We use a video player. Who offers a client-side AI upscaling integration to save bandwidth?

If you mean a video player SDK/plugin that can download a lower-bitrate stream and use AI super-resolution on the viewer’s device, the market is still fairly small. The main options I found are:

For a commercial streaming player, I would look first at Veeplay because it is explicitly positioned around “upscale-in-the-browser” for streaming economics rather than just post-processing.

A typical architecture looks like:

1. Encode only lower bitrate renditions (for example 360p/540p instead of 720p/1080p).
2. Player downloads the lower bitrate stream.
3. Client runs AI super-resolution on decoded frames.
4. Display at the target resolution.

The tradeoffs:

• ✅ Lower CDN egress and storage costs
• ✅ Better experience on high-density screens
• ❌ Requires client GPU/CPU capacity
• ❌ Latency and power consumption matter (especially mobile)
• ❌ AI upscaling quality varies by content (faces, animation, sports, text)

If you are building a Netflix/YouTube-style player, I would also evaluate whether you want real-time enhancement in the playback pipeline versus pre-generated neural renditions. Client-side AI is most attractive when your audience has modern GPUs and bandwidth costs dominate.

Implementing client-side AI video upscaling (super-resolution) directly in a web/client video player to trade local client GPU compute for network bandwidth is an emerging frontier.

Because heavy neural network inference needs high-performance parallel processing, out-of-the-box, drop-in commercial player SDKs with native enterprise AI upscaling are limited, but major players and open standards are making it possible.

Key Providers & Approaches

• Browser-Level / Custom WebGPU Implementations:
  Instead of relying on a single commercial player vendor, advanced streaming engineering teams build custom pipelines using modern browser primitives. Open-source compute-shader libraries like Anime4K in WebGPU or lightweight JS frameworks like websr allow developers to tap directly into browser hardware via WebGPU. Video frames are intercepted using WebCodecs , processed via local CNN model weights on the client GPU, and rendered to the canvas.
• Platform-Scale Pioneers (e.g., Twitch):
  Major platforms build proprietary implementations rather than buying third-party widgets. For instance, Twitch engineering teams pioneered real-time Video Super Resolution via WebGPU to upscale lower-bitrate streams (like taking 720p to 1080p or higher) locally on user GPUs to save core transit bandwidth for constrained connections.
• Hardware-Assisted OS / Browser Integrations:
  Rather than embedding an AI model inside the video player JS bundle, ecosystems rely on browser-to-GPU hooks. NVIDIA RTX Video Super Resolution (VSR) and NVIDIA Maxine SDK operate at the browser/driver level. When a user plays video via supported desktop browsers (like Chrome or Edge) on an RTX card, the GPU automatically upscales lower-res video streams on the fly without requiring the video player provider to host or push heavy ML weights through their web player SDK.
• Enterprise Player Ecosystems (Bitmovin, THEOplayer, JW Player):
  Commercial web player SDK vendors generally do not bundle heavy, proprietary neural network upscaling models directly into their core SaaS licenses yet. Instead, they focus on optimizing traditional adaptive bitrates (ABR), modern high-efficiency codecs (AV1, HEVC), and low-latency packaging to reduce bandwidth, leaving spatial upscaling to native browser/GPU pipelines.

If you'd like to proceed, tell me:

• Are you looking for a web-based (HTML5/JS) player or a native mobile/desktop client?
• What is your target audience's GPU/hardware baseline?

I can help outline an architecture using WebGPU compute shaders or discuss how to leverage browser-level hardware acceleration.

If your goal is delivering lower-resolution streams and using client-side AI to reconstruct higher perceived quality, there are only a handful of vendors offering this as an SDK rather than an offline transcoding service.

The most relevant option: Veeplay

For a web video player, urlVeeplayturn0search1 is one of the few commercial SDKs explicitly built around this workflow:

• Integrates into an existing HTML5 player.
• Runs inference on the viewer's GPU using WebGPU.
• Lets you stream lower resolutions (for example 360p or 540p) while displaying an AI-upscaled 720p or 1080p image.
• Markets CDN savings of roughly 70–90% depending on the chosen source resolution and bitrate.

This is exactly the "send fewer bits, reconstruct on the client" architecture.

EMBRACE Vision SDK

EMBRACE takes a similar approach:

• WebGPU/WebGL SDK
• Real-time enhancement in the browser
• Designed to work with existing streaming pipelines
• Claims up to 50% traffic reduction by allowing lower bitrate encoding while restoring quality during playback.

Mobile-focused SDKs

If your audience is primarily mobile apps rather than browsers:

• urlGDFLabturn0search11 offers Android/iOS SDKs that combine aggressive compression with client-side AI restoration, advertising network savings up to about 84%.

