AI Video Upscaling: How to Improve Quality

What Is AI Video Upscaling and How Does It Work?

AI video upscaling is the process of using neural networks to increase the resolution of video footage beyond its original pixel count. Unlike traditional upscaling, which simply stretches existing pixels to fill a larger frame and produces blurry results, AI upscaling analyzes each frame and generates new pixel data that predicts what the higher-resolution version should look like. The result is a video that appears sharper, more detailed, and closer to native high-resolution footage than anything conventional scaling algorithms can produce.

The core technology behind AI upscaling is called super-resolution. These models are trained on millions of paired images -- a low-resolution input and its corresponding high-resolution ground truth. During training, the network learns patterns: how edges should sharpen, how textures should resolve, how fine details like hair strands, fabric weave, and text should appear at higher resolutions. When you feed it a 720p frame, the model does not simply interpolate between existing pixels. It makes educated predictions about what detail belongs in the spaces between those pixels based on everything it has learned from its training data.

Modern AI upscalers use several neural network architectures to achieve their results. Generative adversarial networks (GANs) pit two networks against each other -- one generates upscaled frames while the other evaluates whether they look realistic. Diffusion models work by gradually adding and then removing noise to reconstruct high-resolution detail. Transformer-based models process temporal information across multiple frames, using motion data to improve consistency in video. Each approach has trade-offs in speed, quality, and artifact generation, which is why different tools produce noticeably different results on the same source footage.

When Should You Upscale Video?

The most compelling use case for AI upscaling is rescuing old footage that was recorded at resolutions that now look dated. Home videos from the early 2000s shot at 480p or 360p, corporate training videos archived at standard definition, documentary footage from older cameras, and surveillance recordings all benefit dramatically from AI upscaling. This footage cannot be re-recorded, and the original content often has significant personal or professional value that justifies the processing time and effort involved in upscaling.

Repurposing archived content for modern platforms is another scenario where upscaling earns its place in your workflow. A brand that recorded product demos in 720p five years ago may want to use that footage in a new campaign without reshooting. A filmmaker who captured behind-the-scenes footage on an older phone can upscale it to match the quality of their primary camera footage. A content creator who has a library of older YouTube videos at 720p can upscale and re-upload them to take advantage of YouTube's preference for higher resolution content in search rankings.

Screen recordings and presentations are a surprisingly practical upscaling target. Many screen capture tools default to lower resolutions, and older webinar recordings were frequently captured at 720p or below. Upscaling these recordings before embedding them in courses, documentation, or marketing materials can make text crisper and UI elements more readable without requiring you to re-record the entire session.

• Old home videos and archival footage at 480p or lower -- AI upscaling can restore detail and make vintage footage presentable on modern screens
• Previously recorded content at 720p that you want to repurpose without reshooting -- upscaling to 1080p or 4K extends the usable life of your existing library
• Screen recordings and webinars where text readability is poor -- upscaling sharpens text and UI elements significantly
• Low-resolution footage from action cameras, dashcams, or security cameras where reshooting is impossible
• Video testimonials or interviews recorded on older phones that need to match the production quality of newer footage

The Best AI Video Upscaling Tools in 2026

Topaz Video AI ($299 one-time purchase) is the most capable dedicated upscaling tool available. It runs locally on your computer using your GPU and offers multiple AI models optimized for different source types -- one for faces, one for animation, one for general footage, one for high-motion content. Topaz can upscale footage up to 4x (e.g., 1080p to 4K, or 480p to 1080p) and includes frame interpolation for smooth slow-motion. Processing times vary by GPU, but a modern NVIDIA card can handle roughly 2-5 frames per second at 4K output. The quality is consistently the best available for dedicated upscaling, which is why it has become the standard tool in post-production houses and restoration studios.

DaVinci Resolve (free, with paid Studio version at $295) includes a built-in Super Scale feature in its Studio edition that provides 2x and 4x upscaling directly inside your editing timeline. The free version of Resolve does not include Super Scale, but it does include basic resize options. For editors who already use DaVinci Resolve, Super Scale is the most convenient upscaling option because it integrates directly into your existing workflow without requiring a separate application. The quality is good but not quite on par with Topaz for challenging footage, and it is limited to 2x and 4x scaling with no fine-tuning of AI model parameters.

CapCut (free) includes a one-click Enhance button that applies AI upscaling and sharpening to video clips. The results are less customizable than dedicated tools, but for social media creators who want a quick quality boost without learning new software, CapCut's enhancement is remarkably effective. It works best on footage that is already decent quality but slightly soft -- the AI sharpening and upscaling combine to produce noticeably crisper output for TikTok, Reels, and Shorts. The limitation is that you cannot control the upscaling factor or choose between AI models, and the processing happens on CapCut's servers rather than locally.

Runway ($12/month) offers AI video enhancement as part of its broader creative AI platform. The upscaling quality is solid for web delivery and social media content, and the cloud-based processing means you do not need a powerful GPU. Runway is best suited for creators who already use the platform for other AI video tasks and want upscaling as part of an integrated workflow. It is not the best standalone upscaling tool, but the combination of upscaling with Runway's other capabilities -- background removal, color grading, and generative tools -- makes it a practical all-in-one option for creators working within its ecosystem.

How Good Is AI Upscaling Really?

The honest answer is that AI upscaling is impressive but imperfect, and the quality depends heavily on what you start with. Clean 720p footage with good lighting, stable camera work, and minimal compression artifacts will upscale to 1080p or even 4K with results that are genuinely difficult to distinguish from native resolution at normal viewing distance. The AI adds convincing texture detail, sharpens edges cleanly, and fills in the gaps between pixels with data that looks plausible. Side-by-side with native 4K, a trained eye can spot the difference, but a casual viewer watching on a phone or laptop will not notice.

