Gaussian Image Quality Analysis for Vehicle Camera Vibration
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Solution Overview
Problem
Current image analysis methods for vehicle camera systems are limited by vibrations and environmental changes, leading to defects in image quality assessment, especially when slight motion occurs, and rely heavily on progressive image-to-image analysis which is flawed by vibration errors.
Innovation Solution
A gaussian image quality analysis system that converts camera images to grayscale, computes pixel intensity changes using convolution equations, differentiates weak and strong pixels based on magnitude and orientation, and calculates variance to assign blur numbers, allowing for effective comparison and filtering of image quality across different camera cleaning systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If progressive image-to-image analysis is used, then image quality assessment can be performed, but vibration errors from earlier images impact each successive image leading to defective analysis
Solution Approach 1:
The patent segments the image analysis process by analyzing each frame independently rather than progressively. Each frame is processed through convolution operations to compute pixel intensity changes without relying on previous frame data, thereby isolating the analysis from vibration errors that propagate in progressive methods
Solution Approach 2:
The patent introduces an intermediary computational approach using convolution equations and variance calculations as intermediate steps. Instead of direct progressive comparison, the system uses these mathematical intermediaries to assess each frame's quality independently, breaking the chain of error propagation
2Measurement precision
If common image analysis methods are used, then image quality can be evaluated, but the methods are highly dependent on and limited by the test procedure used
Solution Approach 1:
The patent changes the fundamental parameters of image analysis from progressive pixel-by-pixel comparison to statistical moment-based analysis (variance, skewness, kurtosis). These parameter transformations make the analysis independent of specific test procedures and applicable across different imaging conditions and camera systems
Solution Approach 2:
The patent creates a universal image quality assessment method that works across different test procedures and environmental conditions. The statistical moment approach serves multiple functions: assessing sharpness, detecting blur, and evaluating image quality without requiring procedure-specific calibration or reference images
3Measurement precision
If full-reference image analysis is used, then image quality can be assessed, but the method is defective when even slight vibrations occur due to motion changes
Solution Approach 1:
The patent extracts the essential quality information from each frame independently through convolution operations and statistical moment calculations. By taking out the analysis from the progressive sequence and treating each frame as a standalone unit, the method eliminates sensitivity to vibrations and motion changes that affect frame-to-frame consistency
Data Source
AI summary
A camera image cleaning system of an automobile vehicle includes a camera generating a camera image of a vehicle environment. A processor having a memory executes a control logic to convert the camera image into a grayscale image having multiple image pixels. A convolution equation is retrieved from the memory and is solved to find derivations of the grayscale image defining changes of pixel intensity between consecutive or neighbor ones of the multiple image pixels of the grayscale image. A magnitude and an orientation of the multiple pixels is computed by the processor and used to differentiate weak ones of the image pixels from strong ones of the image pixels.