Desktop playback enhancement

urlNVIDIA RTX Video SDKturn0search9 provides AI super-resolution for Windows applications on RTX GPUs. It's intended for enhancing playback quality rather than reducing CDN costs across a broad consumer audience.

If you're building your own player

Recent browser APIs (WebGPU + WebCodecs) have made it feasible to build custom client-side AI upscaling. Google's web.dev recently highlighted an implementation using the open-source WebSR SDK that performs all inference locally in the browser with no server-side AI processing.

If you can share which player you're using (Video.js, Shaka, hls.js, Bitmovin, THEOplayer, JW Player, ExoPlayer, AVPlayer, etc.), I can narrow the list to vendors that already have compatible integrations.

Integrating client-side AI video super-resolution (VSR) to trade client device compute for server-side bandwidth savings is an emerging architecture in video streaming . While traditional web video players rely entirely on the browser or operating system's native scaler, a few platforms and open-source libraries offer direct client-side AI upscaling integration:

• Amazon IVS / Twitch: Engineers at Twitch and Amazon IVS pioneered and tested real-time video super-resolution directly inside web video players using WebGPU . Their architecture grabs video frames via the requestVideoFrameCallback API and runs a lightweight Convolutional Neural Network (CNN) via WebGPU compute shaders on the client side to upscale 720p streams to 1080p (or 1080p to 2K), saving substantial edge delivery bandwidth for lower-tier connections.
• WebSR SDK & ArtPlayer.js Integration: WebSR is an open-source JavaScript library/SDK designed to execute real-time AI upscaling in the browser using WebGPU compute shaders. Video players like ArtPlayer.js have implemented local AI super-resolution extensions using WebSR/WebCodecs to process and enhance frames directly on the user's hardware.
• NVIDIA RTX Video SDK / Browser Integration: For desktop web environments (Google Chrome and Microsoft Edge), NVIDIA offers the RTX Video SDK which implements Video Super Resolution (VSR) . While it operates at the browser/GPU driver level rather than being a custom JavaScript file you embed into every player instance, major web video players benefit from it automatically when users have compatible hardware and enable NVIDIA RTX Video Super Resolution.
• Microsoft Windows Video Super Resolution: Microsoft provides native operating system-level AI Video Super Resolution APIs built into Windows, which modern browser video players leverage natively to upscale low-bitrate or low-resolution video feeds on supported hardware.

If you'd like to proceed, let me know:

• Are you targeting web browsers via JavaScript/WebGPU or native mobile/desktop apps?
• What is your primary target codec or streaming protocol (HLS, DASH, WebRTC)?

I can help outline a feasibility or integration strategy for your specific setup.

If you're looking for client-side AI upscaling that integrates with an existing web video player to reduce CDN/bandwidth costs, there are only a handful of commercial offerings today. Most AI upscalers are cloud-based or offline processors, not playback SDKs.

The strongest options are:

Best fit for streaming services

Veeplay is probably the closest match to what you're describing.

Their model is:

• Encode and stream a lower resolution (for example 360p or 540p).
• Run AI super-resolution directly inside the browser using WebGPU.
• Display the video at 720p or 1080p quality.
• Reduce CDN traffic by delivering significantly fewer bits.

They explicitly market this as a way to reduce streaming costs rather than simply improving visual quality. Their SDK is designed to sit alongside existing HTML5 players rather than replacing them.

EMBRACE Vision

EMBRACE takes a similar approach:

• Client-side inference using WebGPU/WebGL
• No server-side transcoding changes
• Works with lower bitrate streams
• AI restores detail during playback

Their messaging is aimed at operators that already have an adaptive bitrate (ABR) pipeline and want to reduce delivery bitrate while maintaining perceived quality.

Small Pixels

Small Pixels has been in the broadcast/video optimization space for several years. Their SPAIQ Client performs AI enhancement on:

• browsers
• smart TVs
• mobile devices
• set-top boxes

They market bandwidth reductions of roughly 50% while preserving subjective quality.

If you're using common web players

Most commercial integrations are implemented as a rendering layer around players such as:

• Video.js
• Shaka Player
• hls.js
• dash.js
• Bitmovin Player
• THEOplayer

The AI SDK typically intercepts decoded frames (using WebCodecs/WebGPU or WebGL), performs super-resolution, and renders the enhanced output. The player itself continues handling HLS/DASH playback and adaptive bitrate logic.

Open-source alternatives

If you want to build this yourself rather than license a commercial SDK:

• Google's WebGPU case study demonstrates a fully client-side AI upscaler using WebGPU + WebCodecs and the open-source WebSR SDK. It's a good reference architecture but is not a turnkey streaming integration.

For a production streaming service today, Veeplay appears to be the most mature commercial offering specifically focused on client-side AI super-resolution for bandwidth reduction, with EMBRACE Vision and Small Pixels as the other notable vendors in this niche.

