Video Waveform Validation Using Virtual Spectrum Analysis
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Solution Overview
Problem
Current automatic test equipment for complex signals, such as video signals, is limited in validating image content and requires extensive manual analysis or re-engineering for each new image, leading to inefficiencies and missed anomalies like electrical noise and glitches.
Innovation Solution
A method using a virtual spectrum analyzer and error bounds toolkit to automatically calculate and apply rules for validating timing, amplitude, and spatial image content of signals, allowing for quick and accurate validation of complex signals without manual re-engineering, by creating a set of rules from a known good waveform and storing them for reuse.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If basic timing and analog component analysis are used for video signal validation, then the testing process is simple and quick, but image content verification is limited and cannot detect subtle anomalies
Solution Approach 1:
The patent segments the video signal validation into three distinct analysis layers: timing measurements (sync pulses, blanking intervals), analog voltage measurements (amplitude levels), and image content verification (pixel-level analysis). This segmentation allows each layer to be processed independently with appropriate algorithms, improving overall verification accuracy without requiring a single complex system to handle all aspects simultaneously.
Solution Approach 2:
The patent implements preliminary action by first performing timing and analog measurements to establish baseline signal characteristics before proceeding to image content verification. The system pre-processes the signal to extract timing parameters and voltage levels, which then guide the subsequent image analysis phase. This staged approach enables detailed image content verification while managing system complexity through progressive analysis.
2Measurement precision
If extensive manual analysis is performed for each new image, then validation accuracy is high, but test development time increases significantly
Solution Approach 1:
The patent implements self-service through automated rule generation that creates validation criteria from a single reference image. The system automatically extracts timing parameters, voltage levels, and image characteristics to generate comprehensive test rules without requiring manual programming for each new image type. This self-configuration capability maintains high validation accuracy while eliminating the need for extensive manual test development for each new image or protocol.
Solution Approach 2:
The patent uses copying by creating a reference model from one known-good image that can be replicated and applied to validate multiple similar images. The system copies the structural and temporal characteristics from the reference image to generate validation rules that can be reused across different test cases. This approach maintains consistent validation accuracy across multiple images while significantly reducing test development time through rule reuse.
3Productivity
If canned standard tests are used, then test development time is reduced, but the tests cannot validate nonstandard or proprietary images
Solution Approach 1:
The patent implements dynamics by creating an adaptive test system that automatically adjusts its validation parameters based on the specific image being tested. The system dynamically generates validation rules tailored to each image's characteristics, timing structure, and content requirements. This dynamic adaptation enables the system to efficiently validate both standard and nonstandard/proprietary images without requiring pre-programmed test sequences for each format, thus maintaining high productivity while achieving universal adaptability.
4Reliability
If visual comparison by operator is used, then image content can be analyzed, but anomalies like electrical noise and glitches are easily missed or ignored
Solution Approach 1:
The patent replaces the mechanical human visual inspection process with automated computer-based image analysis algorithms. The system uses digital signal processing to detect timing deviations, voltage anomalies, and image content defects with objective criteria. This substitution eliminates human limitations such as fatigue, subjectivity, and tendency to ignore subtle anomalies like electrical noise and glitches, thereby improving anomaly detection reliability while achieving full automation of the validation process.
Data Source
AI summary
Method for validating a single waveform or series of waveforms that are intended for evaluating signals within an automated testing environment. Test signal data is supplied by an external source. The method creates a golden template from a known, good instance of the waveform under test and algorithmically applies it to other waveforms under test to determine compliance. In the application to video waveforms, timing parameters, deflection parameters and image content parameters are simultaneously tested resulting in efficient concrete and tangible results. Instead of providing the known, good instance of the waveform under test to a processor that implements the method, descriptive parameters of the known, good instance of the waveform may be provided to the processor that calculates data points of the expected video waveforms and then determines rules for the waveform based on the calculated data points.