What Is the Best AI Video Upscaler?

Overview

There is no independently established, universally best AI video upscaler. The right choice depends on the source footage, the kind of damage, the output resolution, available hardware, budget, and whether the goal is to export an improved file or simply make playback look better. Among the products reviewed here, Topaz Video is a strong all-round desktop option for hands-on restoration because it offers multiple enhancement models and adjustable controls. DaVinci Resolve Studio is a practical choice for editors already working in Resolve; Video2X is an open-source option for users willing to configure a model-based workflow; and NVIDIA RTX Video Super Resolution targets real-time viewing rather than creating a new master file. These are use-case recommendations, not results from a controlled head-to-head test.

AI upscaling enlarges video frames and estimates detail that a lower-resolution or degraded source does not clearly contain. It can improve perceived sharpness, reduce some noise and compression artifacts, or make footage fit a larger display, but it cannot reliably restore lost facts or guarantee authentic fine detail. Inspecting short previews—including motion and difficult frames—is more dependable than trusting a preset or the output resolution alone.

What AI video upscaling does

Video super-resolution is the task of producing higher-spatial-resolution frames from lower-resolution video. It is related to, but distinct from, denoising, deblurring, deinterlacing, and compression-artifact reduction. A clip may need more than one of these treatments: for example, old tape footage can be interlaced, noisy, soft, and compressed at the same time. The deep-learning video-restoration literature treats these as different restoration problems, even though some products combine them in a workflow (Video restoration based on deep learning: a comprehensive survey | Artificial Intelligence Review | Springer Nature Link).

A conventional resize interpolates pixel values to fill a larger frame. An AI model instead uses patterns learned during training to estimate edges and textures that may be plausible for the input. Depending on the method, it may analyze frames individually or use information from adjacent frames. Video methods can align and combine information across time; a frame-by-frame approach may miss that temporal redundancy and can produce inconsistent details or flicker (Video restoration based on deep learning: a comprehensive survey | Artificial Intelligence Review | Springer Nature Link; BasicVSR++: Improving Video Super-Resolution With Enhanced Propagation and Alignment).

The word “recover” needs qualification. If a face, sign, or object is only a few blurry pixels in the source, a model can make it look clearer by synthesizing a likely texture. That does not establish that the generated eyelashes, lettering, or surface pattern are faithful to what was originally recorded. Research on blind super-resolution addresses unknown degradations by training on simulated distortions, but real footage can differ from those simulations (Real-ESRGAN: Training Real-World Blind Super-Resolution With Pure Synthetic Data; Investigating Tradeoffs in Real-World Video Super-Resolution). For evidence, archival, legal, or scientific footage, preserve the original and do not treat enhanced details as independently verified facts.

Upscaling is not the same as frame interpolation

Upscaling increases the width and height of frames. Frame interpolation creates additional frames between existing ones to change the frame rate or produce slow motion. They can be combined, but each can create its own artifacts. Topaz lists enhancement and frame-interpolation models as separate functions (Quick Start | Topaz Video); Video2X likewise describes filtering/upscaling and interpolation as separate modes (GitHub - k4yt3x/video2x: A machine learning-based video super resolution and frame interpolation framework. Est. Hack the Valley II, 2018. · GitHub). If the main issue is softness, frame interpolation is not a substitute for restoration.

Which AI video upscaler is best?

Practical recommendation

For a person asking “what is the best AI video upscaler?”, a reasonable default is Topaz Video when the task is to restore and export a variety of clips as a dedicated desktop job. Its current documentation describes a broad selection of models and manual or automatic settings, but the vendor’s own product guidance is not an independent comparison. This recommendation reflects breadth of controls, not proof that Topaz produces the best result on every footage type (Enhancement | Topaz Video).

Choose Resolve Studio when the video is already being edited or finished in Resolve and the integrated workflow matters more than having a dedicated collection of enhancement models. Choose Video2X when open-source software, local model control, and no software license fee are priorities and a more hands-on workflow is acceptable. Choose RTX Video when the desired result is simply cleaner playback on a compatible computer. The reviewed evidence does not include a controlled, independent test that renders identical clips through all these products on the same hardware and settings; a definitive quality ranking would therefore be unsupported.

