Deinterlacing

Video technology has come a long way, but if you’ve ever watched an old video and noticed horizontal lines or a flickering effect, you’ve encountered something called interlacing. Deinterlacing is the process of cleaning that up and making the video look smoother. 

If you’ve ever wondered how modern videos get their crisp, clean appearance—especially when restoring old footage—this process shoves itself in one way or another. 

What is Deinterlacing?

Deinterlacing is the method used to convert interlaced video into a progressive format. This means taking video that was captured or broadcast using an interlaced system (where alternating lines of a frame are shown) and transforming it into a cleaner, smoother video without flickering or lines. 

Older TV systems, like those used for standard-definition television, worked with interlaced video, and this is where the process of deinterlacing comes into play.

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History of Deinterlacing

Here’s how deinterlacing has been used since its inception:

1. Early Days of Interlacing (1930s–1950s)
   Interlacing was developed to reduce bandwidth usage while still providing smooth motion. By splitting frames into two fields (odd and even lines), early TVs displayed continuous motion with limited resources.
2. Standard-Definition TV Era (1960s–1990s)
   Interlacing became the standard for SDTV, used widely in NTSC and PAL formats. Most TV content during this period was interlaced, which is why old footage often requires deinterlacing on modern screens.
3. Shift to Progressive Scan (1990s–2000s)
   With HDTV and digital video formats, progressive scan (showing full frames at once) replaced interlacing. This provided sharper, smoother images on newer displays.
4. Modern Deinterlacing (2000s–Present)
   As old content was digitized, advanced deinterlacing methods were developed, including tools like FFmpeg and AI-driven solutions, to improve video quality and remove artifacts from interlaced footage.
5. AI in Deinterlacing (2020s and Beyond)
   AI-powered deinterlacing now offers superior quality, especially for complex scenes, by predicting missing data between fields. It’s increasingly used for video restoration and modern playback.

How Deinterlacing Works

When you're working with interlaced video, every frame is split into two fields: odd and even lines. The odd lines are displayed first, then the even ones. This happens so quickly that your eyes are tricked into thinking you’re seeing a full image. 

However, on modern displays or when viewing at higher resolutions, these split fields become noticeable, especially when there's fast motion. Deinterlacing takes these two fields and merges them into one full image, or frame, which is progressive, meaning all lines are shown at once.

There are various ways to do this, some more advanced than others, but the goal is always the same: to turn interlaced video into something that looks good on modern screens.

Deinterlacing in Different Scenarios

Here’s how deinterlacing can be used depending on the use case and context:

1. Live Broadcasting

• In live broadcasts, such as sports or news, motion-adaptive or motion-compensated deinterlacing is often used to ensure smooth playback during fast motion scenes.

2. DVD/SD Video Conversion

• When converting old DVDs or standard-definition video content, tools like FFmpeg can use methods like Yadif or Bwdif to deinterlace video for playback on modern progressive displays.

3. Film Restoration

• AI deinterlacing is often used in restoring old films or video footage, where accuracy is key. The AI can fill in missing details in the interlaced fields for a more polished final product.

4. Streaming

• Streaming services such as Netflix or YouTube tend to rely on MP4 or modern formats like AVIF that handle progressive scanning by default, but may use deinterlacing for older content being viewed on modern devices.

Why Deinterlacing is Important

With today's technology, most displays are progressive, meaning they show a full frame at once, not split fields like interlaced video. If you play interlaced content on a modern display without deinterlacing it first, you’ll notice artifacts like jagged lines or blurriness, especially during fast movements. 

Deinterlacing ensures that your video looks as smooth and clean as possible, eliminating these issues. If you're working with older content or broadcasting systems, deinterlacing is a must.

VMAF (Video Multi-Method Assessment Fusion), developed by Netflix, is becoming a popular metric for evaluating deinterlacing quality. It better reflects subjective human perception of video quality, particularly when combined with PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index Measure). 

Common Methods of Deinterlacing

There are several techniques for video deinterlacing, each with its pros and cons. While the best deinterlacing method is subjective, some of the most common methods include:

1. Weaving: This is the simplest method, where the two fields are combined to make one frame. It’s fast, but can lead to artifacts like combing, especially during motion.
2. Blending: Here, the two fields are averaged together. This reduces flicker, but can result in a loss of detail and a blurred image, especially during motion.
3. Motion Adaptive Deinterlacing: This method analyzes the movement in the video and applies deinterlacing selectively. If there’s no movement, the fields are simply woven together. If motion is detected, the algorithm adapts and applies deinterlacing to reduce artifacts. This produces better results than basic methods.
4. Motion Compensated Deinterlacing: This is a more advanced form of motion adaptive deinterlacing that not only detects motion but also tries to predict it, adjusting the fields in a way that minimizes motion artifacts even further. It’s often considered one of the best deinterlacing methods but is more resource-intensive.

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Challenges of Deinterlacing

Deinterlacing isn't always straightforward. One of the main challenges is dealing with motion. When objects move across an interlaced video, the two fields capture different moments in time, which can create visual errors, or artifacts, when they’re merged. 

These artifacts can look like jagged edges or “combing” in areas of motion. While advanced methods like motion adaptive deinterlacing or motion compensated deinterlacing can reduce these problems, they aren’t foolproof and often require a lot of processing power.

Another challenge is balancing quality and performance. More sophisticated deinterlacing methods require more computational resources, which can slow down your workflow if you don’t have the right hardware.

Deinterlacing in Modern Video Formats

With the shift to high-definition and 4K video formats, interlaced content has become less common. However, if you're dealing with older footage or broadcasts, especially with standard-definition video, you'll still come across interlaced formats. 

1. Deinterlacing in FFmpeg

FFmpeg is one of the most widely-used tools for video processing and includes robust deinterlacing filters. Whether you're digitizing old tapes or converting DVDs, FFmpeg's filters are highly customizable.

2. Deinterlacing in MP4

MP4 is a highly compatible format, widely used for online streaming and media playback. Deinterlacing is typically handled during the encoding or playback stage, depending on the software or player.

3. Deinterlacing in AVIF

AVIF is designed for high-efficiency video compression, typically with progressive scan formats. Deinterlacing is not a core concern, but when dealing with older formats, it may require preprocessing.

4. Deinterlacing in MKV (Matroska)

MKV is a versatile container format, often used for high-quality archiving. Deinterlacing depends on the codecs and media player used for playback.

5. Deinterlacing using AI Tools

AI deinterlacing is at the forefront of video restoration. By leveraging machine learning, AI tools can predict and reconstruct missing lines, delivering superior results compared to traditional methods.

Modern AI-based deinterlacers such as Topaz Video AI offer up to 8x GPU acceleration, making them capable of processing even 4K footage efficiently.

However, batch deinterlacing and upscaling operations can still take significant time—up to 50 minutes per clip minute in some cases for stabilization​.

Conclusion

Deinterlacing is an essential process for converting older video formats to be viewable on modern screens. Whether you're restoring old home videos or working in video production, leveraging the different deinterlacing methods—like motion adaptive deinterlacing and AI deinterlacing—can help you choose the best approach for your needs. 

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