How does upscaling work

Handle this badly and it can look like overcooked picture sharpening, a feature seen in the menus of most TVs, and which we usually advise turning all the way down. Take a piece of p content.

But 4K TVs have almost 8. The simplest way to fill in those additional pixels is doubling, where blocks of pixels repeat the same visual information, leading to block images that are better for retro games than they are for home movies.

Another approach is creating interstitial pixels, smoothing out transitions between pixels in low-res sources. A bright pixel next to a dark pixel in the source video would therefore be bordered by mid-level brightness pixels in the final upscaled image. This results in a soft image. The most pleasing explanation of AI upscaling to the human brain is that these TV processors can recognise objects such as grass, fur or eyelashes and fill in the detail missing in the source image.

Of course not. Nor is it something to get too upset about. Browse All iPhone Articles Browse All Mac Articles Do I need one? Browse All Android Articles Browse All Smart Home Articles Customize the Taskbar in Windows Browse All Microsoft Office Articles What Is svchost.

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Owners of ultra-HD displays get the most out of their screens when watching 4K-mastered content. But when watching lower-resolution content, the video must be upscaled to fill out the entire display. For example, p images, known as full HD, have just a quarter of the pixels in 4K images. Upscaling is done by the streaming device being used — such as a smart TV or streaming media player.

But typically, media players use basic upscaling algorithms that are unable to significantly improve high-definition content for 4K TVs. Basic upscaling is the simplest way of stretching a lower resolution image onto a larger display. Pixels from the lower resolution image are copied and repeated to fill out all the pixels of the higher resolution display.

Filtering is applied to smooth the image and round out unwanted jagged edges that may become visible due to the stretching. Often, however, this is not an ideal way to extract maximum quality from your source. It is possible to achieve meaningful improvements in image quality with traditional video editing tools. One of the typical ways to train a video-enhancing application such as Video Enhance AI is to provide the neural net with the same image or video in both high and low quality. The neural net is then tasked with finding a way to make the low-quality source look as much like the high-quality source as possible.

And, again, a slider for comparison. With a little color grading, a fairly dramatic overall improvement can be achieved. This slider shows a base frame on one side and the final output, with color grading, on the other.

Upscalers are amazing and only getting better. Depending on the shows you like and how much time you want to sink into the project, there are tremendous improvements to be had… or you can just wait a few years, and buy a better TV. You can read more about DS9UP at the links below. Feature image on top shows the impact of upscaling as well as color correction via Photoshop.