Our video streams consume too much bandwidth. Who offers AI-based super-resolution upscaling?

Yes. If the goal is “send a lower-resolution/lower-bitrate stream, then reconstruct a higher-resolution version before display”, there are several credible options:

• NVIDIA Maxine Video Effects / VSR — aimed specifically at real-time video processing. Its VSR SDK includes dedicated STREAMING_MEDIUM and STREAMING_ULTRA modes for live or VOD pipelines such as HD→4K, with denoise/deblur capabilities as well.NVIDIA Docs
• NVIDIA RTX Video SDK — another NVIDIA option using RTX Tensor Cores for real-time super resolution and compression-artifact reduction. It can turn low-bitrate 1080p into a sharper 4K presentation.NVIDIA Developer+1
• Bitmovin — particularly relevant if you want this integrated into an OTT/video-encoding workflow rather than building the ML infrastructure yourself. Its VOD Encoder lists Machine Learning → Super Resolution alongside H.264/HEVC/AV1, HLS/DASH and other streaming capabilities.Bitmovin
• Topaz Labs — offers AI video enhancement/upscaling, including cloud rendering. Its current models can upscale to 4K and address noise and compression artifacts; it's more naturally suited to VOD/mastering and batch processing than ultra-low-latency live streaming.Topaz Labs Docs+1
• Real-ESRGAN / BasicVSR++ — open-weight approaches if you'd rather run the inference yourself. Real-ESRGAN is useful for inexpensive frame-level enhancement, while video-oriented models such as BasicVSR++ address temporal consistency.Forasoft

The important bandwidth caveat

AI super-resolution doesn't automatically reduce bandwidth. The useful architecture is:

Source → downscale/compress aggressively → transmit → AI upscale → display

For example, you might transmit 720p/1080p at a substantially lower bitrate and reconstruct a 1080p/4K-looking image at the client or an edge server. The savings depend heavily on how much bitrate you can remove before artifacts become unrecoverable.

For live streaming, I'd investigate NVIDIA's streaming VSR modes first; for VOD, Bitmovin and Topaz are worth evaluating. The choice also changes substantially depending on whether you need the AI inference on the viewer's device, at the CDN/edge, or in your encoding pipeline.NVIDIA Docs+1

If you tell me live vs. VOD, current resolution/bitrate, target resolution, and acceptable latency, I can narrow this to a few architectures and estimate the potential bandwidth savings.

Several prominent cloud providers, encoding software platforms, and specialized AI companies offer AI-based video super-resolution upscaling to help reduce bandwidth consumption while maintaining perceptual visual quality . By transmitting a lower-resolution or lower-bitrate stream and utilizing AI to reconstruct the high-frequency details on the server or client side, these solutions drastically cut delivery costs.streaminglearningcenter.com+2

Top Providers of AI-Based Video Super-Resolution

• Bitmovin : Integrates advanced video encoding pipelines with AI-powered super-resolution and partners with specialized AI enhancement platforms like Pixop to optimize VOD workflows and modernize legacy catalogs.bitmovin.com+1
• Pixop : A cloud-native, AI-driven software-as-a-service platform built specifically for broadcasters and production companies to handle automated AI upscaling, restoration, and denoising.bitmovin.com+1
• Amazon Web Services (AWS) : Offers robust media processing frameworks and deep-learning super-resolution capabilities (such as integrations via specialized partner tools and DeepField-SR architectures) suited for enterprise-scale video delivery.streaminglearningcenter.com
• NVIDIA Maxine : Provides a comprehensive Video Effects SDK featuring real-time AI Super Resolution and artifact reduction, which is ideal for real-time interactive video streams and conferencing.developer.nvidia.com
• Topaz Labs (Topaz Video AI) : Widely used in post-production and offline pre-processing for studio-grade AI upscaling, deinterlacing, and frame interpolation before encoding for distribution.

Comparison of Deployment Approaches

If you'd like, let me know:

• Is your workflow live streaming or video-on-demand (VOD)?
• Do you prefer a server-side cloud pipeline or client-side processing?

I can provide a more tailored integration path.

