Adaptive Light Source Thresholding for Tone-Mapped Images
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Solution Overview
Problem
Existing image processing systems inaccurately detect vehicle lights due to tone mapping operations, leading to false positives from non-light sources and missed detections of distant lights, especially in compressed 8-bit images, which lose scene brightness information.
Innovation Solution
A non-linear image transformation method using a gamma correction technique with adjustable coefficients (γ and β) and region-of-interest analysis to apply dynamic thresholds, based on real-world brightness and pixel neighborhood information, enabling accurate light source identification in tone-mapped images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If tone mapping compression is applied to reduce dynamic range, then computational resources are saved and image storage is reduced, but scene brightness information is lost causing false positives in light source detection
Solution Approach 1:
The patent introduces an intermediary gamma correction transformation that maps the compressed 8-bit pixel values back to an approximate real-world brightness space. This intermediary step allows the system to work with compressed images while recovering brightness information needed for accurate light source detection, without requiring access to the original uncompressed image data.
Solution Approach 2:
The patent transforms the detection parameters by applying a non-linear gamma correction function to the pixel intensity values. This parameter transformation changes the brightness distribution characteristics of the compressed image, making distant light sources and non-luminant objects distinguishable through their different brightness patterns, thereby resolving the detection accuracy issue.
2Device complexity
If a global light source detection threshold is applied, then processing is simplified, but distant light sources are missed while nearby non-light sources are falsely detected
Solution Approach 1:
The patent applies local adaptive thresholding where the detection threshold varies spatially across the image based on local brightness characteristics. By calculating thresholds for different regions independently, the system adapts to local conditions - allowing detection of faint distant light sources in dark regions while preventing false positives from bright non-luminant objects in well-lit areas.
Solution Approach 2:
The patent makes the detection threshold dynamic rather than static. The threshold adapts to local image conditions by considering the brightness distribution in neighborhoods around each pixel. This dynamic adjustment allows the same algorithm to handle both distant light sources and nearby non-light sources correctly, resolving the contradiction between simplicity and accuracy.
3Measurement precision
If non-linear gamma correction transformation is applied to recover brightness information, then light source detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent performs gamma correction transformation as a preliminary step before light source detection. By pre-processing the compressed image with the inverse gamma transformation, the system recovers brightness information in advance, making subsequent detection simpler and more accurate. This preliminary action separates the complex transformation from the detection logic, improving overall system efficiency.
Data Source
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AI summary
A method of identifying a light source in a tone mapped image is based on comparison of pixel values with thresholds. A non-linear transformation is used to determine light source identification thresholds for each pixel position of the image. The non-linear transformation is such that light source detection is performed in a colour space approximating that of the real world, rather than that colour space of the tone mapped image.