How to Improve Video Quality with AI

Modern Artificial Intelligence Technologies in Video Enhancement

Modern artificial intelligence (AI) technologies are rapidly changing the field of video production. In the past, improving video quality required expensive cameras, professional lighting, and complex post-production software. Today, having access to neural networks is enough — even ordinary webcam footage can be transformed into content of professional studio quality.

Neural networks can increase resolution, remove noise, correct color, stabilize the image, enhance the clarity of faces and objects, and even bring old footage to life. In this article, we will explore how AI helps improve video quality, what tools exist, and how to use them in practice.

Why Neural Networks Became a Breakthrough in Video Enhancement

Traditional image enhancement algorithms worked according to fixed rules — they did not "understand" the content, but simply increased contrast or sharpness. Unlike them, neural networks are trained on millions of examples and can understand context: where the face is, where the background is, where the texture of the fabric or shadow is.

This allows achieving photorealistic results without losing details. AI does not just "enhance" the picture — it restores missing pixels, creating the most natural image possible.

Main Advantages of AI Technologies for Video

Resolution enhancement without artifacts (upscaling);
Noise and blur removal without losing clarity;
Improvement of faces and eyes in low light;
Automatic color correction and stabilization;
The ability to "revitalize" old recordings or archived streams.

Main Technologies Used by Neural Networks to Enhance Video

To understand how AI enhances video, it is important to examine key approaches and mechanisms.

1. Super-Resolution

This technology increases video resolution — for example, from 720p to 4K — not by simply stretching the image, but by restoring details that were missing. The algorithm analyzes each frame, "drawing in" missing elements based on experience gained from thousands of examples.

Popular tools:

Topaz Video Enhance AI — one of the most well-known software, improves resolution and clarity up to 8K.
ESRGAN (Enhanced Super-Resolution GAN) — used in open-source solutions.
DVDFab Enlarger AI — suitable for old videos or DVD archives.

2. Denoising and Deartifacting

The neural network can distinguish noise from real details. This is especially useful for videos shot in low light or on cheap cameras. AI cleans the image, removing grain, color spots, and compression artifacts while preserving textures and lines.

Popular tools:

Neat Video — a plugin for Premiere Pro, DaVinci Resolve, and Final Cut.
DaVinci Neural Engine — built-in AI module, automatically improves quality during editing.

3. Frame Interpolation

This technology increases the frame rate (FPS), making the video smoother. The neural network "fills in" intermediate frames based on object movement. For example, a 24 FPS video can be converted to 60 or even 120 FPS.

Tools:

RIFE (Real-Time Intermediate Flow Estimation) — one of the best open-source models.
FlowFrames — application using AI for smooth FPS upscaling.
Dain-App — simple solution for Windows.

4. AI Color Correction and HDR Enhancement

Neural networks analyze lighting and color temperature, adjusting white balance, contrast, and saturation. This makes the image cinematic without manual color correction. AI can also expand dynamic range (HDR), making shadows and highlights more expressive.

Tools:

Runway ML — automatic color correction and video stylization.
Adobe Premiere Pro Auto Color (Sensei AI) — improves colors based on machine learning.

5. Face Restoration and Enhancement

AI can restore faces in frames even if they are blurred or poorly lit. These neural networks detect facial contours, eyes, and mouth, and restore natural skin textures.

Tools:

GFPGAN — popular AI for face restoration.
CodeFormer — enhances clarity while maintaining realism.
Topaz Video AI — combines several such functions in one interface.

How to Enhance Video Using Neural Networks: Step-by-Step Guide

Step 1. Define Your Processing Goal

Do you want to increase resolution, remove noise, or make smooth animation? The choice of tool depends on this.

Step 2. Prepare the Source Material

The video should be in a good codec (e.g., MP4, MOV, or MKV). The less compressed it is, the more accurately the neural network will work.

Step 3. Choose the Appropriate Tool

For resolution enhancement — Topaz Video Enhance AI;
For smoothness — FlowFrames or RIFE;
For noise removal — Neat Video;
For color improvement — Runway ML or DaVinci Resolve.

Step 4. Adjust Processing Parameters

AI tools allow you to adjust the intensity of upscaling, filters, and sharpness. It is better not to set maximum values — otherwise, the result may look "artificial".

Step 5. Export and Compare

After rendering, compare frames "before" and "after". Usually, AI-enhanced videos have smooth transitions, bright colors, and more detail in shadows.

How AI Helps Streamers Enhance Video in Real Time

Previously, neural network processing technologies were used only during editing, but today they are available for live streams. Modern programs allow video enhancement during the broadcast.

For example:

NVIDIA Broadcast — improves lighting, removes background, enhances face clarity.
OBS Studio + AI filters — add noise reduction and auto-focus.
XSplit with AI Background — removes background without a green screen.

This is especially useful for content creators who do not have professional equipment but want to look high-quality.

Enhancing Old or Archived Videos

AI can literally "resurrect" old recordings:

Restore lost frames;
Improve VHS video clarity;
Add color to black-and-white films;
Stabilize the image.

Tools like DeOldify or Topaz Video Enhance AI are actively used for restoring historical footage and personal archives.

Common Mistakes to Avoid

Excessive sharpening. This makes the image look unnatural.
Incorrect model choice. Some neural networks are optimized for faces, others for nature or animation.
Neglecting color correction. Even perfect resolution will not help if white balance is off.
Insufficient hardware. AI processing requires a powerful graphics card (preferably NVIDIA RTX).

The Future of AI in Video Enhancement

In a few years, video editing will become almost fully automated. Already, neural networks can:

Generate video from text descriptions;
Simulate camera movement;
Change video style (e.g., turn a stream into animation);
Create new scenes based on a short clip.

Platforms like Runway ML, Pika Labs, and Stability Video Diffusion are already taking the first steps toward full AI video-making.

Conclusion

Using neural networks to improve video quality is not just a trend but a real tool that saves time, money, and opens up new creative possibilities. AI makes video content professional even without expensive equipment or post-production knowledge.

Whether you are a streamer, videographer, blogger, or producer, neural networks can help:

Enhance live stream quality;
Improve the clarity of old videos;
Make video cinematic and lively.

The key is to experiment and not fear technology. Artificial intelligence today is your personal video editor, lighting operator, and cameraman in one. Tomorrow, AI may even create the perfect video without any human involvement.