The discoverability of high-quality content is only becoming more important. For video content producers, broadcasters and service providers to be successful in the attention economy, it is of utmost importance to optimise discoverability and meet the user experiences the viewers expect, to prevent them from going elsewhere. The current state of video metadata technology does not help. Although the production of video content is becoming more and more advanced; the metadata available for broadcast video often still resembles the quality and details as was provided in the past century. Much like the early search engines for web pages in the nineties, current video metadata engines and recommender systems lack an understanding of what video content is actually about. There are no smart algorithms. 90% of the metadata that is used as input only consists of whatever was manually added by the video producer or manually added afterwards by a third party.
The solution that Media Distillery presents is smart content recognition technology that automatically generates in-depth, descriptive information about video content in real time.
Especially at broadcasters and tv operators, hardly any of the available software is currently being used. These organisations don’t have the IT capacity and technical mind-set to make the transition towards the optimal user experience that the public demands. Due to this lack of IT knowledge, vast amounts of quality content is currently only used to fill archives. Millions of euros invested in (subsidised) TV content is unable to find for the interested audience. Strong online and often US-based competitors such as Netflix and YouTube are currently filling this gap as they have more funding and economics of scale.
Our main goal is to scale our products for adoption by broadcasters and TV operators, but also to enhance every analysis component in use by Media Distillery. To fulfil this condition, the engine requires algorithms that seamlessly adjust to new settings. In pursuit of this objective we will:
• Enhance our distillery system by integrating two new AI analysis components
• Refine current AI distillery algorithms (analysis components) with the latest insights
• Unlock services for users in by means of Multi-modal analysis integration.
• Data pre-processing, machine learning tasks, calculation and validation of scores for gathered data.
• Unlock services for the TV operators by means of media application for demonstration activities.
• Optimize components to reduce implementation times and costs.
Demonstrate the effectiveness of Media Distillery in four media applications
MoDELS will be integrated within four media applications at broadcasters and TV operators throughout Europe. MoDELS will be used to power recommendations & user search, support editorial content and optimize the user experience in broadcaster’s platforms. Within the context of these demo’s we will:
• Establish the requirements for integration of our data in broadcasting platforms.
• Incorporate user and application specific adaptations in our APIs / software.
• Establish the impact of the technology (financial / non-financial) on the users’ business.
• Generate data to optimize the developed technology.
Success in commercialisation strategy of our new business plan Implement the presented marketing and commercialisation strategy. After testing and validation, we aim to commercialize MoDELS across the broadcasting value chain. To be successful we need to focus on:
• Dissemination: attract business partners and customers to create market demand.
• Business Plan and commercialisation strategy: incorporating a detailed commercialisation strategy (dissemination and exploitation) and a financial plan in view of market launch.
• Establish agreements with key customers in all relevant domains within European broadcasters and distributors.
