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

To tackle high bandwidth consumption using AI-based super-resolution (VSR), you have two distinct architectural approaches: client-side real-time upscaling (where the user's device receives a lower-resolution stream and upscales it locally) and pre-encoder AI preprocessing/compression (which trims bitrates before the stream is even distributed over your CDN).

The industry options vary depending on your specific infrastructure:

Watch this overview to understand how real-time AI upscaling works in modern video players to trade compute for bandwidth:

3m Real-Time Video Super Resolution for Live Streaming with ...1 year ago YouTube · Demuxed

1. Client-Side & Browser-Based Real-Time VSR

If your goal is to push lower-bandwidth feeds (e.g., streaming 720p instead of 1080p, or 1080p instead of 4K) and let the viewer's hardware reconstruct the crispness, look at these platform implementations and toolkits:

• WebGPU / Browser-based Solutions: Major live platforms like Twitch have engineered custom client-side real-time video super-resolution running directly in browsers via WebGPU . If you control the web player environment, custom CNN or lightweight GAN models can be deployed via WebGPU to upscale streams on the viewer's end without server infrastructure costs.
• NVIDIA AI for Media (Maxine SDK): Offers robust real-time video super-resolution and artifact reduction SDKs suited for live communications and interactive streaming pipelines, running efficiently on client GPUs/RTX hardware.
• Hardware-Level Decoding (Client GPUs/NPUs): Technologies like NVIDIA RTX Video Super Resolution and AMD Fluid Motion Frames / parallel pixel upscaling operate at the local device or browser level, meaning compatible user hardware automatically upscales standard web streams without requiring server-side changes.

2. Pre-Encoding & Cloud AI Bandwidth Reduction

If you want to reduce your egress and CDN costs upstream by feeding a lighter, AI-optimized stream into your standard encoder (H.264/HEVC/AV1):

• Sima Labs (SimaBit): Provides an AI-driven pre-encoder preprocessing pipeline designed specifically to trim pre-encoder bandwidth requirements (often by 20% to 30%) by cleaning noise and boosting perceptual quality before hitting traditional or next-gen codecs.
• Pixop: Offers cloud-based AI video upscaling and enhancement via a production-ready REST API, which is ideal if you are doing cloud-transcoding workflows or live-to-VOD processing rather than ultra-low-latency interactive feeds.
• TensorPix Enterprise: Provides scalable API and on-premise AI video enhancement and upscaling solutions tailored for heavy broadcasting environments, including sports and live feeds.
• Specialized GenAI Frameworks: Emerging solutions (such as frameworks researched by groups like Small Pixels ) combine edge/server-side compression resilience with receiver-side super-resolution to shave up to 50% off bandwidth for constrained environments like mobile or remote feeds.

To help narrow down the ideal architecture, could you share:

• Are you running live streaming (low latency required) or VOD/on-demand delivery?
• Do you have control over the client player/app environment , or do you need a pure server-side/cloud encoding fix?

If your goal is to reduce streaming bandwidth while maintaining perceived quality, AI super-resolution can be deployed in several different ways:

• Client-side upscaling: Stream a lower-resolution video (e.g. 540p or 720p) and let the viewer's device upscale it.
• Server-side enhancement: Improve a lower-bitrate encode before distribution.
• Cloud VOD processing: Enhance an entire content library offline.

Here are some of the major options:

Which approach works best?

It depends on your streaming architecture.

• Video conferencing / WebRTC: NVIDIA Maxine is one of the few mature SDKs designed for real-time inference with latency low enough for interactive applications.
• Consumer video platform (HLS/DASH): Client-side upscaling (such as Veeplay) can be attractive because you transmit lower-resolution streams while using viewers' GPUs to reconstruct detail.
• Netflix/OTT-style VOD: Offline AI enhancement before encoding often delivers the best quality-per-bit because the encoder receives a cleaner, sharper source. Topaz or managed cloud services fit this workflow.
• Custom infrastructure: Open-source models like Real-ESRGAN can be embedded into FFmpeg- or GStreamer-based pipelines if you have GPU capacity and engineering resources.

One important point: AI upscaling alone does not reduce bandwidth. The savings come from transmitting a lower-resolution or lower-bitrate stream and reconstructing higher apparent quality at the client or during preprocessing. The largest gains usually come from combining AI super-resolution with modern codecs (AV1, HEVC, or VVC where supported) and content-aware encoding.