How to AI upscale video and enhance its quality

A useful workflow starts with diagnosis, not with selecting the largest output size. The source’s resolution, codec, compression, focus, noise, motion, and interlacing all affect which process is suitable. Topaz’s guide, for example, asks users to choose output resolution and video type and distinguishes progressive from interlaced input (Enhancement | Topaz Video).

A step-by-step workflow

1. Preserve the original. Keep an unmodified source file and work from a copy. This makes it possible to compare the result and return to the source if an enhancement invents texture or removes useful grain.
2. Identify the actual defect. Check several moments, including movement, faces, fine patterns, shadows, and high-contrast edges. Look for blocky compression, sensor noise, motion blur, missed focus, interlacing, or simply low pixel dimensions. An upscaler is not necessarily the right fix for every one of these issues.
3. Set a delivery target. Use the resolution needed for the final screen or edit. For example, a 1920 × 1080 frame enlarged to 3840 × 2160 is a 2× increase in each dimension and has four times as many output pixels. That raises processing and storage demands; it does not mean the source contained four times more recorded detail. Topaz documents this 1080p-to-4K workflow and recommends previewing the result (Upscale 1080 to 4K | Topaz Video).
4. Match the model to the footage. Start with a general model if the degradation is mixed, then test a specialized model if the main issue is clear. Topaz describes Proteus as a general enhancement model for low- to medium-quality footage, Iris as having a face-enhancement emphasis, and Nyx as a denoising model for retaining detail in high-resolution footage. These are the vendor’s descriptions, not guarantees for every clip (Enhancement | Topaz Video).
5. Render short, representative previews. Include a static shot and a difficult moving section. Compare the original and enhanced images at the intended viewing size as well as at close zoom. Topaz’s 1080p-to-4K instructions explicitly advise generating a preview and inspecting it with the zoom tool (Upscale 1080 to 4K | Topaz Video).
6. Use restrained settings. More sharpening or detail enhancement can make edges harsher, faces waxy, or textures unstable. Increase one control at a time so that a visible change can be attributed to a specific setting. If a model removes too much grain or texture, compare a less aggressive result rather than assuming stronger processing is better.
7. Inspect the whole clip before committing. Check temporal consistency: do details shimmer, pulse, or change from frame to frame? Also check halos around edges, smeared motion, faces, text, color shifts, and audio synchronization. A sharp still frame is not sufficient evidence of a good video result; temporal consistency is a recognized problem in video restoration research (Video restoration based on deep learning: a comprehensive survey | Artificial Intelligence Review | Springer Nature Link).
8. Export with a suitable codec and verify it. Choose a format and encoding setting appropriate for the next editing or delivery step. Reopen the exported file and review it; an aggressive final encode can undo some of the visible gains. Topaz documents support for multiple containers and encoders, but users should check the compatibility requirements of their target workflow (Supported File Formats, Encoders and Containers | Topaz Video).

Tool-specific guidance

Topaz Video: broad standalone controls

Topaz Video is a desktop application with enhancement, denoising, stabilization, motion deblur, frame interpolation, and other functions. Its current guide identifies seven main progressive enhancement models, including Proteus, Iris, Nyx, Rhea, Artemis, Gaia, and Theia (Quick Start | Topaz Video; Enhancement | Topaz Video). Its official 1080p-to-4K example starts with Artemis High Quality and suggests Proteus Fine Tune if the first result is not satisfactory. That is useful as a starting point for that described source, not a universal rule for all 1080p clips (Upscale 1080 to 4K | Topaz Video).

Topaz supports local processing, while its cloud options process media on the provider’s infrastructure and may require credits or another paid plan. Users handling private or restricted footage should check the current processing and account terms before uploading. The company’s terms distinguish local processing from cloud processing (18.2.2. *Processing.* If You have a License to the Software, You have two (2) image or video (“Media”) processing options: (a) You can process Media on Your computer for no additional cost (“Personal Processing”) on the local-enabled desktop applicat). Hardware demands also vary by model: Topaz’s current requirements page lists 8 GB or more of VRAM in its recommended Windows specifications, and higher minima for some Starlight models. Check the requirement for the exact model and operating system before purchasing or planning a render (System Requirements | Topaz Video).

