Broadcasting Stream Still Image Alarm Suppression
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Solution Overview
Problem
Existing broadcasting and/or streaming systems generate false still image alarms, requiring manual intervention by staff to resolve, which is resource-intensive and inefficient.
Innovation Solution
A method and device that detect still images in broadcasting and/or streaming data, compare them to a whitelist of intended images, and suppress false alarms by using image analysis techniques to verify if the detected still image is intended, thereby reducing unnecessary staff interaction and increasing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If still image detection is performed to identify errors in broadcasting/streaming data, then error detection capability is improved, but false alarms increase requiring manual intervention
Solution Approach 1:
The system performs preliminary action by creating a whitelist of intended still images before the monitoring process. This whitelist is stored in advance and used to automatically verify detected still images, eliminating the need for manual intervention when intended still images are detected.
Solution Approach 2:
The whitelist acts as an intermediary between the still image detection unit and the alarm output. It mediates the verification process by providing a reference set of intended still images, allowing the system to automatically distinguish between intended and erroneous still images without requiring staff intervention.
2Reliability
If manual verification of still image alarms is performed, then false alarms can be identified, but staff resources are consumed
Solution Approach 1:
The system performs self-service by automatically verifying detected still images against the whitelist without requiring staff intervention. The monitoring device independently determines whether a still image is intended or erroneous, eliminating the need for manual verification and freeing staff resources.
Solution Approach 2:
The whitelist provides feedback mechanism by storing information about intended still images. When a still image is detected, the system compares it with the whitelist and automatically adjusts alarm output based on this feedback, improving alarm accuracy without consuming staff resources.
3Reliability
If all still images trigger alarms, then error monitoring is comprehensive, but unnecessary alarms reduce system efficiency
Solution Approach 1:
The system applies local quality by treating different still images differently based on their nature. Intended still images (those in the whitelist) are handled with suppression, while unintended still images trigger alarms. This localized differentiation maintains comprehensive error monitoring while eliminating unnecessary alarms.
Solution Approach 2:
The system changes the alarm output parameter based on the verification result. When a still image matches the whitelist, the alarm parameter is changed to suppressed state; when it doesn't match, the alarm parameter remains active. This dynamic parameter adjustment maintains monitoring coverage while improving efficiency.
Data Source
AI summary
A method for monitoring data related to broadcasting and/or streaming is described wherein broadcasting and/or streaming data are provided. At least one still image within the broadcasting and/or streaming data is detected in order to identify a possible error. The still image detected is compared with a whitelist of images. A still image alarm is suppressed provided that the still image detected is in the whitelist. Further, a device for monitoring data related to broadcasting and/or streaming is described.