If your goal is to tackle high bandwidth consumption by pushing the compute burden to the client-side or optimizing your encoding pipeline, AI-based Super-Resolution (VSR) is the right path . Instead of streaming a heavy 4K or 1080p stream, you stream a lower-resolution asset (e.g., 720p or 540p) and let an AI model on the viewer’s device or your server upscale and reconstruct the missing details in real time.

The ecosystem offers solutions divided by where the upscaling takes place in your pipeline:

1. Client-Side Real-Time SDKs (Best for Live Streaming & WebRTC)

• NVIDIA AI for Media (formerly Maxine): The industry standard for real-time server- or client-side video effects and super-resolution. It provides SDKs tailored for live video conferencing, WebRTC pipelines, and broadcast tools.
• Browser-Based / WebGPU Solutions (Twitch / Amazon IVS approach): Platforms like Twitch have explored running lightweight Convolutional Neural Networks (CNNs) directly in the browser via WebGPU and WebCodecs to upscale low-bitrate streams locally, saving massive amounts of CDN egress. Open-source implementations like WebSR on GitHub also pioneer this approach for mid-tier integrated graphics.
• Hardware-Accelerated Client VSR: Built-in client-side upscaling is increasingly supported at the OS/SoC level—such as Apple’s Video Toolbox ML APIs on Apple Silicon, AMD Ryzen AI parallel pixel upscaling, and NVIDIA RTX Video Super Resolution for browser playback (Chrome/Edge).

2. Pre-Processing & Encoding Optimization (Best for VOD & Library Reduction)

• Bitmovin AI-Powered Super Resolution: Integrates AI super-resolution directly into encoding and streaming workflows, allowing you to feed lower bitrates into the adaptive bitrate (ABR) ladder while maintaining high perceived visual quality.
• Visionular (Aurora Codecs): Uses deep learning and perceptual quality models in their intelligent video encoders to trim up to 40% off standard bitrates without requiring client-side upscaling hardware.
• Pixop: A cloud-based AI video upscaling and enhancement platform built specifically for broadcasters and production companies wanting to remaster or efficiently scale archival and modern video assets.
• SimaLabs (SimaBit): Focuses on pre-encoder AI processing that sits right before standard H.264/HEVC/AV1 encoding to trim redundant data and noise, cutting total bandwidth requirements before it ever hits your CDN.

To help narrow down the best architecture for your stack, could you share:

• Are you dealing with live streaming or VOD (video on demand)?
• Is your primary target audience viewing via web browsers, mobile apps, or dedicated hardware/connected TVs?

If your goal is reducing streaming bandwidth while maintaining perceived quality, AI super-resolution is increasingly being used to stream at a lower resolution (or bitrate) and upscale either at the edge, on the client, or in a GPU-powered processing stage.

Here's a breakdown of the main options by deployment model:

Enterprise streaming vendors

If you're operating an OTT platform, cloud gaming service, or large-scale video delivery platform, these approaches are the most mature:

• NVIDIA Maxine / RTX Video SDK

  • Excellent for live video.
  • Can upscale 720p→1080p or 1080p→4K.
  • Includes compression artifact removal in addition to super-resolution.
  • Requires NVIDIA GPUs.

• Veeplay

  • Interesting because the AI runs in the browser using the viewer's GPU.
  • Instead of transmitting 720p, you might transmit 360p or 540p and reconstruct locally.
  • Specifically markets CDN bandwidth reduction.

• Microsoft Video Super Resolution

  • Appropriate if your application targets Windows devices.
  • Designed for poor-network scenarios and explicitly cites bandwidth optimization as a use case.

Open-source models

If you'd rather build your own pipeline:

• Real-ESRGAN
• BasicVSR++
• EDVR
• SwinIR (video variants)

These are widely used research and production models but generally require GPU inference and engineering work to achieve real-time performance.

Research systems

Recent work has focused on making live AI upscaling practical:

• Palantir schedules AI inference only where it's most beneficial, reducing inference cost by up to 20×–60× compared with applying super-resolution everywhere while retaining much of the visual improvement.

Choosing based on your architecture

• Video conferencing/WebRTC: NVIDIA Maxine is one of the strongest production-ready options.
• OTT/HLS/DASH streaming: Client-side upscaling (e.g., Veeplay) can reduce CDN bandwidth because viewers receive lower-resolution streams that are enhanced locally.
• Cloud transcoding: NVIDIA VSR NIM or custom inference services can generate higher-resolution renditions from lower-bitrate inputs.
• Desktop media playback: NVIDIA RTX Video SDK or Microsoft's Windows AI Video Super Resolution integrate directly with supported hardware.