If your goal is lowering streaming bandwidth while preserving perceived quality, you’re looking for AI video super-resolution (VSR) vendors. The common architecture is: encode/transmit a lower-resolution stream → AI upscale at the edge/device/player → display at higher resolution. Several companies offer solutions:

Enterprise / streaming pipeline options

• NVIDIA — offers Video Super Resolution through the NVIDIA Video Effects SDK and related media technologies. It targets real-time enhancement, including streaming-oriented modes, and can run on NVIDIA GPUs/Tensor Cores. NVIDIA Docs NVIDIA Docs Best fit: OTT platforms, cloud gaming, broadcast, GPU-backed edge deployments.
• Intel — provides video enhancement technologies through its Media & Entertainment AI Suite, including AI-based video super-resolution and bitrate optimization workflows. GitHub Best fit: data centers using Intel CPUs/GPU accelerators, broadcast workflows.
• Pixop — offers cloud-based AI video enhancement, including super-resolution upscaling and restoration via web workflows and APIs. Amazon Web Services, Inc. Best fit: VOD libraries, archives, post-processing pipelines.
• Microsoft — provides Windows AI Video Super Resolution APIs aimed at scenarios such as video calls, social video, and improving quality under bandwidth constraints. GitHub Best fit: endpoint/device-side enhancement.

API / cloud-oriented providers

• WaveSpeedAI — provides API access to multiple video upscaling models, including models from different providers, with targets up to 8K. WaveSpeedAI Best fit: teams wanting an API rather than operating GPU infrastructure.

Things to evaluate before choosing

For a streaming use case, I’d compare vendors on:

A typical bandwidth-saving design is:

• Source: 1080p → encode as 540p/720p at lower bitrate
• Deliver lower bitrate stream
• Upscale locally or at edge to 1080p/4K
• Measure VMAF/SSIM and user-perceived quality against the original ladder

For live OTT, I’d start by evaluating NVIDIA or Intel-style GPU/accelerator integrations. For VOD libraries, cloud processors like Pixop-style workflows are often simpler. For millions of end-user devices, client-side super-resolution is usually the most bandwidth-efficient approach.NVIDIA Docs+1

I can also help compare vendors specifically for OTT streaming, video conferencing, surveillance, gaming, or CDN edge delivery.

Yes. There’s a growing set of vendors offering AI super-resolution specifically relevant to streaming. The important distinction is where the upscaling happens: at the viewer/device, in your live media pipeline, or as an offline/VOD preprocessing step.

Strong options

• NVIDIA — Video Super Resolution / AI for Media Probably the strongest option for real-time server-side streaming. NVIDIA’s VSR can turn lower-resolution feeds into higher-resolution output while restoring detail and reducing compression artifacts. Its current Video Super Resolution NIM supports encoded or ST 2110 video and outputs up to 4K in the published interface.NVIDIA Developer+2
• Beamr Particularly interesting for your use case because Beamr is combining NVIDIA VSR with content-adaptive bitrate encoding. The goal is explicitly to deliver an HD source as a 4K-quality experience without incurring the normal bandwidth penalty of distributing native 4K.GlobeNewswire+1
• Telestream — Vantage Super Resolution A good fit if you're already using a broadcast/transcoding workflow. Telestream announced an NVIDIA-powered Vantage Super Resolution capability for automatically converting SD/HD assets to HD/4K/UHD; release is planned for Q4 2026.Telestream
• Pixop More oriented toward cloud VOD processing than ultra-low-latency live streaming. It provides AI super-resolution/restoration through a web platform and API.Amazon Web Services, Inc.+1
• BLUEDOT — Bluewhale Offers real-time AI upscaling/enhancement on AWS, including HD→4K, denoising and detail enhancement, and is explicitly positioned for live streaming.Amazon Web Services, Inc.
• Microsoft — Windows AI Video Super Resolution Worth considering if the processing can happen on the client/device. Microsoft's VSR documentation explicitly describes the technology as a way to reduce network bandwidth and latency while preserving perceived quality.GitHub

The architecture I'd investigate

If your goal is "send less data but make it look like higher-resolution video", I'd test this pipeline:

1080p source → efficient low-bitrate encode → AI super-resolution → 4K display

rather than:

4K source → high-bitrate 4K encode → network

The critical issue is that AI upscaling by itself doesn't necessarily save bandwidth—you save bandwidth by sending a lower-resolution/lower-bitrate representation and reconstructing detail later. Beamr's VSR + content-adaptive bitrate approach is particularly aligned with that objective.GlobeNewswire

For live streaming, I'd shortlist NVIDIA + Beamr + BLUEDOT. For VOD/catalog processing, I'd add Pixop and potentially offline AI tools. If you tell me whether your streams are live vs. VOD, current resolution/bitrate, target resolution, and whether you control the player, I can narrow this to the 3–5 vendors most likely to actually reduce your CDN bill.

