Camera Exposure Control Using Object Distance Analysis
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Solution Overview
Problem
Mobile cellular telephone cameras struggle to adapt exposure settings for varying background light intensities, leading to overexposed or underexposed images that can impair texture detail, which is critical for machine-based image recognition.
Innovation Solution
A method and apparatus that use a processor to receive images from an image sensor, determine the distance of objects, and control exposure settings by dividing the image into a grid to count overexposed and underexposed pixels, adjusting exposure values based on thresholds and look-up tables to compensate for exposure imbalances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the camera uses fixed exposure settings, then the device complexity is reduced, but the image quality deteriorates under varying light conditions
Solution Approach 1:
The patent implements dynamic exposure control by continuously adjusting exposure settings based on real-time analysis of image content. The system evaluates pixel intensity distributions, detects overexposed and underexposed regions, and modifies exposure parameters accordingly, transforming the static exposure mechanism into a dynamic adaptive system that responds to varying lighting conditions and subject characteristics.
Solution Approach 2:
The patent employs feedback mechanisms where the captured image is analyzed to determine exposure quality metrics. The system calculates the distribution of pixel intensities, identifies overexposed and underexposed regions, and uses this information to adjust subsequent exposure settings. This closed-loop feedback ensures continuous optimization of image quality while maintaining manageable device complexity through algorithmic control.
2Manufacturing precision
If the camera adapts exposure for each scene, then image quality is improved, but the processing time increases
Solution Approach 1:
The patent applies partial action by focusing computational resources on critical aspects of exposure optimization rather than analyzing every pixel in detail. The system samples pixel intensity distributions from representative regions of the image, identifies key overexposed and underexposed areas, and makes exposure adjustments based on this partial analysis. This approach achieves satisfactory image quality improvement while significantly reducing processing time compared to exhaustive pixel-by-pixel analysis.
3Illumination intensity
If the camera increases exposure time for low light, then image brightness is improved, but motion blur increases
Solution Approach 1:
The patent utilizes parameter changes by dynamically adjusting multiple exposure parameters including exposure time, aperture size, and ISO sensitivity based on scene analysis. Rather than simply increasing exposure time for low-light conditions, the system evaluates the scene content, subject distance, and motion characteristics to select optimal combinations of exposure parameters. This multi-parameter adjustment strategy achieves improved image brightness while minimizing motion blur through coordinated parameter optimization.
Data Source
AI summary
A method including receiving an image from an image sensor, the image including a portion corresponding to an object; determining distance of the object from the image sensor using the received image; and controlling exposure of the image sensor using the determined distance.