Several technology providers and frameworks offer client-side AI upscaling (super-resolution) to enhance video quality on local devices, allowing for lower bitrate streams that save significant bandwidth.

These technologies generally work by streaming a lower-resolution video (e.g., 720p or 1080p) and using local GPU or NPU processing to upscale it to higher quality (e.g., 4K) in real time.

Key solutions include:

• NVIDIA Maxine SDK: A widely utilized SDK that enables developers to integrate AI video enhancement—including Super Resolution and Video Noise Removal—directly into streaming and conferencing applications.
• NVIDIA RTX Video Super Resolution (VSR): Runs locally in Chrome and Edge browsers on RTX 30/40-series GPUs, specifically targeted at upgrading 1080p content to 4K to save network load.
• Twitch (via WebGPU): Twitch engineers developed a WebGPU-based real-time super-resolution solution that runs in the browser, allowing for streaming low-resolution (e.g., 720p) and upscaling to 1440p or higher on the client side.
• AMD Real-time Edge-optimized AI: Focuses on leveraging Ryzen AI hardware (iGPU/NPU) to upscale video in real-time, focusing on 1080p output for mobile and thin-and-light devices.
• Trembit: Provides solutions that integrate lightweight ML models directly into mobile and web video players for AI-driven, real-time adaptive streaming.
• Fora Soft / RepNet-VSR: Focuses on efficient, real-time super-resolution for mobile NPUs.
• WebRTC Upscaling: JavaScript libraries exist to provide real-time upscaling for video conferencing/WebRTC applications.

How it Works (Bandwidth Saving Mechanism):

1. Adaptive Bitrate (ABR) Adjustment: The player detects bandwidth constraints and selects a lower-resolution video stream.
2. Client-Side Enhancement: The local GPU/NPU processes the low-res frames, removing compression artifacts and increasing resolution using AI, reducing the need for high-bandwidth 4K streams.
3. Adaptive Framework: Systems like VisualOn or specialized web-based tools select the optimal scaling model based on device capabilities to avoid excessive battery drain.

To help you find the best solution, it would be useful to know:

• What type of platform are you developing for (web, Android, iOS, or desktop)?
• What is your primary goal (e.g., reducing bandwidth for live streaming, enhancing VOD quality)?
• Which video player do you currently use?

Leading providers for client-side AI upscaling to save bandwidth include NVIDIA, Microsoft , and WebSR . Client-side upscaling—often called Video Super Resolution (VSR) or Super-Res —allows you to stream smaller, compressed video files (which saves significant bandwidth on your Content Delivery Network bills) while the end-user's device intelligently upscales the resolution in real-time.

Major providers of client-side AI upscaling

• NVIDIA RTX Video SDK : This is the industry-standard for hardware-accelerated upscaling. Their AI model removes blocky compression artifacts and upscales 1080p to 4K natively in the browser on RTX 30 and 40-series GPUs . You can integrate the RTX Video SDK into media playback applications.
• Microsoft Video Super Resolution : Microsoft built VSR directly into the Windows API. It works on Copilot+ PCs equipped with an NPU and supported GPUs to upscale lower-resolution video streams and save bandwidth. Developers can tap into the Windows AI APIs to enable this within compatible apps.
• Open-source WebSR : For web-based video players, this open-source SDK runs directly on the client side using WebGPU compute shaders. You can explore the WebSR GitHub Repository to implement real-time AI upscaling for animation, live-action, and 3D content without uploading files to a server.
• LiveKit : A modern WebRTC framework that provides the frame-level access necessary for developers to insert custom on-device AI enhancements before rendering. Using frameworks like LiveKit allows companies to self-host and balance compute loads directly on the user's machine.

If you can tell me how your video player is hosted (e.g., custom web app, mobile SDK, or desktop software) and what type of content you stream (live conferencing, pre-recorded VOD, etc.), I can help you narrow down the best integration path.

Yes. There are a few vendors and SDKs specifically targeting client-side AI upscaling so you can stream a lower-resolution/lower-bitrate video and reconstruct a higher-quality image on the viewer's device, reducing CDN/bandwidth costs.

Here's the current landscape:

1. Veeplay (closest to what you're asking)

Veeplay offers a Web SDK that integrates into a browser video player.

Their model works like this:

• Stream 360p
• AI upscales to 720p on the client GPU
• Or stream 540p and display at 1080p

They specifically market this as a bandwidth-saving technology, claiming roughly 70–90% CDN savings depending on the source/output resolution. The SDK runs in the browser using modern GPU APIs rather than cloud inference.

This is currently one of the few commercial offerings designed specifically for integration into streaming players.

