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What is NVIDIA DeepStream SDK?

NVIDIA's DeepStream SDK is a powerful toolkit designed for streaming analytics, utilizing GStreamer to enable AI-enhanced processing across a multitude of sensors that encompass video, audio, and image data. This SDK allows developers to build sophisticated stream-processing pipelines that effectively incorporate neural networks along with advanced features such as tracking, video encoding and decoding, and rendering, thus facilitating real-time analysis of varied data formats. DeepStream is integral to NVIDIA Metropolis, a holistic platform that transforms pixel and sensor data into actionable insights. It offers a flexible and responsive environment tailored to a range of industries, supporting numerous programming languages including C/C++, Python, and an intuitive UI via Graph Composer. By facilitating immediate understanding of intricate, multi-modal sensor information at the edge, it not only boosts operational efficiency but also provides managed AI services deployable in cloud-native containers orchestrated by Kubernetes. As a result, with the growing dependence on AI for informed decision-making, the functionalities of DeepStream become increasingly critical in maximizing the potential of sensor data. Moreover, the continuous evolution of the SDK ensures that it remains at the forefront of technological advancements, adapting to the changing needs of various sectors.

What is Mividi?

The Mividi IP Video Monitoring System (TSM100) emerges as a remarkable option for evaluating the video quality of IPTV services. IPTV providers typically acquire video content from a diverse array of sources, including satellite, fiber optic networks, terrestrial broadcasts, and locally generated footage. To meet the necessary bandwidth demands and ensure that the content is compatible with various user devices, these source streams are often subjected to processes of decoding and re-encoding. As a result, it is common for service providers to handle numerous programs along with different transport streams at their head-ends. Furthermore, the complexity of operations is compounded by the need for advertisement insertion and the provision of Electronic Program Guide (EPG) information. To ensure that they deliver the highest quality video experiences to their customers, providers must engage in continuous monitoring at several critical stages throughout the stream processing. This proactive approach to monitoring is vital not only for sustaining optimal performance but also for significantly improving viewer satisfaction and loyalty. By implementing such systems, providers can better adapt to viewer needs and respond swiftly to any potential issues that may arise.

Media

Media

Integrations Supported

C
C++
Helm
Kubernetes
NVIDIA Jetson
NVIDIA Metropolis
NVIDIA TensorRT
NVIDIA Triton Inference Server
PyTorch
Python
TensorFlow

Integrations Supported

C
C++
Helm
Kubernetes
NVIDIA Jetson
NVIDIA Metropolis
NVIDIA TensorRT
NVIDIA Triton Inference Server
PyTorch
Python
TensorFlow

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

NVIDIA

Date Founded

1993

Company Location

United States

Company Website

developer.nvidia.com/deepstream-sdk

Company Facts

Organization Name

Mividi

Date Founded

2011

Company Location

United States

Company Website

mividi.com/en-us/solutions/IPTVServiceMonitoring.aspx

Categories and Features

Deep Learning

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

Categories and Features

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