Image Embedding Compression for ML Analytics
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Solution Overview
Problem
Existing video analytics systems face challenges in efficiently processing and analyzing large volumes of image data due to resource constraints, with traditional compression methods prioritizing visual quality over data utility for machine learning models.
Innovation Solution
The use of embeddings to represent images, where a machine learning-based encoder model is trained to maximize the data utility of its output embeddings, allowing for optimal compression and reduced resource consumption without the need for decompression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional compression standards (JPEG, PNG, TIFF) are used to reduce image size, then file size and storage resources are reduced, but important information for machine learning analysis is lost
Solution Approach 1:
The patent changes the fundamental parameter of image representation from pixel-based compression to embedding-based compression. Instead of compressing images to maintain visual quality, the system transforms images into embedded representations that prioritize data utility for machine learning tasks. This parameter change allows the system to reduce file size while preserving or enhancing the information needed for analytics.
Solution Approach 2:
The patent creates a compressed copy of the image in the form of an embedding that captures the essential information for machine learning analysis. This embedding copy is then used for analytics purposes, eliminating the need to decompress and process the full original image, thereby reducing computational resources while maintaining analytical accuracy.
2Reliability
If images are analyzed in the cloud to improve processing capabilities, then analysis accuracy is improved, but network bandwidth consumption increases
Solution Approach 1:
The patent extracts the essential information from the original image and transforms it into a compact embedding representation. This extracted embedding contains the critical data needed for machine learning analysis, allowing the system to send only the essential information to the cloud for analysis rather than transmitting the entire image, thereby significantly reducing network bandwidth consumption.
Solution Approach 2:
The patent segments the image data into an embedding representation that separates the essential analytical information from the redundant visual data. This segmentation allows the system to transmit only the necessary embedded features to the cloud for analysis, reducing the amount of data transferred over the network while maintaining analysis capabilities.
3Quantity of substance
If image compression is applied to reduce data size, then storage and network resources are reduced, but visual quality is compromised
Solution Approach 1:
The patent inverts the traditional compression approach by not focusing on maintaining visual quality but rather on preserving data utility for machine learning. Instead of compressing images to look as similar as possible to the original, the system compresses images to maximize the information content relevant for analytics, achieving efficient data representation without caring about visual fidelity.
Data Source
AI summary
In one implementation, a device receives, via a user interface, one or more parameters regarding formation of an embedding of an image. The device forms, in accordance with the one or more parameters, an embedding of the image by inputting it to a machine learning-based encoder model that was trained to maximize a measure of data utility of its output embeddings. The device provides the embedding for use to train an analytics model. The device causes the analytics model to be used to make inferences about embeddings derived from images captured by one or more cameras.