4K video upscaling can turn 1080p footage into a 3,840-by-2,160-pixel frame. However, the process creates new pixels rather than recovering missing original detail.
A 1080p frame contains 1,920 by 1,080 pixels. Moving to 4K requires four times as many pixels overall. Therefore, software must generate three additional pixels for every original pixel.
Traditional upscaling uses interpolation. The software examines nearby pixels and estimates the colour and brightness needed to fill the larger frame.
This method can produce predictable results, but it often softens the image. Sharpening and noise reduction may help. However, excessive processing can make footage look unnatural.
AI video upscaling takes a different approach. Developers train AI models with examples of low- and high-resolution images. The software then predicts how sharper details might appear.
Read: Adobe Launches AI-Powered Generate Video Tool in Public Beta
AI can produce more convincing textures and edges than basic interpolation. Still, it cannot reconstruct the exact real-world detail that was never recorded. It may also create visual artefacts.
Source quality remains the biggest factor. Professional restorations often return to original film negatives or high-quality masters rather than compressed digital copies.
CBS followed that approach when restoring “Star Trek: The Next Generation.” The project used original 35mm film elements and reportedly took more than three years.
Older interlaced footage requires another step. Users generally need to deinterlace formats such as 480i before upscaling.
Bitrate also matters. Increasing resolution cannot remove compression artefacts such as blocking or banding from a poor source file.
As a result, converting 1080p to 4K increases frame size and can improve presentation. It does not automatically create true native 4 K detail.
