Small Pixels, a spin-off from the University of Florence, combines academic research with technological innovation to transform the audiovisual sector.
Through artificial intelligence and pre-trained neural networks, its solutions significantly enhance video quality, reduce operational costs, increase transmission efficiency, and minimise environmental impact.
Applications range from live broadcasting and gaming to the restoration of historical archives and remote production, optimising workflows without requiring modifications to existing processes.
The use of various advanced neural networks—beyond broadcasting and live sports—enables ideal applications in other fields, including videoconferencing, aerospace image processing, video surveillance, footage management from action cameras and drones, and much more.
Upscaling
In a previous article [Link to the release], we explored upscaling to a higher video standard.
Revamping and Restoration
In a second article [Link to the release], we examined how these technologies are ideal for restoring and revamping historical video archives.
Remote Production
Today, we focus on remote production in the broadcast industry, which was accelerated by the COVID-19 pandemic, when on-site travel was restricted, revolutionising television production.
This approach optimises human and technical resources, eliminating the need to deploy costly on-site production setups when large-scale deployments are unnecessary. It also reduces logistical complexity related to moving crews and oversized production equipment.
Better cost control has made television production accessible to organisations that previously found it unsustainable.
The efficiency of the system extends beyond transmission to real-time management of video and audio signals, facilitating operations such as live editing, advanced graphics integration, and remote production team control, even across thousands of kilometres.
In this context, the Florence-based company plays a strategic role. Organisations adopting Small Pixels’ pre-trained neural network software can make further advancements, gaining a significant competitive advantage.
By leveraging pre-trained neural networks, Small Pixels reduces bandwidth consumption for each video stream in remote production, optimising signal compression without compromising quality.
This allows multiple video streams to be transmitted over the same network infrastructure, enabling more flexible and efficient production models, such as managing multi-camera events from remote control centres, even over long distances.
Broadcasters thus lower operational costs, minimise latency, and improve the sustainability of their operations.
This efficiency is particularly crucial for live broadcasts, especially in sports.
Since the primary constraint of remote production is available bandwidth, meaning network capacity, Small Pixels enables more signals to be transmitted with better quality without increasing overall consumption.
This allows for broader and more detailed coverage, with more camera angles and added-value content.
It is no coincidence that several major international media and broadcasting players are showing growing interest in Small Pixels’ solutions, particularly for live broadcasting of high-complexity sports events.
These productions, often carried out in stadiums or large sports venues, involve the simultaneous use of multiple cameras—up to 20—and, increasingly, the real-time transmission of all audio, video, data, and communications signals to remote production centres, sometimes located on different continents. Transport is carried out via multi-gigabit fibre optic backbones, with bandwidth per video stream ranging between 70 and 150 Mbps.
Although this setup already enables high-quality transmission, many operators are exploring ways to further enhance visual quality or integrate more cameras. However, these objectives inevitably impact the available bandwidth per signal, making management with traditional technologies challenging.
In this scenario, Small Pixels emerges as an enabling technology to overcome these limitations. Thanks to a significant reduction in required bandwidth—up to 30% per stream—without perceptible quality loss, Small Pixels’ solutions offer a concrete opportunity to optimise network resource usage, increase the efficiency of remote workflows, and, where necessary, extend live event coverage by adding new cameras.
This increased efficiency opens up new possibilities for remote production, allowing for more centralised and flexible television production management, with reduced on-site technical infrastructure and greater operational scalability.
Further Discussion Topics [Questo indirizzo email è protetto dagli spambots. È necessario abilitare JavaScript per vederlo. ]:
- How exactly do Small Pixels’ pre-trained neural networks work?
- What are the main advantages of upscaling using neural networks compared to traditional techniques?
- How do Small Pixels’ neural networks enhance video quality?
- Which sectors benefit most from Small Pixels’ neural networks?
- How does the quality of Small Pixels-enhanced videos compare to traditionally processed videos?
INFO: https://www.smallpixels.ai
Synopsis
Small Pixels is revolutionising remote production with pre-trained neural networks that reduce bandwidth requirements by up to 30% per video stream. This innovation enhances quality and efficiency in live broadcasting, enabling more sustainable and scalable production models.
Tags: #SmallPixels, #AI, #broadcast, #remoteproduction, #videoquality, #compression, #livesports, #streaming, #efficiency, #bandwidth
