Vehicle Image Data Segmentation for Storage Optimization
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Solution Overview
Problem
The challenge is to manage and process vehicle-related image data efficiently, as current systems face issues with large file sizes, resource-intensive processing, storage, and transmission.
Innovation Solution
The method involves segmenting image data into regions with different pixel densities, generating analysis data by executing image analysis models on these segmented data, and outputting the processed image data with reduced pixel density for optimized storage and transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image data is processed and stored at high pixel density, then image quality and analysis accuracy are improved, but file size and processing resources increase significantly
Solution Approach 1:
The image data is divided into multiple regions of interest (ROIs), each processed and stored at different pixel densities. Critical regions maintaining high resolution for accurate analysis while less critical regions use lower resolution, thereby reducing overall file size while preserving analysis accuracy where needed
Solution Approach 2:
Different regions of the image are assigned different quality levels (pixel densities) based on their importance for analysis. High-value regions receive high pixel density for accurate detection, while low-value regions use lower pixel density, optimizing the balance between analysis accuracy and storage efficiency
2Measurement precision
If image data is processed and transmitted at high pixel density, then analysis accuracy is improved, but processing time and transmission resources increase
Solution Approach 1:
The image processing pipeline segments the image into multiple ROIs and processes each region independently at appropriate pixel densities. This parallel processing approach reduces overall processing time while maintaining analysis accuracy for critical regions
Solution Approach 2:
Full high-resolution processing is applied only to critical regions where analysis accuracy is essential, while other regions receive reduced processing. This partial application of high-resolution processing optimizes the balance between analysis accuracy and processing efficiency
Data Source
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Figure 1B
Figure 2A~2C
AI summary
Systems, devices, and methods for segmenting image data and utilizing image data having different pixel densities are described. One or more image capture devices can capture vehicle-related image data, which can be segmented such that regions of the image data which are directed to important content and/or far away content can have higher pixel density than regions of the image data directed to less important content and/or content close the one or more image capture devices. Image analysis is performed on the segmented image data. Output image data is generated of a lower pixel density to reduce storage and/or transmission requirements.