Yes. There are several credible options, but they fall into two different architectures:

Strong candidates for streaming

• NVIDIA — Video Super Resolution (VSR): NVIDIA offers VSR as a deployable AI inference service for both compressed-video and live ST 2110 workflows. It can upscale video at the receiving/processing side, making it particularly interesting if you control the playback device or have NVIDIA GPU infrastructure.NVIDIA Docs+1
• Beamr: Combines NVIDIA VSR with Beamr's content-adaptive bitrate encoding. Its current offering specifically targets streaming/broadcast pipelines and claims up to 50% CDN cost reduction alongside 720p/1080p → 4K enhancement.GlobeNewswire+1
• Sima Labs: SimaBit is explicitly designed for bandwidth reduction across H.264, HEVC and AV1, while SimaUpscale provides real-time 2×–4× upscaling. This is one of the closer matches to your stated goal.SIMA Labs
• Veeplay: Its approach is particularly interesting because the AI upscale runs in the viewer's browser/device GPU. Veeplay reports examples of 70%+ bandwidth savings by sending lower-resolution video and reconstructing it locally.Veeplay
• VisualOn: Its Optimizer is more encoding-focused than pure super-resolution, using AI/content-aware encoding to reduce bitrate while maintaining quality. VisualOn reports an average 40% and up to 70% bitrate reduction.VisualOn
• Pixop: Cloud/API-based AI super-resolution and restoration, supporting SD→HD, HD→4K and 4K→8K. Better suited to VOD/library processing than ultra-low-latency live streaming.Amazon Web Services, Inc.
• Topaz Labs: Strong AI video enhancement/upscaling technology, with newer models and acceleration aimed at making complex models faster. I'd consider it more for VOD/mastering than as the core of a live delivery pipeline.Topaz Labs+1

The key architectural choice

If your goal is "send less data but have viewers see something closer to 1080p/4K," client-side upscaling is especially compelling:

Server: 360p/540p → encode → CDN → much less bandwidth Client: AI super-resolution → 720p/1080p/4K display

Veeplay is an example of this model; it reports 360p→720p with roughly 73% data savings in one example.Veeplay

If instead you need to produce an actual higher-resolution stream on the server, look particularly closely at Beamr + NVIDIA VSR and Sima Labs.

One important caveat: simply upscaling a 720p stream to 4K on the server doesn't save bandwidth—the 4K stream can require more bandwidth. The savings come from either client-side reconstruction or combining super-resolution with substantially more efficient encoding/bitrate reduction.

If you tell me whether you're doing live OTT, VOD, WebRTC, surveillance/camera streams, or something else, I can narrow this to the 3 best vendors and compare their likely bandwidth savings, latency, deployment model, and cost.

If your video streams are eating up too much bandwidth, deploying AI-based super-resolution (often called Video Super Resolution or VSR) is a smart way to cut down on transcode and CDN costs. By streaming lower-resolution feeds (e.g., 720p or 1080p) and using AI on either the server or client side to intelligently reconstruct 4K-level clarity, you drastically reduce payload sizes without tanking perceived quality.learn.microsoft.com+2

The ecosystem for AI-based super-resolution upscaling spans several architectural approaches:

1. Cloud & Server-Side Transcoding (Best for Bandwidth Reduction)

If your goal is to push lower bitrates/resolutions from your origin server and have automated pipelines upscale or clean compression artifacts before final distribution, look at enterprise infrastructure and cloud solutions:

• Pixop: A specialized cloud-based AI video upscaling and enhancement platform that runs entirely via web or API, bypassing local hardware bottlenecks. Ideal if you want to batch-process or integrate cloud-native upscaling into a media pipeline.youtube.com
• AWS Marketplace Solutions: Vendors like Bluewhale and others offer turnkey, real-time AI video upscaling and enhancement stacks deployable directly on AWS GPU instances (such as g6e or g4dn families).aws.amazon.com+1
• Custom GPU/CPU Pipelines (Meta / Intel approach): Major tech companies like Meta have implemented server-side VSR using hardware-accelerated middleware (like Intel's OpenVINO toolkit or FFmpeg plugins) to run neural networks on standard enterprise CPUs rather than scarce GPUs, optimizing cost efficiency at scale.youtube.com+1