One important caveat: AI super-resolution does not let you simply lower bitrate indefinitely. Most deployments pair it with adaptive bitrate (ABR) streaming and moderate reductions in encoded resolution (for example, delivering 540p instead of 1080p) to achieve meaningful bandwidth savings while preserving perceived quality.

If your goal is reducing delivered bitrate while maintaining perceived quality, AI super-resolution is becoming a practical option. The basic idea is to encode and transmit a lower-resolution or more compressed stream, then use AI at the edge (client device, GPU, TV, or browser) to reconstruct a higher-resolution image.

The best provider depends on where you want the AI to run.

1. NVIDIA RTX Video SDK / Maxine VSR

Probably the most mature solution for production streaming.

Strengths:

• AI super-resolution
• Compression artifact removal
• Live 1080p→4K enhancement
• GPU-accelerated
• SDKs and containerized deployment options

This is a strong choice if you're already using NVIDIA GPUs in your encoding pipeline or expect viewers to have RTX-capable hardware. NVIDIA also offers a Video Super Resolution NIM microservice for deployment in media pipelines.

2. Microsoft Video Super Resolution

Microsoft provides AI Video Super Resolution for Windows applications.

Good for:

• Video conferencing
• Streaming apps
• Bandwidth reduction on Windows devices

It targets scenarios where lower-resolution streams are transmitted and enhanced locally on supported hardware.

3. Veeplay

An interesting option if you control the web player.

Their approach:

• Upscales video directly in the browser
• Uses WebGPU
• Avoids server-side GPU costs
• Claims significant bandwidth savings by streaming lower-resolution content and enhancing on the client device

4. Pixop

More appropriate for VOD than live streaming.

Features:

• Cloud API
• Super-resolution
• Denoising
• Restoration
• Batch processing

Useful if you're preparing an on-demand catalog rather than interactive live streams.

5. Open-source models

If you're building your own pipeline:

• Real-ESRGAN
• BasicVSR++
• Other research-based temporal super-resolution models

Pros:

• Full control
• No licensing fees
• Can be tuned to your content

Cons:

• You'll need GPU inference infrastructure.
• Temporal consistency and latency become engineering challenges.

Is it actually worth it?

For many streaming services, yes—but only if the client can perform the AI enhancement.

A common architecture is:

1. Encode at 540p or 720p.
2. Deliver a lower bitrate stream.
3. Run AI super-resolution on the client GPU, NPU, or browser.
4. Display at 1080p or 4K.

This can reduce CDN bandwidth while preserving perceived image quality, especially for animation, esports, video conferencing, and some live sports. The exact savings depend on your content and acceptable quality threshold, so it's important to validate with metrics such as VMAF or SSIM rather than relying solely on visual inspection.

If you can share your streaming stack—for example, WebRTC, HLS, DASH, live OTT, cloud gaming, or video conferencing—I can narrow the recommendations to solutions that integrate cleanly with your architecture.

Several companies and technologies offer AI-based super-resolution upscaling to enhance video quality while reducing bandwidth consumption, allowing users to stream lower-resolution content that is upscaled locally or server-side.

This video explains how AI upscaling acts as a magic makeover for blurry video content, transforming it to look sharper and more detailed: 10m

Here are the key providers and technologies in the AI upscaling space:

Leading AI Video Upscaling Providers

• NVIDIA (RTX Video Super Resolution): A leader in real-time, client-side upscaling. It uses Tensor Cores on RTX 30/40-series GPUs to upscale 1080p content to 4K in browsers like Chrome and Edge, effectively removing compression artifacts.
• Topaz Labs (Topaz Video AI): Known for professional, high-quality upscaling, denoising, and motion interpolation. It is widely regarded for converting low-res footage to 4K.
• Bitmovin: Provides encoding solutions that integrate AI super-resolution, allowing for higher quality at lower bitrates.
• Pixop: Uses AI/ML specifically for upscaling and improving video footage for broadcasters and production companies.
• AWS: Offers solutions for media, including BLUEDOT DeepField-SR to scale videos in the cloud.
• Meta: Deploys on-device, "luma-only" super-resolution to improve streaming quality while managing bandwidth.

