Camera Contrast Detection Assessment Using CSNR
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for evaluating the contrast detection capabilities of cameras are time-consuming, complex, and limited to laboratory settings, making it difficult to assess camera performance in real-world conditions.
Innovation Solution
The introduction of the contrast signal to noise ratio (CSNR) metric, which allows for the evaluation of camera contrast detection capabilities in real-time using captured images, enabling both laboratory and field assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional contrast detection measurement methods are used, then measurement precision is improved, but measurement time and system complexity increase significantly
Solution Approach 1:
The patent extracts the essential contrast detection measurement from complex laboratory setups and implements it through a simplified CSNR metric that can be calculated directly from captured images. This extraction allows the measurement to be performed quickly using standard camera operations without requiring specialized measurement equipment or complex procedures.
Solution Approach 2:
The patent replaces mechanical measurement systems and complex laboratory apparatus with a computational approach. The CSNR metric is calculated through image processing algorithms that analyze captured images, substituting physical measurement devices with software-based computation to achieve fast, accurate contrast detection capability assessment.
2Measurement precision
If traditional contrast detection measurement methods are used, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts the core measurement function from complex measurement systems and implements it through a simple CSNR calculation. By taking out only the essential contrast detection requirement and implementing it through a computational metric, the system achieves accurate measurement without requiring complex measurement devices or procedures.
Solution Approach 2:
The patent replaces mechanical measurement systems with a computational approach. The CSNR metric is derived through image processing algorithms that operate on captured images, substituting physical measurement apparatus with software-based computation to reduce device complexity while maintaining measurement precision.
3Measurement precision
If traditional contrast detection measurement methods are used, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent implements self-service by enabling the camera system to automatically perform contrast detection capability assessment through the CSNR metric. The system uses its own captured images to evaluate its performance, eliminating the need for external measurement equipment or complex operational procedures. This self-service approach maintains measurement precision while dramatically improving ease of operation.
4Productivity
If real-time contrast detection assessment is implemented, then productivity is improved, but computational resources required increase
Solution Approach 1:
The patent applies partial action by calculating the CSNR metric using a representative subset of image data rather than processing the entire image set. This approach enables real-time contrast detection assessment with reduced computational resource consumption, achieving a balance between productivity improvement and energy efficiency.
Data Source
AI summary
In one embodiment, a system receives pixel data from a pair of regions of an image generated by an imaging device, the pair of regions includes a first region and a second region, where the first region includes a first plurality of pixels and the second region includes a second plurality of pixels. The system determines a plurality of pixel pairs, where a pixel pair includes a first pixel from the first plurality of pixels and a second pixel from the second plurality of pixels. The system calculates a plurality of contrasts based on the plurality of pixel pairs. The system determines a contrast distribution based on the plurality of contrasts. The system calculates a value representative of a capability of the imaging device to detect contrast based on the contrast distribution. The system determines a reduction in contrast detectability of the imaging device based on the value.