Multi-Target Automatic Exposure Control via Pixel Intensity Segmentation
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Solution Overview
Problem
Conventional CMOS image sensors often produce images with exposure issues due to saturation and blooming, and traditional automatic exposure control methods using a single target mean intensity can result in too many bright or dark pixels, degrading image quality.
Innovation Solution
Implementing multi-target automatic exposure and gain control based on pixel intensity distribution, where the image is segmented into regions with associated weight factors, and multiple target mean intensity values are dynamically selected to adjust exposure and gain, ensuring optimal image brightness and reducing saturated or dark pixels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional automatic exposure control uses a single target mean intensity, then the exposure control process is simple, but the image quality degrades with too many bright or dark pixels
Solution Approach 1:
The patent segments the image into multiple regions (e.g., bright regions, dark regions, mid-tone regions) and calculates separate mean intensity values for each region. This allows different target intensities to be applied to different regions, improving overall image quality by preventing excessive bright or dark pixels while maintaining reasonable process complexity through automated region classification.
Solution Approach 2:
The patent dynamically selects target mean intensity values based on the actual image content and region characteristics. Instead of using a fixed single target intensity, the system adapts the target intensity for each region based on its intensity distribution, thereby improving image quality while the automation keeps the process complexity manageable.
2Speed
If a single window of part of image is used for calculating mean intensity, then the calculation is fast, but the target intensity estimation becomes less accurate due to different intensity distributions in different parts of the image
Solution Approach 1:
The patent divides the image into multiple regions and calculates mean intensity for each segment rather than using a single window. This segmentation approach captures the intensity distribution characteristics of different parts of the image, improving target intensity estimation accuracy while maintaining calculation efficiency through localized processing of smaller regions.
Solution Approach 2:
The patent applies different processing and target intensity values to different regions of the image based on their local characteristics. By recognizing that different parts of the image have different intensity distributions, the system tailors the exposure control to each region's specific needs, thereby improving overall estimation accuracy without significantly increasing computational burden.
3Adaptability or versatility
If post image-acquisition algorithms are applied to correct exposure, then exposure adjustments can be made, but the overall image quality degrades due to multiple manipulations
Solution Approach 1:
The patent performs exposure and gain control during the image acquisition process itself by dynamically adjusting the target mean intensity based on region analysis, rather than applying corrective algorithms after image capture. This preliminary action prevents the need for subsequent post-processing manipulations, thereby maintaining image quality while providing adaptive exposure adjustment capability.
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 results in improved image quality by accurately adjusting exposure and gain, minimizing the number of bright and dark pixels, and providing a more accurate estimation of target intensity, leading to better image rendering with reduced post-processing degradation.
Implementation Method 1
each pixel includes a photodiode that transforms incident light into an electrical charge
Data Source
AI summary
An example method of multi-target automatic exposure and gain control based on pixel intensity distribution includes capturing a series of digital images with an image sensor. As the series of digital images are captured, exposure time and/or gain are adjusted to adjust a mean intensity value of the digital images until a target mean intensity value is reached. The method includes dynamically selecting the target mean intensity value from a plurality of target mean intensity values based on a relative number of pixels, in each captured digital image, that have an intensity value that falls outside a range of intensity values.