Image Quality Detection System Using Histogram Compression
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Solution Overview
Problem
Current imaging technologies face challenges in capturing consistent images due to varying lighting environments and backgrounds, requiring high training and skill to ensure proper exposure, saturation, focus, and color, which is time-consuming and expensive.
Innovation Solution
An image quality system that uses an image-capturing device to test focus through blur tests, evaluate histograms for saturation and underexposure, and apply adjustment curves to compress the histogram, ensuring consistent exposure and color matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If automated image quality detection and correction is implemented, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The system performs self-diagnosis and self-correction by automatically detecting image quality parameters (focus, exposure, saturation) and applying corrections without user intervention. The processor autonomously evaluates captured images against quality criteria and retakes or adjusts images that fail to meet standards.
Solution Approach 2:
The system dynamically adjusts image capture parameters such as exposure time, gain, and focus based on real-time quality assessment. By changing these parameters automatically, the system maintains image quality across varying lighting conditions without requiring user expertise.
2Manufacturing precision
If multiple image quality tests are performed, then manufacturing precision is improved, but productivity decreases
Solution Approach 1:
The system performs quality tests immediately after image capture before the image is finalized or stored. By conducting focus, exposure, and saturation tests as preliminary checks, the system ensures quality standards are met before proceeding to color matching and final processing.
Solution Approach 2:
The system implements a feedback loop where test results from focus, exposure, and saturation analysis are immediately used to determine whether to retake the image or proceed to correction. This closed-loop approach ensures high image quality consistency while minimizing the number of retakes needed.
3Reliability
If automated retake notification is implemented, then reliability is improved, but loss of time increases
Solution Approach 1:
The system rapidly evaluates multiple quality parameters (focus, exposure, saturation) in quick succession and immediately notifies users of failures. By skipping unnecessary delays and performing parallel assessments, the system maintains high reliability while minimizing the time required for quality verification.
Data Source
AI summary
A method and instructions for operating an image quality system can comprise: obtaining an original image; sensing a subsequent image with an image-capturing device, comprising: testing a focus of the subsequent image with the image-capturing device implementing a blur test, informing a user to retake the subsequent image based on the subsequent image failing the blur test, evaluating a histogram for the subsequent image, informing the user to retake the subsequent image based on the histogram including a value exceeding a saturation threshold, and informing the user to retake the subsequent image based on the histogram including the value exceeding an underexposure threshold; compressing the histogram with an adjustment curve; and color matching the subsequent image to the original image.