Camera Contrast Evaluation Using CSNR for Real-Time Detection
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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 assessment in field conditions, especially for high dynamic range images and machine vision tasks.
Innovation Solution
The introduction of the contrast signal-to-noise ratio (CSNR) metric, which is proportional to SNRI, does not saturate and is simpler to calculate, allowing for real-time analysis of camera data and automatic adjustment of camera parameters, enabling effective contrast detection across various lighting and environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If SNRI (Signal-to-Noise Ratio Idealized) is used to evaluate contrast detection capabilities, then measurement accuracy is improved, but calculation complexity and time consumption increase significantly
Solution Approach 1:
The patent extracts the essential components needed for contrast detection evaluation from the complex SNRI calculation. Instead of computing the full SNRI metric which requires multiple complex operations, the invention identifies and computes only the critical contrast-related components directly from image data, thereby maintaining measurement accuracy while dramatically reducing calculation complexity and enabling real-time evaluation.
2Measurement precision
If traditional contrast detection methods are used, then measurement accuracy is maintained, but real-time assessment in field conditions becomes impossible
Solution Approach 1:
The patent segments the contrast detection evaluation process into independent, computationally efficient steps that can be executed in real-time. By breaking down the evaluation into discrete operations on image patches and pixel pairs, the method enables field deployment and real-time assessment while preserving measurement accuracy through systematic processing of image data.
3Device complexity
If CDP (Contrast Detection Probability) is used, then calculation simplicity is improved, but measurement accuracy and applicability to high dynamic range images deteriorate
Solution Approach 1:
The patent changes the evaluation parameters and methodology to overcome CDP's limitations. Instead of using CDP's probability-based approach which saturates and lacks accuracy, the invention employs direct contrast calculation from pixel pairs with proper normalization, adjusting the measurement parameters to handle high dynamic range images effectively while maintaining computational efficiency and real-time capability.
Data Source
AI summary
In one embodiment, a system determines 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 of the image, 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, where a contrast is calculated between the first pixel and the second pixel. 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.