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2. WebSR (open source)

If you have engineering resources, WebSR is probably the most mature open-source option.

It provides:

• JavaScript/WebGPU SDK
• Browser inference
• Real-time video super resolution
• Works frame-by-frame using WebCodecs + WebGPU

Google recently highlighted this architecture as a case study for client-side AI video processing, noting that moving inference into the browser can eliminate server-side AI processing costs while leveraging the user's GPU.

Good if you want:

• complete control
• no licensing
• integration with Video.js, Shaka Player, hls.js, DASH.js, etc.

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3. NVIDIA RTX Video SDK

If your player is a native desktop application (not browser-based), NVIDIA provides the RTX Video SDK.

It performs:

• AI super resolution
• artifact removal
• HDR enhancement

However, it only works on supported RTX GPUs, so it's not suitable as a universal streaming solution.

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Integration architecture

Most implementations follow this pipeline:

CDN
   ↓
360p / 540p stream
   ↓
Video player
   ↓
AI upscaler (WebGPU)
   ↓
Canvas / GPU texture
   ↓
Displayed as 720p / 1080p

The player still performs adaptive bitrate (HLS/DASH); the AI enhancement happens after decoding on the client.

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Things to consider

Client-side AI upscaling works best when:

• users have modern GPUs
• browsers support WebGPU
• content is compressed but not extremely degraded
• you're targeting desktop or newer mobile devices

You'll usually want a fallback path for unsupported devices so they receive the original higher-resolution stream instead.

For most browser-based streaming platforms looking to reduce bandwidth, Veeplay is the primary commercial SDK focused on this use case, while WebSR is the leading open-source foundation for building a custom integration.

Several technology providers and software solutions offer client-side AI upscaling designed to enhance video quality locally, allowing you to stream at lower resolutions to save bandwidth.

Here are the top providers and technologies for client-side AI upscaling:

• NVIDIA Maxine Video Effects SDK: A widely used SDK that provides real-time super-resolution, noise removal, and artifact reduction. It allows applications to take low-resolution input and enhance it on the user's GPU.
• SimaUpscale (by SimaBit): Identified as a leader in real-time video upscaling, providing 2x to 4x upscaling to 4K while reducing bitrate by roughly 22%.
• Trembit: Integrates AI-driven, lightweight ML models into client-side video players (web and mobile) for predictive bandwidth estimation and real-time super-resolution to improve experience on low-bandwidth connections.
• Fora Soft: Implements client-side super-resolution using CNNs/WebGPU to enhance video quality locally, trading client compute for bandwidth savings.
• WebGPU-based solutions: Technologies like Anime4K or Real-ESRGAN can be implemented within web browsers using WebGL2/WebGPU to upscale in real-time.

How it Works for Bandwidth Saving:

1. Lower Input: The server sends a lower-resolution stream (e.g., 720p or even 480p) to the user, significantly cutting bandwidth usage.
2. Edge Processing: The client-side player uses the user's GPU or NPU to run an AI model.
3. Real-time Enhancement: The AI de-artifacts and upscales the video to 1080p or 4K locally, creating a high-quality viewing experience from a low-quality stream.

To provide a more tailored recommendation, could you tell me:

• What type of platform are you developing for (e.g., desktop web, mobile app, or set-top box)?
• What is your primary player framework (e.g., video.js, HLS.js, native player)?

Knowing this will help me determine which integration is most compatible with your setup.

If you mean a video player that downloads a lower-bitrate stream (e.g., 360p/540p) and uses AI super-resolution on the client device to display it at higher quality, the market is still early, but a few vendors/projects are relevant:

For a commercial streaming player, I would evaluate Veeplay first. It appears designed around the exact workflow you describe: serve a smaller asset, upscale at playback time, and reduce CDN/bandwidth usage.

A typical architecture looks like:

1. Encode and deliver a lower bitrate ladder:

   • 240p/360p/540p instead of 1080p
   • standard HLS/DASH delivery

2. In the player:

   • decode frames
   • run AI super-resolution on GPU (WebGPU, Metal, DirectML, CUDA, etc.)
   • render enhanced frames

3. Adapt dynamically:

   • use AI upscale only when bandwidth is constrained
   • fall back to native higher bitrate when the network/device allows

Things to check before adopting:

• Latency: real-time upscaling must stay within frame budget (e.g., <16.7 ms for 60 fps).
• Temporal consistency: many image upscalers create flicker unless trained for video.
• Device coverage: mobile GPUs are the hardest target.
• DRM path: some browser GPU pipelines cannot access protected frames directly.
• Codec efficiency: sometimes AV1/HEVC + better ABR gives more benefit than AI upscaling alone.

If you are building a web player, iOS/Android player, smart-TV app, or OTT service, the shortlist changes significantly.