Computers or images for that matter are at a good place when tasked with displaying square oriented graphical objects. There is also an inherent problem when it comes to Raster images that are usually fixed with anti-aliasing.
To understand anti-aliasing you need to understand what raster images are or in other loose terms bitmaps. Images are usually divided into two types when it comes to digital imaging. The first is vector based images.
Examples are the Adobe Illustrator format, the CorelDraw format, and the Scalable Vector Graphics (SVG) formats. Then we have the raster images or bitmaps. These are common on the Internet and in photography. You may all have heard of the likes of Photoshop and common image formats such as JPEG and PNG.
Without going into too much detail, we will be discussing those more in separate articles. The links will be provided once they are published publicly.
Back to Anti Aliasing.
The type of images the implement anti-aliasing are the raster images. In short, therefore, these type of images are made up of a map or 2D grid of tiny dots known as pixels. These pixels are of standard size and consist of any number of colors based on the bit storage format. There is an article here that explains more about the memory bits.
These dots are actually square in shape and it takes several of them to make up a complete image. The number of dots that span an image horizontally and vertically is called the resolution. You can see the image above as a reference.
The more pixels that can be packed in a certain span gives the image a higher resolution. This makes the image smoother when you have to deal with diagonal lines and curves in an image. If the resolution is too low to some extent you will notice curves and diagonals look like staircases or jagged edges and that does not play well with what we perceive with our eyes.
Enter Anti Aliasing
Anti-aliasing is, therefore, is the application of algorithms to smooth out jagged edges on curved lines and diagonals in an image. Graphics software does this by examining the contrast between two pixels and finding an average. It then applies that average to the adjacent pixels to give a smooth transition. The resulting effect is the easing of the jagged edges thereby simulating smooth diagonal lines and curves.
Found this article interesting? Follow Brightwhiz on Facebook, Twitter, and YouTube to read and watch more content we post.