This narrative begins in 2006. One of the more forward-thinking European broadcasters began the search for a system to take their single national feed, that was funded through traditional ad break advertising, and produce just over 30 variants of this channel. Every channel (regional feed) would now include some regional advertising content, replacing the national advertising breaks at specific times of the day. Starfish Technologies was awarded the contract to design and supply this system. It was implemented using SDI based technology located at each of the regional distribution hubs, the majority of which were unmanned. This system worked well and generated a significant additional revenue stream, so the broadcaster requested an ‘upgrade’ to this system that would also enable local news bulletins and late changing schedules to be inserted into every regional feed. These requirements were best implemented by moving to a centralised architecture and building a complete regional channel system located at its main transmission centre. The first centralised Starfish Technologies system went live in 2009 and again proved reliable, commercially rewarding and with the significant benefit of providing viewers with locally relevant news content.
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Over the last three decades, we’ve seen a shift from specialised units for various video and audio functions, to high performance hardware platforms which can be repurposed on-the-fly to support any AV processing, and now to virtualized media applications and microservices which can be spun up and deployed (and paid for) only when you need them.
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The Monarch EDGE encoder and decoder pair powers remote and cloud-based productions by securely transporting high-quality live video across public Internet
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In the media industry, conversations usually center on encoding, streaming, packaging and low latency. Put another way, it is all about delivering
superior visual quality and
high-fidelity audio with
better compression. Amidst all the discussions on digital video workflows, file ingest is often ignored. For example, when is the last time anyone compared different file ingest tools? It is safe to say that file ingest is one of the most undervalued steps in video production workflows today.
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Imagine a sponge or rather thousands of sponges to be transported over several kilometers. It would require several trucks to store and transport them all!
Let’s take all those sponges and squeeze them. If you look closely at the sponge, you will see that it has lots of holes in it, filled with air. When you squeeze it, you remove the air which is useless and the entire sponge takes up less space.
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Over the last 20 years, the number of shared images and videos has considerably increased. In terms of resolution, we have moved from SD, HD to 4K – even 8K – and this is not about to stop. Higher frame rates, higher resolutions, more precision and higher dynamic range (HDR) imply a considerable increase in the amount of data to be transported on our networks. Bandwidth and storage are getting cheaper but this does not compensate the drastic increase of data to be transported or stored. Compression is therefore more than ever a fundamental step in distributing your video over the internet.
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Every color pixel in a digital image is created through some combination of the three primary colors: red, green, and blue. Each primary color is often referred to as a “color channel” or “color component”, and has a range of intensity values specified by its bit depth. The bit depth for each primary color is termed the “number of bits per channel”, typically ranging from 8 to 16bits. The “bits per pixel” (bpp) refers to the sum of the “number of bits per color channel” i.e. the total number of bits required to code the color information of the pixel.
An uncompressed RGB image with a bit depth of 8 bits per color will have 24 bpp or 24 bits per pixels (8 bits for the Red, 8 bits for the Green, 8 bits for the Blue)
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Technology improves constantly, internet connections get better and faster, but in conjunction, video resolutions get higher and files exponentially bigger! Compression is more than ever a requirement to distribute your video.
Over the last 20 years, resolution and frame rates have moved from SD (@24fps), to HD, then 4K (@60fps), now reaching 8K (@120fps) and will likely keep increasing. Thanks to all these improvements, we are entertained with better images and videos, and machine vision algorithms (AI, Analytics) can make better decisions.
Higher frame rate, higher resolution, more bit per pixel (or precision) and higher dynamic range (HDR) imply a considerable increase in the amount of data to be transported, recorded and processed.
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No matter where you are in the modern world, digital video is most likely in every one of your spaces (and if it is not, you are probably carrying it in your pocket by way of a smart phone). What does it take to record, encode, decode, edit and deliver digital video assets and virtual experiences—from live concerts to breaking news stories to sports events as well as surveillance footage and digital medical records? The short answer is a whole lot of talent and technology.
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Encoding an OTT (over-the-top) ladder can be a demanding job. To satisfy the needs of viewers on a variety of screen sizes and resolutions, like 4K TVs and smartphones, you will inevitably end up with an encoding ladder that has many rungs. For every rung, or every step on the ladder, typical encoding scenarios will require an encoding instance.
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