personalised linear-like playback of either editorially curated , or algorithmically machine-recommended content .
A third application to TV Every where is standalone subscription video-on-demand ( SVoD ) services that are decoupled from an operator ’ s billing system . “ To support this model , you need to employ a complex and rule-rich product monetisation module ,” Sørhaug adds , while emphasising the Vimond provides an end-toend suite of products to support every step in setting up and running a successful TV Everywhere ecosystem .
He describes : “ Vimond ’ s deep application programming interface ( API ) library and editorial tools enable front-ends with discoverability and usability in mind — with tools for real-time and dynamic content updates to keep the service fresh and relevant , keeping viewers coming back day after day to watch their favourite shows and movies .”
And while cord-cutting is increasingly a worrisome trend as content consumption moves towards connected devices and screens , there is also a need to address the cord-nevers — people who have not had any pay-TV subscriptions to speak of — as Sørhaug points out .
For this segment of consum-
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Vimond offers an end-to-end suite of products to support every step in setting up and running a successful TV Everywhere ecosystem , including Vimond IO , a cloud-based editing tool .
Stein Erik Sørhaug , VP of product strategy , Vimond : TV Everywhere offers the potential to better understand what viewers want through technologies such as AI . Content discovery , particularly , is an immediate application for AI .
ers , keeping content fresh and relevant is critical , alongside the offering of cost-effective and flexible subscriber plans . “ For these paying customers , it is extremely important to apply functionality , as Vimond does , to reduce churn and reduce customer retention costs .”
Because TV Everywhere involves the streaming of video content over the open Internet , there exist challenges and oppor-
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tunities in equal measure . Where the former is concerned , piracy is an existential concern .
Content protection is indeed an increasingly important topic , highlights Edgeware ’ s Brandon . “ A few years ago , those wanting to pirate a movie would have to go into a cinema with a camera and produce copies of a video or DVD . Now , because so much content is delivered online , programming
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is more exposed to the threat of piracy than it has ever been .”
To combat this , Edgeware offers a forensic watermarking solution that integrates Content- Amor ’ s technology into Edgeware ’ s TV CDN platform . The solution , which was awarded the IABM Design and Innovation award at IBC 2017 , is able to identify and manipulate several pixels within video streams and change them in a way that creates a specific code within the video itself .
“ And again , because programming is delivered from the edge of the network , a distributor can add this watermark at the latest point , so each viewer has a unique code ,” says Brandon . “ This is what makes it easy for us to help content distributors spot exactly who has stolen a particular video stream .”
The transition to the online video domain , however , offers the golden opportunity to truly understand each consumer , and reinvigorate the discovery process by offering personalised content that will truly appeal to the target audience .
As sophisticated metadata continues to proliferate , articificial intelligence ( AI ) is likely to come to the fore this year . Vimond , for example , recently announced a technology and marketing partnership with video AI company Valossa . With the integration of
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their respective products , the companies add content recognition capabilities to Vimond ’ s platform , making it easier to search for contextually relevant images and video content .
Vimond ’ s Sørhaug says : “ While many AI solutions in the market have been waiting for a business plan , Vimond and partners like Valossa have found real-world applications for the now in AI .”
One area AI has made great strides , he believes , is in video recognition and the automated creation of “ deeply rich ” metadata that can be searched in the cloud to open up vast content archives previously inaccessible . “ This opens new opportunities for using historic video to rapidly create new materials and content .” For example , if there is a celebrity death or a royal marriage , archive content can be accessed , and using Vimond ’ s storytelling tools , new and deep content can be quickly created .
Content discovery , Sørhaug agrees , is also an immediate application for AI . “ Using machine learning and intelligent understanding of human behaviour , you can now blend with editorially curated content from a human , a personalised linear viewing experience targeted exactly to your own interests beyond simple genre matches .”
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