Eye-Based Environmental Parameter Detection for Camera Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image capture technologies face challenges in accurately adapting to varying environmental parameters, such as illumination and color temperature, leading to suboptimal image quality, especially when manual settings are complex for users and pre-stored profiles do not match specific environments, and existing automatic detection methods fail without apparent white objects.
Innovation Solution
A method and apparatus that utilize a first camera to obtain eye information, specifically sclera color, to estimate environmental parameters like color temperature and luminance, which are then used to control a second camera's image capture settings, enabling automatic exposure and white balance processing, even when white points are scarce in the scene.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual camera parameter settings are used to adapt to environmental parameters, then image quality can be optimized, but the operation complexity increases and requires user expertise
Solution Approach 1:
The system automatically detects environmental parameters (illumination, color temperature) and adjusts camera settings without user intervention. The camera performs self-calibration by analyzing the captured image to determine optimal exposure time, sensor sensitivity, and white balance, eliminating the need for manual parameter setting while maintaining image quality.
Solution Approach 2:
The system captures a test image, analyzes it to detect environmental parameters, and uses this feedback to adjust camera settings before capturing the final image. This closed-loop feedback mechanism ensures optimal image quality by continuously adapting to environmental conditions without requiring user input.
2Ease of operation
If pre-stored profiles are used for different environments, then operation is simplified, but measurement precision of environmental parameters deteriorates
Solution Approach 1:
Instead of selecting from fixed pre-stored profiles, the system dynamically calculates camera parameters based on real-time environmental detection. The exposure time, sensor sensitivity, and white balance are continuously adjusted according to the detected illumination and color temperature, providing precise adaptation to any environment rather than relying on discrete profiles.
Solution Approach 2:
The system transitions from static pre-stored profiles to dynamic real-time parameter adjustment. By continuously detecting environmental parameters and updating camera settings accordingly, the system adapts to changing conditions with precision, eliminating the mismatch problem inherent in fixed profiles.
3Adaptability or versatility
If automatic detection methods using white points are used, then adaptability is improved, but reliability fails when no white objects are present in the scene
Solution Approach 1:
The system uses a test image capture as an intermediary step between environmental detection and final image capture. This test image serves as a reference to detect environmental parameters, separating the detection function from the final image content, thereby eliminating the requirement for white objects in the final scene.
Solution Approach 2:
The system performs preliminary environmental detection by capturing and analyzing a test image before capturing the final image. This preliminary action establishes the environmental parameters (illumination, color temperature) that will guide the final capture, ensuring reliable adaptation regardless of the final scene content.
4Manufacturing precision
If environmental parameters are continuously detected and adjusted, then image quality is optimized, but processing time and energy consumption increase
Solution Approach 1:
The system performs environmental detection and parameter adjustment periodically - specifically, by capturing a brief test image before the final image rather than continuously during capture. This periodic approach provides sufficient adaptation while minimizing additional time consumption, as the test image capture is a single, brief operation.
Solution Approach 2:
The system uses a partial action approach by capturing only a test image for environmental detection rather than continuously processing the final image. This limited detection action provides sufficient environmental information to optimize the final capture without the excessive time cost of continuous analysis, achieving the right balance between adaptation and efficiency.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Embodiments of the present invention provide a method and apparatus for controlling image capture. There is disclosed a method for detecting an environmental parameter for controlling image capture, the method comprising: obtaining eye information of a user of an electronic device using a first camera on the electronic device; detecting an environmental parameter of an environment where the user is located based on the eye information captured by the first camera; and responsive to a predefined condition being satisfied, controlling image capture by a second camera on the electronic device at least in part based on the detected environmental parameter, the first camera and the second camera being located at different sides of the electronic device. There is also disclosed a corresponding apparatus, an electronic device, and a computer program product.