Video Asset Monitoring Using Masked Region Analysis
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Solution Overview
Problem
Conventional multimedia content distribution networks (MCDN) face challenges in quality control and performance monitoring, relying on user feedback and costly on-site visits for performance feedback, which is inefficient and costly.
Innovation Solution
A method and system for test monitoring of baseband video signals in MCDN, involving the acquisition of video and audio signals at predetermined rates, application of image masks, and comparison against known images or thresholds, with automated execution of pass or fail scripts and logging of results, using an expert test monitoring platform (ETMP) to assess video and audio check points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional MCDN architecture uses user support requests and on-site visits for feedback, then quality control can be performed, but cost and time consumption increase significantly
Solution Approach 1:
The system enables self-service quality monitoring by automatically capturing video frames, applying image masks to isolate regions of interest, comparing masked regions against reference images, and generating test results without requiring human operators to manually inspect video quality or visit user locations. The monitoring system performs quality control autonomously using automated image processing algorithms.
Solution Approach 2:
The patent replaces the mechanical process of manual on-site visits and human inspection with automated digital image processing. Instead of physically visiting user locations to assess video quality, the system uses computational algorithms to automatically analyze video frames, apply image masks, compare against references, and generate quality assessment results electronically.
2Reliability
If conventional MCDN architecture relies on user feedback for performance monitoring, then quality control is possible, but feedback is reactive rather than proactive
Solution Approach 1:
The system performs preliminary quality monitoring actions by continuously capturing video frames and automatically analyzing them before user complaints occur. The automated system proactively identifies quality issues by comparing current video frames against reference images and detecting anomalies, enabling preventive maintenance before actual user problems manifest.
Solution Approach 2:
The patent implements continuous feedback loops where the system automatically monitors video quality in real-time, compares results against thresholds, and generates immediate test results. This closed-loop feedback mechanism enables proactive identification and reporting of quality issues without waiting for user support requests.
3Productivity
If automated testing of video signals is implemented, then quality control efficiency improves, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the video image into distinct regions of interest using image masks. Instead of analyzing the entire video frame, the system isolates specific areas (such as picture-in-picture regions, text areas, or specific visual elements) for targeted quality assessment. This segmentation reduces computational complexity while maintaining monitoring effectiveness.
Solution Approach 2:
The system implements local quality assessment by applying different analysis methods to different regions of the video image based on their specific requirements. The image mask mechanism allows the system to focus computational resources on specific areas that require monitoring, rather than uniformly processing the entire frame, thereby optimizing the balance between monitoring comprehensiveness and system complexity.
Data Source
AI summary
A method and system for monitoring video assets provided by a multimedia content distribution network (MCDN) includes an expert test monitoring platform (ETMP) configured to emulate MCDN client systems at a facility of an MCDN service provider. The ETMP may be used to test monitor MCDN performance by acquiring a baseband video signal and performing a test operation including at least one check point condition. The check point condition may be associated with a masked region of the video signal and may also involve a test of an audio channel. A plurality of test operations and/or check point conditions may be defined and executed on the baseband video signal, while the results of the test operation may be logged.