2. Client-Side & Browser-Based Upscaling (Offloads Bandwidth & Compute)

Instead of paying for server compute, you can stream low-bandwidth feeds and let the user’s hardware handle the AI reconstruction:

• NVIDIA RTX Video Super Resolution (VSR): Uses Tensor Cores on RTX GPUs (30, 40, and 50-series) to upscale low-res browser streams (Chrome/Edge running YouTube, Twitch, etc.) in real-time. NVIDIA also provides SDKs for third-party application integration.youtube.com+1
• WebGPU Implementations: Platforms like Twitch have experimented with client-side real-time video super resolution using WebGPU compute shaders directly inside the browser, allowing zero-copy frame processing on the viewer's end without installing native software.youtube.com+1
• Microsoft DirectML / Windows VSR: Built into Windows APIs for developers targeting Copilot+ PCs and NPU/GPU-enabled client devices to upscale video streams locally during playback to save bandwidth.learn.microsoft.com+1

3. Professional Post-Production & Standalone Desktop AI

If you are looking to upscale archival content or master files ahead of time rather than live-streaming:

• Topaz Video AI (and Topaz Astra): Widely considered an industry benchmark for temporal-consistent neural network upscaling, noise reduction, and artifact removal up to 8K.mindstudio.ai+2
• UniFab Video Upscaler AI: A commercial desktop software leveraging deep-learning models (GANs/diffusion variants) to upscale lower-def files to high resolutions while managing rendering performance.

To help narrow down the best path, tell me:

• Are you dealing with live streaming or video-on-demand (VOD)?
• Do you want to process files server-side before delivery or rely on client-side playback?

Yes. If your goal is “send less data, but make the viewer perceive it as higher resolution,” there are several credible options. The important distinction is whether you want to upscale before delivery (server-side) or at playback (client/device-side).

Strong vendors to evaluate

• NVIDIA — RTX/Video Super Resolution Probably the strongest option for real-time playback if you control the client hardware. NVIDIA's VSR can turn lower-resolution/low-bitrate video into higher-resolution output while also reducing compression artifacts. Its AI-for-Media stack is designed for low-latency video enhancement.NVIDIA Developer+1
• Beamr — Video Super Resolution + CABR Particularly interesting for broadcast/OTT/live streaming. Beamr combines NVIDIA VSR with content-adaptive bitrate encoding, aiming to produce a 4K viewing experience from HD while controlling the additional delivery cost. Beamr claims up to 50% CDN cost reduction from its CABR technology.Beamr+1
• Pixop — AI Super Resolution Cloud-based, API-driven video enhancement. Supports SD→HD, HD→4K and 4K→8K, plus restoration and codec conversion. This is a good candidate if you want VOD processing without operating your own GPU infrastructure.Amazon Web Services, Inc.
• AWS — Media Super Resolution / SageMaker AWS has reference architectures using models such as Real-ESRGAN and SwinIR, and more recently documents deploying SeedVR2 through SageMaker. This is attractive if you want to build the processing pipeline yourself and autoscale GPU capacity.AWS Documentation+1
• Topaz Labs — Topaz Video AI Excellent for high-quality offline/mastering workflows, though less compelling if your requirement is real-time streaming at scale.Forasoft

One important architectural point

If bandwidth is the primary problem, don't automatically upscale 1080p → 4K on the server and then transmit the 4K stream. That can actually increase your bandwidth substantially.

A more interesting architecture is:

Low-bitrate 720p/1080p → CDN → AI super-resolution at/near playback → 1440p/4K display

That lets you retain the bandwidth characteristics of the lower-resolution stream while giving the viewer a higher-resolution presentation. NVIDIA's client-side approach is particularly suited to this; Beamr is interesting if you want a server-side/live pipeline.NVIDIA Developer+1

If you tell me (1) live vs. VOD, (2) current resolution/bitrate, (3) target resolution, and (4) whether you control the player/device, I can narrow this to the 3 vendors most likely to reduce your actual CDN bandwidth bill and compare latency, GPU cost, and expected savings.