Software's Impact on the Progression of Testing and Measurement Techniques

Software's Impact on the Progression of Testing and Measurement Techniques

In the ever-evolving world of broadcasting, a significant shift is underway as test and measurement (T&M) and quality control (QC) strategies adapt to the challenges posed by IP workflows. The focus is on a robust integration of software capabilities, cloud operations, and artificial intelligence (AI) to tackle the complexities introduced by IP broadcasting.

A fundamental rethinking of content routing and delivery is taking place, with the emphasis shifting from traditional visual and hardware-centric monitoring to network-centric analysis. This new approach necessitates monitoring network-specific parameters such as packet drops, jitter, latency, multicast losses, and Precision Time Protocol (PTP) synchronization, in addition to video waveform or picture signals [1][3].

Modern T&M solutions are evolving to meet these demands. They integrate waveform monitors and rasterizers with deep IP monitoring features, providing visibility across SDI and IP domains. For instance, Leader’s ZEN Series and LPX500 Series offer a comprehensive view, facilitating smoother migration and operational confidence [1].

Cloud operations are another key trend, with broadcasters adopting this technology to support scalable, flexible, and remote monitoring of IP streams and network health. This allows real-time diagnostics and management of IP multicast configurations, bandwidth, and routing, critical to preventing traffic storms and packet flooding [3].

Artificial intelligence (AI) is increasingly employed to reduce test complexity, automate problem detection, and improve diagnostics speed. For example, Emerson’s Nigel AI Advisor simplifies T&M by focusing engineers on innovation rather than managing data overload. AI transforms fragmentary data into coherent strategies and enables real-time optimization in operational contexts [2][4].

AI-enhanced QC and advertising measurement are also on the rise, supporting granular audience targeting, real-time campaign optimization, and post-campaign attribution in connected TV (CTV). This reflects an industry-wide trend toward dynamic, data-driven QC encompassing both technical signal quality and content/audience metrics [4][5].

As Erik Otto suggests, in the long term, AI could potentially reduce the need for traditional T&M due to its ability to produce better code and outcomes. However, there may still be a requirement for dedicated physical devices for detecting faults such as jitter [2].

Triveni Digital President and CEO Mark Simpson highlights that the approach to T&M and QC is driven by new distribution and platform technologies. AI techniques are expected to increase in Triveni's monitoring system for quality scoring of feeds based on observations [1].

Ashish Basu from Interra Systems mentions that the company has been working with AI and machine learning for three to four years and has incorporated both into many of its products. Interra Systems uses AI and machine learning in areas like video signal quality or degradation measurements, but does not claim to be an AI company or produce AI products [1].

In the IP broadcast world, software is increasingly used for setting up, troubleshooting, and monitoring equipment, as well as for QC and regulatory compliance. Despite migration to software, cloud, or hybrid workflows, broadcasters approach T&M and QC in the same way [6].

Matthew Driscoll, vice president of product management at Telestream, observes that despite this migration, practical applications like clock and slate reading, contextual analysis, and object detection still hold potential for AI [7].

Ravi McArthur, product manager for Telestream's Qualify automated QC-in-the-cloud system, notes that the aim is to allow users to work where they want to be, with Qualify now being made available for on-prem operation [8].

Kevin Salvidge, sales engineering and technical marketing manager at Leader Electronics of Europe, states that software-based solutions are being adopted in cloud-native QC platforms and file-based and OTT workflows [9].

Mediaproxy has been completely software-based since its founding in 2001 for QC and compliance [10]. Some advanced broadcasting organizations may not have an interest in looking at captioning QC because it’s not mandated in their specific geographic region [11].

In conclusion, the role of software and cloud infrastructure is vital in supporting the complex IP environment, while AI acts as a force multiplier that streamlines testing, monitoring, and QC tasks to handle increasing scale and intricacy in IP-based broadcast systems [1][2][3][4].

1. The shift in the broadcasting industry is focusing on software capabilities, cloud operations, and artificial intelligence (AI) to tackle complexities in IP workflows.
2. A rethinking of content routing and delivery is occurring, moving from visual and hardware-centric monitoring to network-centric analysis.
3. Modern test and measurement (T&M) solutions integrate waveform monitors, rasterizers, and deep IP monitoring features for visibility across SDI and IP domains.
4. Cloud operations are essential for scalable, flexible, and remote monitoring of IP streams and network health, enabling real-time diagnostics and management.
5. AI is employed to reduce test complexity, automate problem detection, and improve diagnostics speed, with tools like Emerson’s Nigel AI Advisor focusing engineers on innovation.
6. AI-enhanced QC and advertising measurement are on the rise, supporting granular audience targeting, real-time campaign optimization, and post-campaign attribution in connected TV (CTV).
7. In the long term, AI could potentially reduce the need for traditional T&M, but dedicated physical devices may still be required for fault detection.
8. Triveni Digital’s monitoring system is expected to increase AI techniques for quality scoring of feeds based on observations.
9. AI and machine learning have been incorporated into Interra Systems’ products for video signal quality or degradation measurements, but the company does not claim to be an AI company.
10. Software is increasingly used for setting up, troubleshooting, and monitoring equipment, as well as for QC and regulatory compliance in IP broadcasting.
11. Despite this migration to software, cloud, or hybrid workflows, broadcasters approach T&M and QC in the same way.
12. Practical applications like clock and slate reading, contextual analysis, and object detection still hold potential for AI in the IP broadcast world.
13. Telestream's Qualify automated QC-in-the-cloud system aims to allow users to work where they want, with the availability for on-prem operation.
14. Software-based solutions are being adopted in cloud-native QC platforms and file-based and OTT workflows.
15. Mediaproxy has been completely software-based since its founding for QC and compliance, and advanced broadcasting organizations may not have an interest in looking at captioning QC due to regional regulations.

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