Optimal Image Resolution Sampling for Analysis
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Solution Overview
Problem
Image analysis is resource-intensive and time-consuming, especially with high-resolution images, leading to high computational costs and long execution times, while data sampling to lower resolutions compromises accuracy.
Innovation Solution
A method that determines an optimal image resolution for sampling based on a learned model, reducing resource consumption and execution time while maintaining accuracy through data sampling and interpolation techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image data is analyzed at high resolution, then measurement precision is improved, but use of energy and computational resources increases
Solution Approach 1:
The patent changes the resolution parameter of image data based on the specific analysis task requirements. Instead of uniformly using high resolution for all images, the system determines the optimal resolution parameter that satisfies accuracy requirements while minimizing computational resource consumption. This is achieved through learned models that predict the appropriate resolution level for different image analysis scenarios.
2Use of energy by moving object
If image data is sampled at lower resolution, then use of energy and computational resources is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-processing image data through learned models that predict the optimal resolution before the actual image analysis task. The system performs resolution determination in advance, transforming the original high-resolution image into an optimally downsampled version that maintains sufficient accuracy for the specific analysis task while reducing computational burden.
3Measurement precision
If high resolution image data is used, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The patent dynamically adjusts the resolution parameter based on task requirements to optimize the balance between accuracy and processing speed. By changing the resolution parameter from its maximum value to an optimal value determined by learned models, the system achieves both high accuracy and fast execution.
4Productivity
If low resolution image data is used, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The patent performs preliminary resolution optimization before the main image analysis task. The learned model predicts the optimal resolution level in advance, ensuring that the image is processed at the highest possible resolution that still achieves the required accuracy, thus maximizing both productivity and measurement precision.
Data Source
AI summary
One embodiment provides a method comprising receiving image data with a first image resolution, and determining an optimal image resolution for sampling the image data based on a learned model. The optimal image resolution is lower than the first image resolution. The method further comprises sampling the image data at the optimal image resolution, and performing image analysis on sampled image data resulting from the sampling.