The quality drops significantly when the source footage has problems beyond low resolution. Heavy compression artifacts -- the blocky, smeary patches you see in old YouTube videos or heavily compressed DVDs -- confuse the AI models. The upscaler tries to interpret compression blocks as real detail and sometimes generates strange textures or patterns where smooth gradients should be. Motion blur from camera shake or fast movement creates similar problems: the AI cannot determine what the sharp version of blurred content should look like, so it guesses, and those guesses often produce waxy or oversharpened artifacts.

Faces are both the best and worst case for AI upscaling. When the original face is relatively clear and well-lit, AI upscalers with face-specific models (like Topaz's face recovery model) produce stunning results -- they can reconstruct facial features, add realistic skin texture, and sharpen eyes and teeth in ways that look remarkably natural. But when the original face is very small in the frame, heavily shadowed, or partially obscured, the AI sometimes generates facial features that are subtly wrong. The uncanny valley effect is real with AI face reconstruction, and a face that looks almost-but-not-quite right is worse than one that is simply blurry.

Can You Fix Bad Video Quality After Recording?

The answer depends entirely on what "bad quality" means for your specific footage. If your video is sharp and well-exposed but recorded at low resolution -- say 480p or 720p from an older camera or phone -- AI upscaling can dramatically improve perceived quality. The footage has real detail captured in those pixels; the AI just needs to expand and enhance what is already there. This is the scenario where upscaling works best, and the results can be transformative. A 720p video that looked dated and soft can become a convincing 1080p or even 4K file that holds up alongside modern recordings.

If your footage is blurry due to missed focus, camera shake, or subject motion, the realistic expectation should be modest improvement rather than complete repair. AI deblurring tools exist, and they can reduce the appearance of blur by sharpening edges and adding texture. But they cannot reconstruct the sharp detail that was never recorded in the first place. A face that is out of focus will look slightly less blurry after processing, but it will not suddenly snap into crisp focus. The AI is making educated guesses about what should be there, and those guesses are imperfect. Think of it as polishing scratched glass -- you can reduce the scratches, but you cannot make the glass perfectly clear.

Noise and grain from low-light recording respond well to AI processing. Modern AI denoisers like Topaz Video AI and DaVinci Resolve's noise reduction can remove grain while preserving underlying detail far better than traditional noise reduction, which tended to smear away detail along with the noise. If your footage is grainy but otherwise sharp, AI denoising followed by upscaling can produce excellent results. The combination of removing noise and then adding resolution gives the AI cleaner data to work with, and the output quality is often impressive.

• Low resolution but sharp footage: AI upscaling is highly effective and can produce near-native quality results at 2x-4x the original resolution
• Out-of-focus or motion-blurred footage: modest improvement only -- AI cannot reconstruct detail that was never captured by the camera sensor
• Grainy or noisy low-light footage: AI denoising works extremely well and can be combined with upscaling for dramatic improvement
• Heavily compressed footage (old YouTube, DVD rips): mixed results -- AI struggles with compression blocks and may introduce artifacts while trying to reconstruct detail
• Dark or underexposed footage: AI exposure correction and color grading tools can recover some shadow detail, but severely underexposed footage will show noise when brightened

Practical Upscaling Workflow for Creators

A good upscaling workflow starts with honest evaluation of your source footage. Before processing anything, watch the original video at 100% zoom and identify its specific problems. Is it simply low resolution but otherwise clean? Is there visible noise or grain? Are there compression artifacts? Is there motion blur on key subjects? Your answers determine which processing steps to apply and in what order. Applying the wrong enhancement chain wastes hours of processing time and can actually degrade quality rather than improve it.

The ideal processing order is: denoise first, then color correct, then upscale last. Denoising before upscaling gives the AI cleaner source data to work with, which produces dramatically better upscaled results than upscaling noisy footage and trying to denoise afterward. Color correction before upscaling ensures the AI model sees accurate tonal relationships when generating new detail. If you reverse this order and upscale first, you amplify noise and color issues to a higher resolution, making them harder to fix later.

Export settings matter as much as the upscaling itself. After upscaling, export using H.264 or H.265 codec at a bitrate appropriate for your output resolution -- 20-30 Mbps for 1080p, 50-80 Mbps for 4K. Exporting at too low a bitrate after upscaling will compress away the fine detail your AI tool just spent hours generating. For archival purposes, use ProRes or DNxHR if your storage allows it. For social media delivery, H.265 at medium-high bitrate provides the best quality-to-file-size ratio, since platforms re-encode your upload anyway and a high-quality source file survives platform compression better than a heavily compressed one.

1. Evaluate your source footage at 100% zoom. Identify whether the primary issue is resolution, noise, compression artifacts, blur, or a combination. This determines your processing pipeline
2. Run AI denoising first if your footage has visible noise or grain. Tools like Topaz Video AI and DaVinci Resolve Studio handle this well. Process at the original resolution -- do not upscale yet
3. Apply color correction and exposure adjustments to the denoised footage. Fix white balance, contrast, and exposure before upscaling so the AI model works with accurate color data
4. Run the AI upscaler on the clean, color-corrected footage. Start with 2x upscaling (e.g., 720p to 1440p or 1080p to 4K). Only go to 4x if the source is exceptionally clean
5. Preview the upscaled output at 100% zoom before committing to the full render. Check faces, text, and fine textures for artifacts. Adjust model settings if needed
6. Export at an appropriate bitrate: 20-30 Mbps for 1080p, 50-80 Mbps for 4K in H.264 or H.265. Use ProRes for archival. Do not undermine the upscale by over-compressing the export
7. For batch processing multiple clips, queue all files in your upscaling tool and run overnight. A 10-minute 4K upscale from 1080p source takes 30-90 minutes depending on your GPU