What is Super-resolution? Super-Resolution is an image quality enhancement technology powered by artificial intelligence (AI). As video grew in popularity, so did past content. Some viewers get frustrated with the lack of quality on current displays, content service companies are leading to an interest in AI-enhanced "Super-resolution”. In this post, we're going to know what "Super-Resolution" is, and the advantages and disadvantages of using it to improve image quality, with the help of our image improvement experts at BLUEDOT.

Why is it good AI improving video quality.

Before the advent of "Super-Resolution," there were many ways to improve the quality of your footage. The most popular way to do this is to fill pixels with similar pixels between them. Videos are made of pixels. As the higher resolution of video, there will be empty spaces between pixels that make up the video. A "rule-based" approach is used to fill those spaces with the "best possible pixels that human think", but this method of video enhancement often results in a blurry overall look, with little detail.

Whereas, when using AI to improve video quality, video AI trained on many videos will look at the input video and find the best pixels to fill in the gaps between pixels, naturally improving the quality of the video. Especially, AI fixes the details of the video, such as removing noise and improving illumination, resulting in a more aesthetically pleasing high-quality video compared to traditional methods.

You input a low-resolution video, insert a high-resolution video into the output video, and train the AI model to say, "If I have a video like this, I want it to look like this”. We must train on a lot of different videos to find the optimal pixels for any given video. This is because when AI improves the image quality, it should be look so natural that you can't tell if it's a high-quality video enhance by AI or an original high-quality image.

Disadvantages of AI image quality improvement

However, applying Super-Resolution to improve video quality has a significant drawback in that it takes a long time. Furthermore, spending long time at computer to improve the quality of your footage is not only time-consuming but also usage of computing, as it increases the cost. However, since Super-Resolution is based on artificial intelligence, it takes a long time due to the underlying computation of deep learning.

AI mimics human "Neurons," which are responsible for transmitting signals. Each neuron requires one multiplication and one addition operation, which means that for an AI to mimic the hundreds of millions of neurons in the human body, it would have to perform hundreds of millions of multiplication and addition operations. As the size of the AI model increases, the amount of computation increases dramatically, and super-resolution, which process video contents, takes a long time due to the large number of computations.

Especially, videos have a large amount of data compared to text, require more computation because each pixel is viewed as a signal. A typical hour of FHD quality video requires about 4GB of data, so processing videos and images with AI is a time-consuming and somewhat challenging task.

Super-Resolution's competitive advantage is “Speed”.

Speed is to be a competitive advantage for developers of super-resolution technology because it requires significant computing resources to improve video quality. The most basic role of super-resolution is to improve video quality, and if this is the basic task, it can only become a competitive technology if it is developed with the following in mind: how much computing resources can be used, and whether it is cost-effective?

In this regard, BLUEDOT's video quality enhancement AI is seen to have sufficient competitiveness. The 'FHD (real-time) video quality enhancement and upscaling solution' developed by BLUEDOT boasts a fast video processing speed that few other super-resolution technologies have and can improve image quality at the real-time (30fps) level. In addition, the quality is never inferior to other super-resolution technologies, so you get the best of both worlds: time- and cost-efficiency and superior quality.