Deinterlacing using AI usually brings satisfactory results, but as a video professional, you seek that extra performance. Deinterlacing isn’t always a straightforward success, and one of the main challenges is dealing with motion. This guide will walk you through real-life scenarios and best practices for extracting the maximum results using the current advancements in AI technology.
Artifacts in motion
The appearance of artifacts in interlaced videos on progressive displays is determined when objects move across the interlaced video, and the two fields capture different moments in time, enforcing visual errors. Artifacts mostly appear in the well-known form of jagged edges where balancing quality and performance leads to maximized results. These visual distortions lead to errors such as:
- Fast-moving objects with jagged edges create edges that affect video clarity.
- Flickering and blurring when interlaced videos are combined with slow motion.
- Degradation of quality due to user perception. Even high-value footage may appear outdated if deinterlacing is not properly executed, particularly when audiences are accustomed to HD or 4K resolution.
Achieving the edge: How to prepare your footage
There is no universal ‘golden rule’ to maximize deinterlacing, but preparation is key. A common mistake is applying AI deinterlacing without fully understanding the footage.
First, understand your footage and identify whether the material has significant motion, compression issues, or improperly deinterlaced frames. It will give you a clue as to which deinterlacing method can be nominated as the primary and secondary tryout. Sometimes, preprocessing might be a good idea to maximize deinterlacing. Still, in most cases, it won’t work as AI video enhancers use pre-trained models to analyze what is wrong in the original footage and interpolate missing pixels to achieve a clean-up of the result.
Always inspect the final output at native resolution before proceeding to further enhancement with upscale. Combining these two methods in the right order won’t affect the maximizing deinterlace results.
Maximizing AI deinterlacing performance
Interlaced videos cause the most problems in use cases when the source material is a progressive 720x486 MPEG-4 AVC file that was improperly deinterlaced. This results in interleaving fields being burned into a single frame.
Simple fact - get your video into the best possible shape before using AI tools. To maximize deinterlacing results, follow these principles:
The most common misconception when using AI video enhancers is that they can deal with any input type. Completely wrong. The end results can only be improved in proportion to the video input. VHS quality can’t be upscaled to 4K, not even close to 720p. Always keep with the rule - the better the source, the better the results.
Don’t combine multiple AI filters at once. While multi-enhancement results will be interpreted as an improvement compared to the input file, this is where video professionals can make room to maximize deinterlacing using AI. With this approach, you want to avoid creating unwanted artifacts that may reside when combining multiple AI filters and being seen at high-end resolutions. First, think of getting the input file into the most available quality, then deinterlace using AI tools, check the results, and if satisfied with the outcome, upscale to the desired resolution in comparison to the original video quality. Always remember to take one enhancement at a time.
In certain cases, denoising contributes to maximize deinterlacing. It can be used, even knowing it will remove some detail. In the end, it allows you to use a better enhancement model when upscaling later on.
Finally, creativity never disappoints. Impressive in many ways, AI technology is still in its early development. As we better understand how neural networks work and train the models with more efficiency, the results will continue to improve significantly. This opens a lot of room for improvement. If the input video was significantly compressed, and yet you are satisfied with deinterlacing results, there is no need to push with 4X upscale. Rather, AI will gradually upscale with 1x or 2x steps. If you are missing details in the deinterlaced video, filters such as AI Deep Clean will in some cases add a bit of extra resolution to your videos. Think logically and don’t be afraid to try various combos knowing how different AI filters work in principle. One enhancement at a time.
Rule of thumb: the more input details you give, the more confusion may appear, as AI video enhancers have to do more interpolation.
Conclusion
AI video enhancement continues to evolve, opening new possibilities for video professionals. The key is to remain flexible, experiment with different approaches, and always prioritize quality over speed. With a hands-on mindset and a solid workflow, you can elevate your video projects to meet studio-grade standards.
