Ateliere Creative Technologies has won the Gold for Cloud Based Services in the Digital Media World Awards 2022. The Digital Media World Awards showcase innovative products that help broadcasters, service providers, producers and many others adapt to the evolving content production and distribution landscape. Products must be innovative, boundary-pushing, and creative by finding new ways of working and forging new markets.
Our proprietary technology, FrameDNA/Deep Analysis Video Deduplication, which can be accessed through the Ateliere Connect platform, addresses many challenges, such as the need to distribute and stream content to multiple platforms and/or countries. This results in many versions of content to meet numerous different compliance requirements and language needs; one title could generate hundreds of versions. Content libraries and archives contain significant duplication that stresses storage pools, and makes moving content into flexible cloud-based delivery and distribution workflows too costly. Deep Analysis/FrameDNA automates video deduplication, optimizing storage pools and easing movement of content into the cloud.
Ateliere’s FrameDNA AI/ML first fingerprints every frame in the image track of a file upon ingest into Ateliere Connect, and based on structural similarities, identifies the scenes that are different, allowing for comparison. Deep Analysis can then automatically extract the variant clips without having to manually scan through the entire file.
Deep Analysis is IMF generation in the cloud, converting the results of its scan into a base Composition PlayList (CPL) that contains the original material and various supplemental (CPLs) that describe how to combine your original material and deltas together to compose different versions. The deduplication workflow reduces QC costs by limiting content to be validated only to elements that are different, such as subtitles. By storing content in the form of CPLs, users can quickly render the required localized versions on demand, keeping and discarding texted elements based on the content platform’s requirements.