Digital Nirvana’s MetadatorIQ is the latest offering of the company’s secure, scalable platform to automate the process of metadata generation for production, preproduction, and live content workflows. MetadatorIQ is optimized for Avid and MediaCentral production management assets, it is a hybrid on-premise/SaaS application that applies advanced machine learning and AI-based content analysis to accelerate metadata generation and shorten content delivery cycles.
MetadatorIQ leverages the powerful video intelligence feature for the automatic generation of better structured, more detailed, and more accurate metadata.
Media organizations can reap two major benefits from MetadatorIQ:
Automatic and easy-to-use – MetadatorIQ is designed to automate the end-to-end process of creating transcription and video-based metadata for Avid-based assets. Users can search and retrieve content effortlessly within Avid as it automatically generates locators, customizable based on duration and color, which are then ingested into the Avid Interplay.
Video intelligence – Integrated with video intelligence, MetadatorIQ is capable of logo detection, face recognition, object identification, and shot-change identification among others.
Seamless solution – MetadatorIQ lives on the customer site for security reasons and can be installed on either a dedicated server or a virtual machine. The application automatically extracts the original asset and creates a low-res proxy or audio-only version of the actual media file.
Secure: MetadatorIQ protects valuable assets by moving the low-res proxy files to encrypted storage in the cloud, and the proxies are then erased once the job is processed.
In this era of digitalization, metadata has become an integral part of the information that can make organizing, finding, understanding, and working with instances of data easier. Extracting the experience spanning over two decades in the broadcast and media industry, Digital Nirvana is catering to metadata management and is constantly evolving in this space by applying machine learning and AI to content analysis improving the findability of these previously hard-to-index rich content types through the automated creation of high-quality metadata.
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