To facilitate high accuracy in duplicate content detection, the solution uses video and audio similarity technology to compare individual frames of different assets and provides a duplication score with a confidence level between the assets. Keeping the above in mind, we built our AI-based video deduplication solution. Finally, it is also critical that the solution can be simply integrated with existing customer systems, thereby not causing changes in their existing processes. Our belief was that if we can help customers identify the volume of duplicate content, we can help them reduce operational costs, and especially that identifying duplicate assets is not humanly possible as customers have thousands of them. We noticed fairly early in our journey an increasing trend of content duplication (in the cloud) for a number of our customers. ![]() Therefore, a video deduplication solution becomes critical to identify the primary video asset and reduce storage costs.Īdditionally, while reducing costs and providing more effective backup processes are the guiding principles of deduplication solutions, it is also important that the solution works seamlessly with existing media asset management (MAM) systems to facilitate minimal changes in operations management for the content operations teams. Because metadata tagging of content over the years has become the norm rather than an exception for media organizations to facilitate search simplicity, duplicate content renders this process moot as it significantly increases the time to find the relevant content. Duplicate content versions result in media assets with the same metadata. As organizations grow and add more content and distribution streams, editorial operations also increase in conjunction to take care of compliance needs. These duplicates are created as a result of requirements for different aspect ratios or resolutions or different audio tracks for the same video asset the presence of black frames, graphics, text, or other effects and existing video asset subsets such as proxies and highlights (or clips) amongst other reasons, to facilitate compliance with regional requirements of distribution. Media organizations tend to create multiple versions of the same content during the editorial and postproduction process, inadvertently adding unwanted dollars to their storage expenses. Quantiphi is also a launch partner for AWS Media Intelligence solutions and has multiple AWS Service Delivery designations recognizing its expertise in supporting specific AWS services. Quantiphi is an Amazon Web Services (AWS) Advanced Consulting Partner and a member of the AWS Partner Network (APN) with AWS Competencies in Machine Learning (ML), Data & Analytics, Migration, and DevOps. ![]() In this blog, we present Quantiphi’s artificial intelligence (AI)–based deduplication solution that is tailored for media assets to help broadcasters reduce costs and lower backup times by removing an estimated average of 25 percent of duplicate content. ![]() These transformation initiatives are leading to increased migrations to the cloud and proliferation of media assets, thereby leading to increased storage and networking costs with challenges in the retrieval of relevant content. The media and entertainment (M&E) industry is undergoing a multitude of transformations, driven by ever-changing industry trends across the value chain from content production, supply chain, and broadcast to distribution. This blog was coauthored by Vibhav Gupta (Quantiphi), Noor Hassan (Amazon Web Services), and Liam Morrison (Amazon Web Services).
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