Camera Contrast Detection Using CSNR for Real-Time Assessment
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Solution Overview
Problem
Existing methods for evaluating the contrast detection capabilities of imaging systems, such as cameras, are either too time-consuming and resource-intensive (like SNRI) or limited in accuracy and applicability (like CDP), making them unsuitable for real-time or field-based assessments, especially for machine vision tasks like autonomous driving.
Innovation Solution
The introduction of the contrast signal-to-noise ratio (CSNR) metric, which is proportional to SNRI, simple to calculate, and non-saturating, allowing for real-time evaluation of camera performance across various lighting conditions, enabling automatic adjustment of camera parameters for improved functionality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If SNRI (signal-to-noise ratio idealized) is used to evaluate camera contrast detection capabilities, then measurement accuracy is improved, but measurement time and computational complexity increase significantly
Solution Approach 1:
The patent extracts the essential measurement functionality from the complex SNRI calculation by identifying and measuring only the critical components needed for contrast detection capability assessment. Instead of performing the full SNRI calculation, the system extracts and measures contrast detection probability (CDP) and contrast signal-to-noise ratio (CSNR) as simplified surrogate metrics that capture the essential measurement intent without the computational overhead.
Solution Approach 2:
The patent creates simplified proxy measurements (CDP and CSNR) that replicate the essential information obtained from full SNRI measurements. These proxy metrics serve as lightweight copies that provide sufficient measurement accuracy for practical applications while requiring minimal computational resources and time.
2Measurement precision
If traditional contrast detection measurement methods are used, then measurement accuracy is maintained, but device complexity and resource requirements increase
Solution Approach 1:
The patent employs simple, easily computable metrics (CDP and CSNR) that can be calculated rapidly from standard camera output data. These metrics serve as disposable, lightweight measurement tools that provide sufficient accuracy for practical purposes without requiring complex measurement systems or extensive computational resources.
3Measurement precision
If comprehensive contrast detection measurements are performed in laboratory conditions, then measurement accuracy is improved, but adaptability to real-world field conditions decreases
Solution Approach 1:
The patent develops measurement metrics (CDP and CSNR) that are universally applicable across different camera systems and operating conditions. These metrics can be calculated from standard camera output data regardless of whether the camera is operating in controlled laboratory environments or varied field conditions, making the measurement system highly adaptable and versatile.
Solution Approach 2:
The measurement system uses the camera's own output data to perform self-assessment of its contrast detection capabilities. By analyzing the camera's captured images and computing CDP/CSNR from the actual output, the system enables real-world field cameras to automatically evaluate their own performance without requiring external measurement equipment or controlled laboratory 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.