Image Data Validity Verification via Color Group Segmentation
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Solution Overview
Problem
Existing methods for checking the validity of image data in display devices are inefficient in detecting errors, especially in safety-critical applications like motor vehicles, where accurate display of information is paramount, and they often require significant computational resources and storage capacity.
Innovation Solution
A method that divides image data into groups based on color and brightness values, calculates test values for each group, and compares them with specified values to determine validity, allowing for efficient error detection and reduced computational and storage requirements, while also accounting for tolerances and generating error messages or resets as necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If image data is checked as a whole without grouping, then verification completeness is maintained, but computational resources and processing time increase significantly
Solution Approach 1:
The image data is divided into multiple groups based on color value ranges (e.g., first group for maximum color value, second group for intermediate values, third group for minimum values). This segmentation allows parallel processing of different color groups, reducing overall computation time while maintaining verification completeness through group-specific test values.
2Productivity
If image data is divided into multiple groups based on color values, then computational resources are reduced, but verification complexity increases
Solution Approach 1:
Different verification approaches are applied to different color groups. For example, the first group (maximum color value) uses one test value calculation method, while the second and third groups use different methods suited to their characteristics. This local optimization reduces overall computational burden while maintaining accuracy.
Solution Approach 2:
The patent changes the verification parameter from checking all image data uniformly to using group-specific test values based on color value thresholds. This parameter transformation simplifies the verification process by converting a single complex check into multiple simpler, parallel checks.
3Measurement precision
If all image data points are processed to determine validity, then accuracy is maximized, but storage capacity and computational effort increase
Solution Approach 1:
The patent extracts representative test values from each color group instead of processing all individual image data points. By calculating test values based on color value thresholds and group characteristics, the method reduces data processing volume from millions of pixels to a manageable number of group representatives, while maintaining detection accuracy through strategic sample selection.
Data Source
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AI summary
The invention relates to a method for verifying a validity of image data, wherein the image data are associated with a display device (22). The image data are classified into at least two groups, depending on a color value of the image data and/or a brightness value of the image data, wherein a test value for a respective group is determined from the image data of the at least one group. The determined test value of a group is compared to a specified test value of the respective group. A validity of the image data is only ascertained if the determined test value of the respective group and the specified test value of the respective group correspond to each other. An invalidity of the image data is ascertained if the determined test value of the respective group and the specified value of the respective group do not correspond to each other.