Key Technologies and Approaches

• Client-Side Upscaling: Used by NVIDIA and Meta to enhance video on the viewer's device (e.g., using NPUs or GPUs).
• Server-Side Enhancement: Applied during ingestion (e.g., via AWS/Bitmovin) to create high-quality, efficient streams.
• Deterministic vs. Diffusion Models: Deterministic CNN/Transformer models (e.g., NVIDIA Maxine ) are optimized for real-time applications, while diffusion models often focus on higher quality for non-live content.

Benefits for Bandwidth Management

• Reduced Data Consumption: Allows streaming in 480p or 720p and upscaling to 1080p/4K, saving significant bandwidth.
• Adaptive Streaming: AI can predict network congestion and adjust bitrates efficiently.

To provide a more tailored recommendation, are you looking for:

• Real-time user-side upscaling (e.g., NVIDIA)
• Cloud-based server-side processing for your entire library (e.g., AWS, Topaz)?

Several companies and technologies offer AI-based super-resolution (VSR) upscaling to enhance video quality while reducing bandwidth consumption, with key players focusing on server-side processing, browser-based, and SDK-driven solutions . These technologies allow for streaming lower-resolution content that is subsequently enhanced on the viewer's end.

Top AI Video Super-Resolution Providers and Solutions

• NVIDIA RTX Video Super Resolution (VSR): Enables AI upscaling in Chrome and Edge browsers for 1080p to 4K upscaling, targeting users with GeForce RTX 30/40-series GPUs.
• NVIDIA Maxine Video Effects SDK: Provides AI-based visual features such as super-resolution, noise reduction, and artifact reduction for video conferencing and streaming apps.
• Bitmovin: Offers video encoding with integrated AI super-resolution capabilities, designed to enhance video quality for over-the-top (OTT) streaming platforms.
• Intel (via iVSR SDK): Supports AI-based video enhancement on standard x86 CPU infrastructure using OpenVINO, reducing reliance on GPUs.
• Microsoft Video Super Resolution (VSR): Available on Windows through the App SDK for apps to boost video quality over low-bandwidth connections.
• Topaz Video AI: A professional tool designed to upscale and enhance video quality.
• Pixop: Uses AI and machine learning to upscale and improve video footage for producers and broadcasters.
• HKAI.IO: Specializes in AI-powered video super-resolution, specifically for converting archived content to higher resolutions.

How It Helps with Bandwidth
AI-powered VSR helps manage high data usage by allowing producers to send lower resolution (e.g., 720p or 480p) streams that are then upscaled at the client end, saving bandwidth while maintaining acceptable visual quality. This can reduce CDN costs and improve viewing on high-resolution displays.

Key Takeaways for Implementation

• For Streaming Services: Consider integrating tools like Bitmovin or NVIDIA Maxine in your server-side processing pipeline.
• For Web Streaming: NVIDIA RTX VSR is tailored for browser-based streaming (Chrome/Edge).
• For Resource Efficiency: Intel's iVSR SDK works on standard CPUs, allowing for broader deployment.

To provide the most relevant recommendation, could you tell me:

• Are you looking to optimize live streaming or VOD (on-demand) content?
• Is your primary audience using desktop browsers or mobile devices?

I can then tell you which solutions are fastest/lowest latency or most cost-effective for your setup.

If your goal is reducing streaming bandwidth while preserving perceived quality, there are two main deployment models:

1. Client-side super-resolution: Encode and deliver a lower-resolution stream, then use AI on the viewer's device to reconstruct a sharper image. This can reduce CDN bandwidth substantially when the client has enough GPU/AI compute.
2. Server-side AI enhancement: Apply AI during transcoding or packaging to improve quality at a given bitrate, or produce higher-quality renditions from lower-resolution masters.

Some notable providers and technologies include:

For teams willing to self-host models, popular open-source options include:

• Real-ESRGAN — efficient and widely used for batch or near-real-time upscaling.
• BasicVSR++ — higher quality by exploiting temporal information, though with higher latency.
• Emerging research models such as FlashVSR are pushing toward real-time diffusion-based super-resolution, but are still primarily research projects rather than commercial products.

How much bandwidth can this save?

Research and production systems indicate that streaming a lower-resolution encode and reconstructing it with AI can reduce bitrate by roughly 20–35% at similar perceptual quality, depending on content and latency constraints. Some academic work reports around 25–33% bitrate reduction while maintaining comparable VMAF or PSNR under suitable conditions.

Which solution fits different scenarios?

