Interra Systems Baton Plus incorporates data analytics into the QC process.
Since file-based workflows became ubiquitous in broadcast facilities, automated content QC solutions have provided significant cost and reliability advantages compared with QC by visual inspection. However, file-based media workflows are continually increasing in sophistication, making it ever more challenging for broadcasters to ensure content quality.
Another issue that broadcasters face is the large amount of QC data that is populated by modern quality control systems. What can media organizations do to make the most of this information? Too much data can be overwhelming without an effective analysis strategy.
Interra Systems’ Baton file-based automated QC solution addresses these issues, enabling broadcasters to perform comprehensive quality checks of SD, HD, UHD/4K, and mixed workflows, as well as analyze data and identify trends for a deeper understanding of content quality. By allowing broadcasters to gain a deeper insight into quality control across the organization, Baton leads to improvements in content QC processes and increased efficiencies.
The solution is based on a unique Measure > Analyze > Optimize (MAO) framework that incorporates data analytics into content QC processes. By enhancing the way that broadcasters have previously approached QC (i.e., check a file, review verification report, take action, forget about issue), the MAO model enables better long-term planning of content QC procedures and strategic changes to made. Broadcasters can gain a more holistic view and deeper insights about how the content QC has happened over a long period of time and across different departments and facility locations.
Inside the measurement process
Here’s how it works. During the measurement stage, QC performance metrics are tracked over an extended time period. Metrics are analyzed to identify common patterns and operational issues, pointing out potential areas of improvement as well as recommended changes. Optimization occurs when those changes are applied to the content QC processes.
This entire process is then replicated a second time to verify that the applied changes have made a positive impact on different QC performance metrics. New measurements are analyzed for additional issues, and adjustments are made.
There are several ways that broadcasters can apply the MAO framework to their workflow to improve content QC processes, including asset categorization, QC results summarization, and capacity planning.
For asset categorization, broadcasters can glean metadata and error information about their files from QC reports to figure out how many hours of content have been processed, what bit rates are being used, and which assets are SD, HD, or UHD/4K files. This can be especially beneficial for stations transitioning from SD to HD, giving them a precise measurement of how much legacy content still exists for SD-to-HD upconversion planning purposes.
Carrying out in-depth analysis of QC results, broadcasters can better understand the content QC process and decrease the number of files that are failing. This solution allows broadcasters to view the percentage of files failing due to an error, the different kinds of errors present in various files, and which errors are more prominent than others. Common errors can be identified. After they isolate the reason for the error, broadcasters can adjust their QC process to address these issues as needed.
From a capacity planning perspective, broadcasters want a QC system that is mean and lean.
The MAO framework ensures that checkers aren’t sitting idle, QC tasks are completed in a reasonable amount of time, and higher priority tasks are given the necessary resources. With enough capacity, the system can easily handle peak load situations. In addition, media organizations with QC systems across multiple sites can equitably distribute resources, regularly reviewing their distribution and usage.
Ultimately, today’s broadcasters want to deliver a superior television experience to viewers. Yet, as the content lifecycle becomes more complex, providing a high-quality experience is no easy task. Interra Systems’ Baton offers broadcasters a reliable, efficient, and scalable QC solution. The MAO framework is the key advantage, making analytics an essential part of the QC file-based media workflow for deeper understanding of the content experience.
You might also like...
It seems almost superfluous today to specify that audio is digital because most audio capture, production and distribution today is done numerically. This was not always the case and at one time audio was primarily done without the help of…
For anyone who’s seen the first series to bear the title, the name Penny Dreadful will conjure up images of occult happenings in a shadowy, late-Victorian world. After twenty-seven episodes across three highly successful seasons, Showtime aired the last e…
There is level and then there is loudness. Neither can be measured absolutely, but by adopting standardized approaches it is possible to have measurements that are useful.
The first burst error correcting code was the Fire Code, which was once widely used on hard disk drives. Here we look at how it works and how it was used.
There are two basic reasons to know the level of an audio signal. One of these is more technical and one of them is more subjective.