Sold by: MEGAZONECLOUD Corporation
Deployed on AWS
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DeepField-SR is a fixed functional hardware accelerator leveraging FPGA to offer the highest computational efficiency for Video Super Resolution.
Overview
Based on proprietary Neural Network trained with real world video data from the internet and fusing spatio-temporal information in multiple frames, DeepField-SR produces superior high resolution video quality.
As DeepField-SR is designed in scalable architecture, it can flexibly respond to various high resolution upscale request. Its runtime performance on single EC2 f1.2xlarge is 11-14fps to upscale video up to 4K resolution. The API is integrated within an FFmpeg workflow, meaning that simple command enables DeepField-SR acceleration and upscaling user input video.
Highlights
- Deep Neural Network trained with real world video data and suitable for resource limited FPGA
- Designed with High Level Synthesis
- Easy to update and upgrade per market demands
Details
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Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
AmazonLinux v2
Deployed on AWS
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Dimension | Cost/hour |
|---|---|
f1.2xlarge Recommended | $1.20 |
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Delivery details
64-bit (x86) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
Fixed CVE-2021-43527
Additional details
Usage instructions
Access the application via a browser at https://www.notion.so/Product-Access-Instruction-f59fbce5fabe4dc8b9bc25f45705253d
Resources
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Support
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If you need an assistance, please send an e-mail to info@blue-dot.com and we will support you as quickly as possible.
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Updated weekly
By MEGAZONECLOUD Corporation
By QuasiScience
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25
In Edit/Processing-Video
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In Media & Entertainment
AI generated from product descriptions
Hardware Acceleration Architecture
Fixed functional hardware accelerator leveraging FPGA for video super resolution with optimized computational efficiency
Neural Network Training Methodology
Proprietary Neural Network trained with real world video data from the internet and fusing spatio-temporal information across multiple frames
Scalable Processing Performance
Runtime performance of 11-14fps on single EC2 f1.2xlarge instance for upscaling video to 4K resolution with flexible architecture for various high resolution upscale requests
API Integration Framework
API integrated within FFmpeg workflow enabling video super resolution acceleration through simple command-line execution
Hardware Design Approach
Designed with High Level Synthesis methodology for resource-optimized FPGA implementation suitable for edge deployment
Pre-configured Video Processing Engine
FFmpeg with all dependencies pre-installed and configured for executing image and video conversion pipelines
Image Preparation Capabilities
Support for image resizing, format conversion, and cropping operations optimized for machine learning applications
Video Transcoding Support
Capability to process and transcode videos across multiple formats for media production workflows
Operating System Foundation
Built on hardened Linux distributions with security configurations applied
Multi-format Media Processing
Support for processing and converting images and videos across various file formats
Video Codec Support
Supports HEVC, SHVC, H.264, MPEG-2, and JPEG 2000 video codecs
Low-Latency Processing
Low-latency mode for real-time video processing and delivery
Advanced Packaging Formats
Supports advanced packaging including CMAF, ATSC3.0, and DRM
Wide Input Protocol Support
Enables full IP transition with support for TS, ST2022-6, and ST2110 input formats
Statistical Multiplexing
Optimized statistical multiplexing for efficient bitrate allocation across multiple channels
Standard contract
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