Automated Video Scaling and Alignment Testing
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Solution Overview
Problem
Manual video scaling and alignment tests for DVB-T compliant display devices are time-consuming and prone to variability due to continuous user intervention, making automation challenging, especially for tests involving dynamic backgrounds and tolerance values.
Innovation Solution
An automated method utilizing image recognition and comparison techniques that focuses on detecting and comparing green and gray targets on a captured frame from a live video, independent of video content, using a modified Hough Transform and mean square error calculation to determine proper scaling and positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual testing is used for video scaling and alignment, then the tester can continuously monitor and adjust test results, but the testing process becomes time-consuming and requires continuous user intervention
Solution Approach 1:
The system automatically performs video scaling and alignment testing by capturing display output, processing images through computer vision algorithms, and comparing results against reference data without requiring continuous human intervention. The automated image processing pipeline independently completes detection, comparison, and evaluation tasks that previously required manual tester involvement.
2Productivity
If automated image comparison is used for video positioning tests, then testing speed increases, but the comparison function becomes disabled due to dynamic background video changes
Solution Approach 1:
The system extracts specific target elements (such as test patterns, logos, or reference objects) from the dynamic video background by using image processing techniques. By isolating these stable target features from the changing background, the system can perform reliable position and alignment comparisons without being affected by background video changes.
Solution Approach 2:
Instead of comparing entire video frames, the system focuses comparison operations on specific local regions containing test targets. By applying different processing strategies to different parts of the image (rigid comparison for stable targets, flexible handling for dynamic backgrounds), the system maintains comparison accuracy in critical areas while tolerating background variations.
3Adaptability or versatility
If tolerance values are applied to target positions in automated testing, then the testing system becomes more adaptable to variations, but the comparison function is disabled
Solution Approach 1:
The system dynamically adjusts comparison parameters such as tolerance thresholds based on the specific test configuration and observed variations. By modifying comparison sensitivity and acceptance criteria as parameters rather than fixed rules, the system maintains both adaptability to position variations and full automation capability for evaluating whether targets fall within acceptable ranges.
Data Source
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AI summary
The present invention relates to automation of systems that are intended for testing display devices. Video scaling and alignment testing are performed manually by testers in prior art. This gives rise to a great loss of time. The method mentioned in the present invention discloses automated application of test steps which are applied to an image captured from a live video at a certain time.