts and transmits only the essential components needed to reconstruct event segments - specifically the metadata (timestamps, key information) and selected frames - rather than transmitting complete video data. This extraction eliminates unnecessary data transmission while preserving all information needed for event reconstruction.
Solution Approach 2:
Video data is divided into discrete event segments with clear boundaries. Each segment is independently processed and transmitted with its metadata, allowing receivers to reconstruct only the necessary portions without waiting for or processing entire video streams, thereby reducing transmission delays.
4Adaptability or versatility
If conventional transmission methods are used, then simplicity is maintained, but scalability and customization are limited
Solution Approach 1:
The system dynamically adjusts event detection parameters, metadata generation, and frame selection based on varying requirements. The AI model can adapt to different event types, severity levels, and transmission conditions, enabling scalable deployment across diverse scenarios while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The system allows modification of key parameters such as event detection thresholds, metadata inclusion criteria, and frame selection parameters. These parameter changes enable customization for different applications (security, healthcare, etc.) and scalable deployment without fundamentally changing the system architecture.
Data Source
AI summary
The disclosure relates to a method and a system for optimizing transmission of user relevant events. The method includes generating an event metadata for a user relevant event from an event snippet of the user relevant event. The event snippet is obtained from a multimedia content. The multimedia content includes at least one of an audio stream or a video stream. The method further includes transmitting the event metadata associated with the user relevant event. The event metadata includes a set of frames associated with the user relevant event, a start timestamp and an end time stamp associated with the user relevant event, and key information associated with the user relevant event. The method further includes reconstructing a segment of the multimedia content associated with the user relevant event based on the event metadata.