Camera Exposure Control via Feature Brightness Detection
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Solution Overview
Problem
Existing electronic devices with cameras face challenges in maintaining optimal exposure control across varying environmental conditions, leading to issues like supersaturation or unclear images due to inappropriate exposure settings.
Innovation Solution
An electronic device with a camera and processor system that generates input images, detects features and texture information, and adjusts exposure parameters based on brightness, texture, and dynamic range calculations to optimize image capture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If auto exposure control is performed based on imaging environment, then exposure adjustment is automated, but image quality may deteriorate when exposure control is not appropriate (supersaturation or low brightness)
Solution Approach 1:
The patent implements a feedback mechanism where the processor analyzes the captured image to detect features and determines whether to adjust the exposure parameter based on the detected feature brightness. This closed-loop feedback allows the system to automatically correct exposure issues by comparing the captured image characteristics against predetermined thresholds, thereby improving image quality while maintaining automation.
Solution Approach 2:
The patent dynamically changes the exposure parameter (first parameter) based on the brightness of detected features in the captured image. When the brightness of detected features falls outside a predetermined range, the processor adjusts the exposure parameter to optimize image quality. This parameter adjustment resolves the contradiction by adapting the automated exposure control to specific image content characteristics.
2Ease of operation
If exposure parameter is adjusted based on overall image brightness, then exposure control is simplified, but features in low-brightness regions may not be properly captured
Solution Approach 1:
The patent applies local quality analysis by detecting features at specific locations in the captured image and evaluating their brightness independently. Instead of relying solely on overall image brightness, the system identifies local features (such as edges, corners, or distinctive patterns) and adjusts exposure based on the brightness of these specific regions. This ensures that features in low-brightness regions are properly captured while maintaining ease of automated operation.
Solution Approach 2:
The patent replaces complex manual exposure adjustment mechanisms with an automated image processing system that uses feature detection algorithms. The processor automatically identifies features, measures their brightness, and determines appropriate exposure adjustments without requiring user intervention. This substitution maintains ease of operation while achieving precise feature brightness detection through computational methods.
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
Improves exposure control performance by dynamically adjusting camera settings based on real-time image analysis, reducing the risk of supersaturation and enhancing image clarity across different lighting conditions.
Implementation Method 1
a camera configured to generate a detection signal by photoelectrically converting incident light
Data Source
AI summary
An electronic device and a method of controlling the electronic device are provided. The electronic device includes a camera configured to generate a detection signal by photoelectrically converting incident light, and one or more processors configured to control operations of the camera and process the detection signal, wherein the one or more processors are further configured to generate a plurality of input images from the detection signal, determine a first parameter applied to the camera, based on brightness information of each of the plurality of input images, detect at least one feature from each of the plurality of input images, determine whether to update the first parameter, based on a result of detecting the at least one feature, and adjust the first parameter, based on a brightness of the at least one feature upon determining to update the first parameter.