AI-Based Content Isolation for Network Transmission Capacity
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Solution Overview
Problem
Current data transmission methods are limited by bandwidth constraints, which hinder the efficient transmission of high-resolution data, particularly in visual content like images and videos, leading to reduced network capacity and quality.
Innovation Solution
The implementation of a computer-implemented method using trained artificial intelligence algorithms, such as convolutional neural networks, to identify and isolate representative content from background data, reducing the data volume transmitted by removing unnecessary background information and encoding only the subject matter, thereby increasing channel capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data transmission methods are used to transmit high-resolution visual content, then data resolution quality can be maintained, but network transmission capacity is limited due to bandwidth constraints
Solution Approach 1:
The patent extracts and removes background content from visual data, transmitting only the subject matter. This is achieved through background subtraction techniques that identify and separate foreground objects from background scenes, reducing the data volume that needs to be transmitted while preserving the essential visual information.
Solution Approach 2:
The patent segments visual content into distinct components: foreground subjects and background elements. By dividing the visual data into these separate segments and selectively transmitting only the foreground portions, the system reduces overall data transmission requirements while maintaining image quality for the important elements.
2Loss of information
If all original content data including background is transmitted, then complete visual information is preserved, but data volume increases reducing channel capacity
Solution Approach 1:
The system extracts only the essential visual information (foreground subjects) while discarding redundant background data. This extraction process uses image processing algorithms to identify and isolate meaningful content, transmitting minimal data while avoiding information loss for the important visual elements.
Solution Approach 2:
The patent applies different quality levels to different parts of the visual content. High-resolution data is transmitted for foreground subjects where detail is important, while background areas are either compressed more aggressively or excluded entirely, optimizing the balance between information preservation and data volume reduction.
Data Source
AI summary
In some embodiments, the present invention provides for an exemplary inventive system, including: a communication pipeline, including: at a first end of the communication pipeline: a first processor configured to: obtain a plurality of original content data units having a representative content associated with a subject; apply a trained artificial intelligence algorithm to identify: the representative content of the subject and original background content that is not associated with the subject; remove the original background content to reduce a volume of data being transmitted resulting in an increased capacity of the communication channel; encode and transmit each respective modified content data unit from the first end of the communication pipeline to a second end; a second processor configured to: receive and decode each respective modified content data unit; generate a respective artificial background content; and combine the representative content associated with the subject and the respective artificial background content to form each composite content data unit.