DaVinci Resolve Studio: integrated editing

Blackmagic Design’s Resolve 20 feature guide identifies 3× Enhanced and 4× Enhanced Super Scale in the Studio version. It also warns that Super Scale is among the application’s more computationally intensive tools and suggests proxies or optimized media for projects with many scaled clips (DaVinci Resolve 20 New Features Guide). This makes Resolve a useful option when the footage is already in its editing and finishing pipeline, but it does not establish that Super Scale will outperform other products on every source.

Video2X: open-source model selection

Video2X is a machine-learning-based video super-resolution and frame-interpolation framework. Its project lists Anime4K, Real-ESRGAN, Real-CUGAN, and RIFE support, with Windows and Linux builds and container options. The repository identifies the project license as GNU AGPL version 3 and separately lists licenses for included dependencies (GitHub - k4yt3x/video2x: A machine learning-based video super resolution and frame interpolation framework. Est. Hack the Valley II, 2018. · GitHub). That makes it an option for users who want to experiment without buying a commercial application, but it is less of a turnkey guarantee: the user still has to choose suitable models and assess the results. Check the repository’s license and third-party notices for the intended use.

NVIDIA RTX Video: playback enhancement

NVIDIA says RTX Video Super Resolution analyzes frames with a neural network, enhances edges and features, and removes some compression artifacts. It applies to video playback in supported Windows applications, including VLC and supported browsers, on RTX GPUs; it is distinct from DLSS, which can use game-engine data (RTX Video FAQ | NVIDIA). Use it when the objective is to view online or local content more cleanly on a high-resolution screen. It is not equivalent to processing a clip and exporting an enhanced archival or editing master.

Limitations and trade-offs

Higher resolution does not equal recovered truth

Upscaling changes the number of pixels in the output. Some systems also infer edges or textures from learned patterns. The result may look sharper, but the software cannot reliably determine details that were never captured or have been destroyed by blur, clipping, compression, or motion. This matters most for small lettering, faces, and other details that viewers may interpret as factual. For documentary evidence or measurements, retain the original and disclose that an enhanced copy has been processed.

Temporal artifacts can be more noticeable than still-frame defects

Video restoration must keep details coherent as objects move and scenes change. The research literature explains that processing each frame without exploiting temporal relationships can create flicker; video super-resolution methods therefore use frame alignment and information propagation to share evidence across time (Video restoration based on deep learning: a comprehensive survey | Artificial Intelligence Review | Springer Nature Link; BasicVSR++: Improving Video Super-Resolution With Enhanced Propagation and Alignment). A model can still struggle with occlusion, fast motion, scene cuts, fine repetitive patterns, or degraded source material. Review playback, not just selected stills.

Resolution increases computation and file size

A 2× enlargement in width and height creates four times the number of pixels per frame. At the same frame rate, processing more pixels raises compute demands; retaining a high-quality intermediate can also require more storage than a delivery encode. Product requirements are not interchangeable: Topaz lists substantial memory and GPU needs for some models, while Blackmagic flags Super Scale’s compute cost (System Requirements | Topaz Video; DaVinci Resolve 20 New Features Guide). The actual render time varies with clip length, resolution, model, hardware, and settings; no single render-rate figure applies to all users.

A sharper output can still be a worse restoration

Noise reduction can smooth texture; sharpening can create bright or dark halos; generative detail can look artificial; and a high-resolution export can preserve compression artifacts more clearly. Quality should be judged against the source and the intended display, including motion and fine patterns. Research evaluates restoration using different measures of fidelity and perceptual appearance, and those measures do not turn a subjective, source-dependent choice into one universal product ranking (Video restoration based on deep learning: a comprehensive survey | Artificial Intelligence Review | Springer Nature Link; AIM 2020 Challenge on Video Extreme Super-Resolution: Methods and Results).

Final decision rule

• Choose Topaz Video for a dedicated, flexible desktop workflow with multiple restoration models, after confirming that your hardware supports the models you intend to use.
• Choose DaVinci Resolve Studio if integrated editing and finishing are the priority and the Studio edition’s Super Scale fits the footage.
• Choose Video2X if open-source software and model experimentation matter more than a fully guided commercial interface.
• Choose NVIDIA RTX Video Super Resolution if you want better compatible playback rather than a new enhanced export.

The best result is the least aggressive workflow that visibly improves the target footage without introducing distracting or misleading detail, flicker, or other artifacts. Because no independent, controlled comparison establishes a single winner across current products and footage types, testing a short representative sample with the intended output settings remains the soundest way to choose.