Display Error Detection Using Pixel Histogram Correlation
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Solution Overview
Problem
Existing error detection methods, such as CRC, are inadequate for accurately detecting errors in display images that may result in minor visual changes, making it difficult to determine if a user correctly recognizes icons or characters displayed on a screen.
Innovation Solution
A circuit device and method that calculates a histogram of pixel values, performs correlation operations, and uses indices to evaluate the dissimilarity between foreground and background images, allowing for accurate error detection based on visibility and shape coincidence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If bit error detection such as CRC is used, then error detection capability is improved, but the ability to detect visually significant errors deteriorates
Solution Approach 1:
The invention changes the detection parameter from bit-level error detection (CRC) to visual significance-based detection. By calculating a visual significance map that assigns importance weights to different image regions based on human visual characteristics, the system detects errors in visually important areas while ignoring minor errors in less important areas, thus resolving the contradiction between error detection capability and visual error detection accuracy
Solution Approach 2:
The invention introduces a visual significance map as an intermediary between the image data and error detection process. This map acts as a weight distribution that highlights visually important regions, allowing the error detection to focus on areas that matter to human perception rather than treating all pixels equally, thereby improving visual error detection accuracy
2Reliability
If all pixel errors are detected, then detection completeness is improved, but the ability to identify user-recognizable errors deteriorates
Solution Approach 1:
The invention applies local quality by assigning different detection thresholds and importance weights to different regions of the image based on their visual significance. Visually important regions such as foreground objects and text receive higher weights and stricter detection thresholds, while background regions receive lower weights, allowing the system to maintain detection completeness while improving accuracy for user-recognizable errors
Data Source
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AI summary
[Object] A circuit device, an electronic apparatus, and an error detection method capable of accurately detecting an error that a user is likely to erroneously recognize are provided. [Solution] A circuit device 100 includes an image acquisition circuit 130 for acquiring a display image and an error detection circuit 150 for performing error detection on the display image. The error detection circuit 150 calculates a histogram of pixel values of the display image, performs a correlation operation using the histogram, calculates an index indicating a degree of dissimilarity between a foreground image that is an image of a given region in the display image and a background image that corresponds to a background of the foreground image in the display image based on a result of correlation operation, and performs the error detection based on the index.