BERT Video Error Visualization via Pixel Illumination
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Solution Overview
Problem
Current digital network analysis tools, such as BERTs, struggle to provide effective subjective analysis of video stream quality due to the expense and complexity of decoding full motion video on portable devices, and conventional decoding schemes hide error conditions, making it difficult to detect corrupted pixels.
Innovation Solution
A battery-operated BERT that gains test access before the decoder stage to analyze raw data flows, displaying only corrupted pixels and allowing for subjective analysis, with options to decode compression schemes like MPEG-2 or use a reference test file for error identification, and incorporating color coding and histogram presentations to show error history.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If full motion video decoding is performed on portable field test tools, then subjective evaluation of video quality becomes possible, but the device complexity and cost increase significantly
Solution Approach 1:
The invention segments the video stream analysis by focusing only on specific time periods and error conditions rather than processing the entire video stream. The system identifies and displays only corrupted pixels or specific error patterns, separating the error detection function from full video decoding, thereby reducing complexity while maintaining subjective evaluation capability.
Solution Approach 2:
The invention extracts only the essential error information from the video stream without requiring complete video decoding. By pulling out specific error conditions and corrupted data points, the system enables subjective evaluation of quality issues without the computational burden of full motion video decoding on portable devices.
2Reliability
If conventional decoding schemes are used, then video can be displayed properly, but error conditions such as lost pixels are hidden
Solution Approach 1:
Instead of displaying the complete video stream with errors hidden by conventional decoding, the invention inverts the approach by displaying only the error conditions and corrupted pixels. This inversion makes error detection the primary function, allowing technicians to see lost pixels and error patterns that would normally be concealed by standard decoding algorithms.
Solution Approach 2:
The invention uses color coding to highlight error conditions in the video display. Corrupted pixels or error conditions are displayed in distinct colors (such as red or yellow) compared to normal video content, making error detection visually apparent and eliminating the difficulty of measuring quality degradation in the traditional video stream.
3Measurement precision
If bit error rate measurement is performed using traditional methods, then objective analysis is provided, but subjective evaluation of video quality cannot be conducted
Solution Approach 1:
The invention merges objective bit error rate measurement with subjective video quality evaluation in a single integrated display. The system combines numerical BER metrics with visual representations of error conditions, allowing technicians to simultaneously obtain precise measurement data and conduct subjective evaluation of video quality without requiring separate analysis systems.
Solution Approach 2:
The invention introduces an intermediary visual display layer that translates objective error data into visually interpretable representations. This intermediary layer presents error conditions in a format that enables subjective evaluation while maintaining the precision of objective measurement, bridging the gap between quantitative BER data and qualitative video quality assessment.
Data Source
AI summary
Systems and methods for analyzing the performance of a digital network include capturing a stream of digital data, e.g., interne protocol (IP) packets, that represent streaming video, identifying which of the IP packets include bit errors, determining to which of a plurality of pixels the IP packets including bit errors belong and identifying such pixels as corrupted pixels, and illuminating only the corrupted pixels on a display of a tool. Corrupted pixels in different time blocks can be displayed with different colors to gain a better appreciation of the bit error rate over time.