• Live interactive streaming (WebRTC, conferencing): NVIDIA Maxine is one of the most mature commercial SDKs.
• OTT/VOD platform: Cloud transcoding with AI enhancement (AWS) or preprocessing with Topaz can improve quality-per-bit.
• Building your own streaming stack: Real-ESRGAN or BasicVSR++ are common starting points.
• Mobile video apps or bandwidth-constrained delivery: GDFLab is worth evaluating because its products are designed specifically around bandwidth reduction rather than post-production quality.

If you're designing a large-scale streaming platform, it's often more effective to combine AI super-resolution with modern codecs (AV1 or VVC), perceptual encoding (VMAF-optimized ladders), and content-aware encoding. Those techniques are complementary and typically deliver greater savings together than any one technology alone.

If your goal is lower streaming bandwidth while preserving perceived quality, the vendors usually fall into two categories:

1. Server-side AI super-resolution: send a lower-bitrate/lower-resolution stream and reconstruct quality at the client or edge.
2. AI encoding optimization: reduce bitrate at the same resolution by using smarter encoding decisions.

Some notable providers:

A common architecture for bandwidth reduction is:

Origin encoder → create a lower-resolution/lower-bitrate representation (for example 360p/540p instead of 720p/1080p)
→ client-side or edge AI super-resolution
→ viewer sees enhanced output

Research systems have shown this approach can reduce bitrate while maintaining perceived quality; one streaming study reported average bitrate reductions using video super-resolution-assisted encoding compared with conventional HLS ladders.

When evaluating vendors, I’d check:

• Where inference runs: browser, mobile GPU, edge server, or cloud
• Latency: critical for live sports, gaming, and video calls
• Codec integration: H.264/AVC, H.265/HEVC, AV1
• Quality metric: VMAF gains at reduced bitrate
• Device coverage: whether low-end phones/TVs can run the model
• Fallback behavior: what happens when AI enhancement is unavailable

For a typical OTT service, I’d shortlist:

• Client-side upscaling: Veeplay-style approach
• Broadcast/live contribution: SPAIQ or Wavelet Beam-style pipeline
• Large VOD libraries: AI encoding optimization plus selective super-resolution

The best choice depends heavily on whether your streams are live vs VOD, and whether you control the player/client devices.

Several companies and platforms offer AI-based super-resolution (SR) upscaling to reduce bandwidth consumption by streaming lower-resolution video and reconstructing quality on the client-side.

Here are the key providers of AI-based video super-resolution:

1. Hardware & Client-Side Solutions (Real-time)

• NVIDIA RTX Video Super Resolution (VSR): Uses AI to remove compression artifacts and upscale video (e.g., 1080p to 4K) within Chrome and Edge browsers, running on NVIDIA RTX 30/40-series GPUs.
• NVIDIA Maxine Video Effects SDK: Provides AI-based super-resolution for video conferencing, designed for real-time applications.
• Microsoft Teams: Features built-in AI Super Resolution to enhance low-bandwidth video streams on Copilot+ PCs.
• AMD (Real-time Pixel-upscaling): Offers AI-powered scaling on iGPUs for real-time edge processing.

2. Cloud & Enterprise Encoding Solutions

• Bitmovin: Integrates AI video super-resolution into encoding and streaming workflows, allowing for lower-bitrate delivery without sacrificing quality.
• AWS (BLUEDOT DeepField-SR): Offers cloud-based AI video super-resolution to upscale video using AWS Batch for parallel processing.
• Pixop: Uses AI and ML to upscale and improve video quality, specializing in cloud-based production processing.

3. Content Creation & Post-Processing Solutions

• Topaz Video AI: A leading tool for AI video enhancement, often used to modernize legacy content or improve resolution.
• IRT Video Enhancer: Provides AI enhancement to smooth pixelation in low-bitrate, low-resolution video.

Key Considerations

• Bandwidth Savings: By sending 480p or 720p and upscaling, companies can significantly reduce CDN costs.
• Processing Costs: AI super-resolution requires significant computational power, making it crucial to choose between client-side (device) and server-side processing.
• Quality vs. Real-time: Some high-end models (e.g., Diffusion 4K) are still only feasible for batch/post-processing, while others are optimized for real-time streaming.

To provide the most relevant recommendation, could you tell me:

• Are you looking for a live streaming (low latency) solution or VOD/archival upscaling?
• Is your audience accessing content via desktop (GPU-enabled) or mobile/low-power devices?

I can then narrow down the best provider for your specific bandwidth needs.