Image Processing Device Gamma Correction for Vehicle Cameras
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Solution Overview
Problem
Existing image processing techniques for liquid crystal display devices, particularly in vehicle-mounted camera systems, suffer from unnatural image generation due to sudden changes in gamma correction values between local areas with different characteristics, leading to degraded image quality and visibility issues that can obstruct safe driving.
Innovation Solution
An image processing device that calculates modulation gain values for both global and local areas, creates a correction intensity map, and applies these values to the input image, ensuring smooth transitions and preventing unnatural brightness reversals or overexposure/blackout between adjacent areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If gamma correction is performed for each local area unit to improve visibility, then brightness contrast is improved, but unnatural images with brightness reversal occur at local area boundaries
Solution Approach 1:
The patent applies different gamma correction strategies to different regions: strong correction in local areas with large luminance differences, and weak or no correction in local areas with similar characteristics to adjacent regions. This prevents brightness reversal at boundaries while maintaining visibility improvement where needed.
Solution Approach 2:
The patent dynamically adjusts the gamma correction parameter (correction coefficient) based on the luminance characteristics of each local area. By changing this parameter according to local conditions, the system achieves both visibility improvement and natural transition at boundaries.
2Manufacturing precision
If linear interpolation is used to smooth gamma correction values at local area boundaries, then brightness reversal is prevented, but visibility improvement is reduced
Solution Approach 1:
The patent applies different gamma correction strategies to different regions: strong correction in local areas with large luminance differences, and weak or no correction in local areas with similar characteristics to adjacent regions. This prevents brightness reversal at boundaries while maintaining visibility improvement where needed.
Solution Approach 2:
The patent dynamically adjusts the gamma correction parameter (correction coefficient) based on the luminance characteristics of each local area. By changing this parameter according to local conditions, the system achieves both visibility improvement and natural transition at boundaries.
3Illumination intensity
If strong gamma correction is applied to all local areas, then visibility is improved, but level difference noise increases in the entire image
Solution Approach 1:
The patent applies different gamma correction strategies to different regions: strong correction in local areas with large luminance differences, and weak or no correction in local areas with similar characteristics to adjacent regions. This prevents brightness reversal at boundaries while maintaining visibility improvement where needed.
Solution Approach 2:
The patent dynamically adjusts the gamma correction parameter (correction coefficient) based on the luminance characteristics of each local area. By changing this parameter according to local conditions, the system achieves both visibility improvement and natural transition at boundaries.
Data Source
AI summary
The image processing device acquires feature quantities (maximum value, minimum value, average value, histogram, etc.) of the entire area (GA) of the image and feature quantities of each local area (LA) of the image from the input image, and calculates a plurality of modulation gain values (gamma correction curves) for GA and each LA. Furthermore, the image processing device determines the correction intensity for each LA from the feature quantity of the GA and the feature quantity of each LA, and creates the LA correction intensity map. Finally, the image processing device finally applies the result of combining a plurality of modulation gain values based on the LA correction intensity map to the input image.


