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Data Science & Analytics
Super Resolution
The Super Resolution demonstration uses Amazon SageMaker running the Real-ESRGAN and SwinIR upscaler AI models to enhance the resolution and visual quality of archived video content. Media companies, broadcasters, and content owners can leverage this functionality to breathe new life into your legacy video libraries. By applying advanced machine learning techniques, the demonstration upscales and sharpens low-resolution footage, transforming pixelated or blurry videos into crisp, high-definition content. This process unlocks new monetization opportunities by enabling the repurposing and distribution of archival footage across modern platforms and devices. Additionally, improved visual quality can enhance viewer engagement and satisfaction, making historical or nostalgic content more appealing to contemporary audiences.
Architecture
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Disclaimer
References to third-party services or organizations on this page do not imply an endorsement, sponsorship, or affiliation between Amazon or AWS and the third party. Guidance from AWS is a technical starting point, and you can customize your integration with third-party services when you deploy them.
The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.