Ambient Light Estimation Using IR Reflectivity Corrections
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Solution Overview
Problem
Conventional cameras with infrared capabilities face challenges in accurately switching between day and night modes due to limitations in ambient light estimation, particularly in environments with varying infrared reflectivity of materials, which can lead to suboptimal image quality and inefficient power consumption.
Innovation Solution
A system that uses image processing techniques to estimate ambient light levels by analyzing RGB and IR channel data, employing scene segmentation and IR reflectivity corrections to determine the appropriate camera mode, thereby optimizing image capture settings without relying on dedicated hardware sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional cameras use simple ambient light sensing to determine day/night mode, then the device complexity is reduced, but the measurement precision of ambient light estimation deteriorates due to varying infrared reflectivity of materials
Solution Approach 1:
The patent segments the captured image into multiple regions corresponding to different materials (e.g., skin, clothing, background objects). Each region is analyzed separately to determine its infrared reflectivity characteristics, allowing the system to account for varying reflectivity across different materials in the scene without requiring complex dedicated hardware sensors
Solution Approach 2:
The patent changes the parameter being measured from raw infrared intensity to corrected ambient light estimation by applying reflectivity compensation. The system captures infrared channel data, applies material-specific reflectivity corrections to each segmented region, and aggregates these corrections to produce an accurate ambient light estimation that accounts for varying infrared reflectivity across different materials
2Measurement precision
If the camera uses accurate ambient light estimation with IR reflectivity corrections, then the measurement precision improves, but the device complexity and processing requirements increase
Solution Approach 1:
The patent makes the image sensor perform multiple functions: it captures both visible light and infrared channel data for the same scene. This multi-functionality allows the system to use the existing image sensor data for both image capture and ambient light estimation, eliminating the need for separate dedicated hardware sensors while achieving high measurement precision through software-based IR reflectivity corrections
Solution Approach 2:
The patent creates a computational model that copies the physical scene into segmented regions with assigned material properties. By representing the scene as segmented regions with known or estimated infrared reflectivity characteristics, the system can apply corrections to the infrared channel data to estimate ambient light accurately without requiring physical hardware changes
3Reliability
If the camera operates in night mode with IR illumination, then the image quality is improved in low light conditions, but the power consumption increases due to continuous IR LED operation
Solution Approach 1:
The patent implements a feedback mechanism where the estimated ambient light level (corrected for IR reflectivity) continuously informs the decision to switch between day and night modes. The system monitors the corrected ambient light estimation and adjusts the IR LED operation accordingly, turning off IR illumination when sufficient ambient light is detected and enabling it when ambient light falls below the threshold, thereby optimizing power consumption while maintaining image quality
Solution Approach 2:
The patent makes the camera mode dynamic rather than static. The system continuously evaluates the corrected ambient light estimation and dynamically switches between day mode (IR cut filter in place, IR LEDs off) and night mode (IR cut filter removed, IR LEDs on) based on current lighting conditions. This dynamic adaptation allows the camera to operate in the most efficient mode for each situation, improving overall power efficiency while maintaining reliable image quality
Data Source
AI summary
Devices and techniques are generally described for estimation of ambient light levels. In various examples, an image sensor may capture a frame of image data. In some examples, an ambient light value of the frame may be determined. In some examples, a first region of the frame corresponding to a first physical characteristic and a second region of the frame corresponding to a second physical characteristic may be determined. In various examples, a first reflection coefficient associated with the first physical characteristic and a second reflection coefficient associated with the second physical characteristic may be determined. In some examples, an IR correction value may be determined for the frame of image data based at least in part on the first reflection coefficient and the second reflection coefficient. An estimated ambient light value may be determined based at least in part on the IR correction value and the ambient light value.