Media2Cloud V4
Increasing the value of your media assets while optimizing your storage infrastructure in the cloud
Overview
Media libraries are challenging to monetize effectively with outdated and incomplete metadata. In order to leverage them better, Machine Learning can provide insights across various types of content, such as video, photos, articles, and documents. Moving content to the cloud can enable Machine Learning insights, which allows customers to drive more revenue from content while lowering total cost of ownership. On-premise media asset storage is expensive, inflexible, slow, and unreliable. Media content is storage-hungry, with even a small archive potentially representing 20+ TB. Migrating digital workloads to the cloud enables customers to lower costs by an average of 52% (5-yr TCO estimate). Cloud-based media storage also allows customers to take advantage of the latest innovations in media asset management and supply chain applications. So why aren’t all media workflows in the cloud? The standard response we hear is that the migration process costs too much and is too complex.
To help streamline and automate the migration process, AWS offers the Media2Cloud V3 solution; a serverless end-to-end ingest and analysis workflow to move your customers’ video assets and associated metadata to the cloud. During the migration, Media2Cloud analyzes video, images, and audio to automatically create valuable metadata using Amazon Rekognition, Amazon Transcribe, Amazon Textract, and Amazon Comprehend.
Watch the new series of demo videos on broadcast.amazon.com
Dynamic frame analysis
Using perceptual hash to understand whether adjacent frames are similar. It runs Amazon Rekognition Image APIs on frames that are significantly different. As a result, it saves 65% API requests on fast motion contents and saves up to 95% on steady content such as News content. Watch the quick demo on broadcast.amazon.com.
Auto face indexer
Auto index unrecognized faces during the analysis workflow. After faces are given (tagged) real names, the Auto Face Indexers propagate the name changes without a need of re-run the analysis workflow. Tag once; Faces recognized across all new and previously analyzed contents. Watch the quick demo on broadcast.amazon.com.
Scene change detection
Auto index unrecognized faces during the analysis workflow. After faces are given (tagged) real names, the Auto Face Indexers propagate the name changes without a need of re-run the analysis workflow. Tag once; Faces recognized across all new and previously analyzed contents. Watch the quick demo on broadcast.amazon.com.
GenAI plugins
Primarily for summarizing transcript into a short story, identify Genre, sentiment, and highlights of the content.
Ad break detection and contextual ads
Using Scene change detection, Amazon Transcribe, Amazon Rekognition Label API, AWS Elemental MediaConvert Loudness (LUFS) output to identify non-intrusive, frame accurary Ad break opportunities of the content. It also provides contextual IAB-like categories before and after each Ad break.
Shoppable experience
Last but not least – check out this special feature, AI/ML-enabled Shoppable Media Experience collaborated with IIC Monetization team. Watch the technical deep dive (55 minutes) on broadcast.amazon.com.
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