Barcode Scanner Exposure Time Estimation via Combined Decodability
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Solution Overview
Problem
Existing imaging-based barcode scanners face challenges in optimizing exposure time when reading barcodes on moving objects with varying speeds, as current auto-exposure algorithms primarily focus on image contrast and visibility rather than considering both contrast and blur effectively.
Innovation Solution
A method is developed to estimate an optimized exposure time by determining signal-to-noise ratio (SNR) and blur decodability functions, measuring these parameters for an image captured with a first exposure time, and calculating a combined decodability function to determine the optimal exposure time based on both SNR and blur values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If auto-exposure algorithms are designed to enhance image visibility or contrast, then image contrast is improved, but decoding performance on moving objects deteriorates due to insufficient consideration of blur
Solution Approach 1:
The patent changes the parameters considered in auto-exposure algorithms from only contrast/visibility to include both contrast and blur measurements. By introducing blur as an additional parameter and creating a combined decodability function that integrates both contrast and blur metrics, the system optimizes exposure time to simultaneously maintain image contrast and minimize motion blur, thereby improving decoding performance on moving objects
Solution Approach 2:
The patent implements a feedback mechanism where the system measures both contrast and blur from captured images, evaluates a combined decodability function, and adjusts exposure time accordingly. This closed-loop approach continuously optimizes exposure parameters based on actual image quality metrics, allowing the system to adapt to varying object speeds and lighting conditions while maintaining reliable barcode decoding
2Measurement precision
If exposure time is increased to improve signal-to-noise ratio, then signal quality is improved, but image blur increases due to motion during longer exposure
Solution Approach 1:
The patent transforms the exposure time determination from a single-parameter optimization (based only on signal-to-noise ratio) to a multi-parameter optimization that simultaneously considers signal-to-noise ratio and blur. The combined decodability function integrates both metrics, allowing the system to find an optimal exposure time that balances signal quality and image clarity, preventing excessive blur while maintaining adequate signal levels
Solution Approach 2:
The patent introduces dynamic adjustment of exposure time based on real-time measurement of both signal-to-noise ratio and blur characteristics. Rather than using a fixed or single-criterion exposure setting, the system continuously adapts exposure parameters based on the measured trade-off between signal quality and motion blur, optimizing performance for varying object speeds and lighting conditions
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach improves the decoding performance of barcode scanners by adjusting exposure time to account for both signal quality and image clarity, enhancing the ability to read barcodes accurately on moving objects with different speeds.
Implementation Method 1
Imaging systems that include CCD, CMOS, or other imaging configurations comprise a plurality of photosensitive elements (photosensors) or pixels
Implementation Method 2
Light reflected from the target bar code is focused through a lens of the imaging system onto the pixel array
Data Source
AI summary
A method and apparatus for estimating an optimized exposure time for an imaging-based barcode scanner. The method includes (1) determining a SNR decodability function that enables a decodability value be determined from a signal-to-noise ratio; (2) determining a blur decodability function that enables a decodability value be determined from a blur value; (3) measuring a blur and a signal-to-noise ratio for an image captured with a first exposure time; and (4) determining an optimized exposure time based on the measured blur, the measured signal-to-noise ratio, and a combined decodability function that depends upon both the SNR decodability function and the blur decodability function.