Dynamic Motion Threshold Adjustment for Image Processing
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Solution Overview
Problem
Existing image processing technologies face challenges in uniformly detecting mobile objects across frames with fluctuating contrast, leading to erroneous detection and increased after-images of mobile objects.
Innovation Solution
An image processing apparatus and method that calculate contrast and adjust motion-detecting thresholds based on the contrast level, allowing for accurate detection of mobile objects by generating a composite image using a contrast calculator, threshold calculator, and composite image generator.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a fixed motion-detecting threshold is used for all frame images, then the device complexity is reduced, but the measurement precision of mobile object detection deteriorates when contrast fluctuates
Solution Approach 1:
The motion-detecting threshold is changed from a fixed value to a dynamic value that automatically adjusts according to the contrast of each frame image. The threshold calculator computes different thresholds based on real-time contrast measurements, allowing the detection system to adapt to varying image conditions without increasing operational complexity.
Solution Approach 2:
The threshold parameter is modified based on the contrast parameter of the frame image. When contrast changes between frames, the motion-detecting threshold is recalibrated accordingly, ensuring that detection sensitivity remains optimal across different lighting and scene conditions.
2Reliability
If the composite ratio of the preceding frame image is increased to improve S/N, then the noise reduction effect is enhanced, but the after-image of mobile objects increases
Solution Approach 1:
Different composite ratios are applied to different regions of the image based on motion detection results. Stationary regions use a higher composite ratio for better noise reduction, while regions containing mobile objects use a lower composite ratio to minimize after-image effects, achieving localized optimization of both noise reduction and motion fidelity.
Solution Approach 2:
The system uses feedback from motion detection results to dynamically adjust the composite ratio. The finite difference calculation provides feedback about motion presence, which then controls the blending proportion of the preceding frame, creating a closed-loop system that balances noise reduction against mobile object artifacts.
3Ease of operation
If a single threshold value is used for motion detection, then the ease of operation is improved, but the adaptability to different contrast conditions deteriorates
Solution Approach 1:
The threshold calculator automatically determines the appropriate motion-detecting threshold for each frame image based on its contrast characteristics, eliminating the need for manual threshold adjustment. The system serves itself by computing optimal thresholds from the image data, maintaining ease of operation while achieving full adaptability to varying contrast conditions.
Data Source
AI summary
An image processing method is performed by a processor of an image processing apparatus which performs image processing of an input frame image. In the image processing method, a tonal histogram is generated, of which a gross area corresponds to a total number of pixels in the input frame image, and contrast is calculated from the tonal histogram. An input of a motion-detecting level with respect to the input frame image is received. A motion-detecting threshold corresponding to the motion-detecting level and the contrast is calculated. A composite image of the input frame image and a preceding frame image of the input frame image is generated based on a comparison result of a finite difference value between the input frame image and the preceding frame image, and the motion-detecting threshold.