Upscaling video from 1080p to 4K is a common goal for viewers with modern displays, but the process involves significant limitations and trade-offs. While any video file can be resized to 4K, the challenge lies in the fact that moving from 1080p to 4K requires adding three additional pixels for every original pixel, quadrupling the total pixel count. Traditional interpolation software estimates what those new pixels should look like based on surrounding color and brightness, but it cannot recover detail that was never recorded, often resulting in softer footage. Sharpening and noise reduction tools can help, but applying too much or too little of either can degrade the image in different ways.
A prominent example of the difficulty of proper resolution upgrades is the HD remaster of Star Trek: The Next Generation, commissioned by CBS in 2011. The studio went back to the original 35mm film negatives, which hold roughly the equivalent of 20 megapixel resolution, to remaster all seven seasons from broadcast quality to 1080p for Blu-ray. That process used 25,000 reels of original film stock, included visual effects and CGI upgrades, and took over three years to complete. One of the producers reported that the effort cost the studio more than $12 million.
Working from analog film makes such upgrades easier because the original negatives contain far more detail than what made it into the original broadcasts. Digital master files, by contrast, are locked to a fixed resolution with a set number of pixels, leaving no extra data to recover. When the best available source is already low resolution, every missing pixel created by upscaling is essentially a guess, which can look convincing but will not be a perfect match to reality.
AI-based upscalers are emerging as an alternative to interpolation, as they are trained on pairs of low and high-resolution images. Instead of estimating each pixel mathematically, these models predict what a sharper version of the image should look like, which can yield sharper and more convincing results. However, AI upscalers can also introduce unusual artifacts that interpolation does not, and their quality is expected to improve over time as the models evolve.
For older video, interlacing is another factor to consider, as frames were once split into odd and even horizontal lines that refreshed alternately for CRT displays. Modern screens do not use interlacing, so such footage must be de-interlaced before upscaling, and even then, converting a 480i NTSC video to 4K may not produce watchable results. Upscaling tools generally offer configurable settings for both AI and interpolation methods, allowing users to tweak parameters and retry if a single pass is unsatisfactory, provided disk space is available.
The source material matters more than anything else in this process, as a smaller resolution leap yields better outcomes. Upscaling from 1440p to 4K requires fewer fabricated pixels than jumping from 1080p or even 480p to 4K. Compression also plays a role, because low-bitrate files lose data per second, and upscaling cannot undo blocky artifacts or banding that result from heavy compression. Ultimately, a like-for-like match to true 4K is not guaranteed, and the quality of the result depends almost entirely on the quality of what was originally recorded and stored.
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