• Interpolation to the nearest neighbor—

Nearest-neighbor interpolation is a simple way to increase image size and doesn’t require complex calculations. Hence, it offers faster results, but the quality of the output might not be as fine as those obtained with other advanced algorithms. Here, the interpolated pixels take on the values of the nearest pixels. However, nearest here doesn’t necessarily mean mathematically closest. Nearest- neighbor interpolation is a great way to preserve fine details present in an input but can also create jaggedness in images that were previously smooth. The method is relatively faster and the results generally have fewer artifacts. 

• Sinc and Lanczos resampling—

This is probably the most complicated algorithm in the list. The Lanczos interpolation works like magic when it comes to preserving detail, and minimizing artifacts. The process using which it accomplishes this is complex and not easy to understand. This method produces an interpolation result similar to bicubic interpolation. The output has more of a ringed effect rather than a blurry one. Sometimes, you will see a dark or light halo along sharp edges of the rendered image. Although it may not be desired, the effect increases the perceived sharpness and clarity of an image. The Lanczos technique can be used to receive sharper and cleaner output content. Note that it is more suitable for video upscaling than still images.

• Edge-directed interpolation algorithms—

These algorithms are designed to keep the edges in an image intact even after upscaling. This is unlike certain other algorithms which often cause staircase artifacts to appear.Edge-directed interpolation can be achieved using algorithms such as DCCI or Directional Cubic Convolution Interpolation, ICBI or Iterative Curvature-Based Interpolation, EGGI or Edge-Guided Image Interpolation and NEDI or New Edge-Directed Interpolation.

• Bilinear algorithm—

When linear interpolation is applied in two directions, it is referred to as bilinear interpolation. It targets 4 closest pixels, and takes their weighted average for the output. It’s a great option for resizing, but causes softening of edges which might be undesirable in some cases. This method is ideal for continuous tone images. Bilinear interpolation produces significantly better results but at a higher computational cost.

• Hqx—

This scaling algorithm can provide better magnifying results for computer graphics that have low resolution or fewer colors (typically between 2 and 256 colors). It can produce sharp edges and high levels of detail.

• Vectorization—

Vectorization or vector extraction is another option. Vectorization first creates a resolution-independent vector representation of the graphic to be scaled. Then the resolution-independent version is rendered as a raster image at the desired resolution. A lot of upscaling software programs use this technique. 

• Deep convolutional neural networks—

This technique uses machine learning to create more detailed images, such as photos and intricate artwork. This algorithm is also used by a lot of popular media scaling software tools.

It’s important to note here that upscaling does not apply to old footage alone. A lot of people use upscaling tools for their YouTube channels. This may be required when the videos are shot at 1080p or lower resolutions. Such renders can easily be upscaled to 4K on YouTube using upscaling tools. But why would someone do that? Well, they probably don’t own a 4K-capable camera. Besides, editing a 4K footage is not an easy task. Also, 4K footage can take up a lot of space on the drive.

Now, the question that arises here is— How is AI so effective in upscaling media? Well, AI learns, just like humans do.