Camera Contrast Detection Evaluation Using CSNR Metrics
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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 and adapt to changes over time.
Innovation Solution
The introduction of the contrast signal to noise ratio (CSNR) metric, which allows for the evaluation of camera detection capabilities by calculating the mean contrast divided by its standard deviation, enabling real-time analysis and adaptation of camera settings in various lighting and environmental conditions.
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 transforms the complex traditional contrast detection parameters into a simplified CSNR metric that combines contrast and signal-to-noise ratio into a single computational parameter. This parameter transformation enables real-time calculation from standard image data without requiring specialized measurement equipment or procedures, thus reducing measurement time while maintaining detection precision.
Solution Approach 2:
The patent replaces physical measurement equipment and laboratory-based mechanical systems with computational image analysis. By using software-based CSNR calculation on captured images, the system eliminates the need for specialized hardware measurement devices, reducing both time and complexity while preserving measurement accuracy.
2Measurement precision
If traditional contrast detection measurement methods are used, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent creates a universal CSNR metric that can be applied across different camera systems, lighting conditions, and image types using the same calculation methodology. This multi-functional approach allows a single simple algorithm to replace multiple specialized measurement devices and procedures, reducing system complexity while maintaining precision across diverse applications.
Solution Approach 2:
The patent substitutes complex physical measurement systems with a computational algorithm that can be implemented in software. The CSNR metric is calculated using standard image processing techniques on existing image data, eliminating the need for specialized measurement hardware, laboratories, or complex experimental setups.
3Adaptability or versatility
If real-time camera performance evaluation is implemented, then adaptability is improved, but computational resources required increase
Solution Approach 1:
The patent calculates CSNR metrics selectively based on the specific evaluation needs rather than performing exhaustive analysis on all image data. By computing the metric only on relevant image regions or at appropriate intervals, the system achieves real-time adaptability while minimizing unnecessary computational energy consumption.
Solution Approach 2:
The patent transforms complex multi-parameter camera performance evaluation into a single CSNR parameter that can be computed efficiently. This parameter consolidation reduces the computational burden while maintaining the ability to assess and adapt camera performance in real-time across varying conditions.
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.