
3Q is an API-first video infrastructure for developers and engineering teams who want direct control over their media backend. A REST video API and native player SDKs give you programmatic access to hosting, ingestion, encoding, live streaming, video-on-demand, and delivery, so you can build video portals, streaming apps, or OTT backends on a single European platform.
The stack is transparent by design. 3Q supports adaptive bitrate streaming over HLS and DASH with mixed HEVC and AVC codecs and automatic Live-to-VoD. Delivery runs over a proprietary global CDN, encryption, and HTTP/2 over TLS 1.3. The Cookie- and Consent-free HTML5 Video Player is barrier-free in accordance with WCAG 2.1/BITV 2.0 and needs no consent layer. Video AI exposes speech-to-text transcription, automatic subtitles, translation, and chapter markers through the same API, and integration fits your existing pipeline and video workflows.
What sets 3Q apart is ownership. 3Q runs on its own independent European video infrastructure, so your data stays in the EU and under German jurisdiction. 3Q is GDPR-compliant and all processes are ISO/IEC 27001 certified, with modular pay-as-you-go pricing and 24/7 support from real video experts.
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You can watch videos from anywhere, anytime, even offline. It's easy to download: simply copy the link from your browser, and then click 'Paste Link" in the application. You can save full playlists and channels on YouTube in high-quality and other video or audio formats. Download your YouTube Mix, Watch Later and Liked videos as well as private YouTube playlists. Receive new videos from your favorite YouTube channels automatically. You can feel the action around you with virtual reality videos. To experience the amazing VR experience in 360deg, download 360deg videos. You can bypass any restrictions placed by your Internet service provider to bypass your school firewall or workplace firewall. To access YouTube and other sites, set up an in-app proxy connection.
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Google Cloud Vision AI
Harness the power of AutoML Vision or leverage pre-trained Vision API models to extract meaningful insights from images stored in the cloud or at the network's edge, allowing for emotion detection, text interpretation, and much more. Google Cloud presents two advanced computer vision solutions that utilize machine learning to provide top-notch prediction accuracy for image analysis. You can streamline the creation of bespoke machine learning models by simply uploading your images, using AutoML Vision's intuitive graphical interface to train these models, and fine-tuning them for optimal performance in terms of accuracy, latency, and size. Once perfected, these models can be seamlessly exported for use in cloud applications or on various edge devices. Additionally, Google Cloud’s Vision API grants access to robust pre-trained machine learning models via REST and RPC APIs. You can easily assign labels to images, categorize them into millions of pre-existing classifications, identify objects and faces, interpret both printed and handwritten text, and enhance your image catalog with rich metadata for deeper insights. This combination of tools not only simplifies the image analysis process but also empowers businesses to make data-driven decisions more effectively.
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Amazon Rekognition
Amazon Rekognition simplifies the integration of image and video analysis into applications by utilizing reliable, highly scalable deep learning technology that doesn’t necessitate any machine learning knowledge from users. This powerful tool allows for the identification of various elements such as objects, individuals, text, scenes, and activities within images and videos, alongside the capability to flag inappropriate content. Moreover, Amazon Rekognition excels in delivering precise facial analysis and search functions, which can be employed for diverse applications including user authentication, crowd monitoring, and enhancing public safety.
Additionally, with the feature known as Amazon Rekognition Custom Labels, businesses can pinpoint specific objects and scenes in images tailored to their operational requirements. For instance, one could create a model designed to recognize particular machine components on a production line or to monitor the health of plants. The beauty of Amazon Rekognition Custom Labels lies in its ability to handle the complexities of model development, ensuring that users need not possess any background in machine learning to effectively utilize this technology. This makes it an accessible tool for a wide range of industries looking to harness the power of image analysis without the steep learning curve typically associated with machine learning.
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