![]() This particular algorithm makes up for its clumsiness with a strong advantage: You can dictate just how much quality you want to remain with the image. The image suffers color loss and may introduce artifacts (weird pixellation at random points of the image) when used excessively. With transform encoding, an image’s colors are averaged out using a special mathematical formula called discrete cosine transform. ![]() The former is more common in images and the latter in video. There are actually two algorithms commonly used to compress images this way: transform encoding and chroma subsampling. But lossy doesn’t imply that you’re eliminating pixels. When taken too far, it can actually make the image unrecognizable. It’s kind of like how file compression works, except we’re dealing with smaller units of data.Īs the name implies, lossy compression makes an image lose some of its content. It does this by building an index of all the pixels and grouping same-colored pixels together. As a method, lossless compression minimizes distortion as much as possible, preserving image clarity. When you think of the word “lossless” in the context of image compression, you probably think about a method that tries its hardest to preserve quality while still maintaining a relatively small image size. For now, we’re going to talk about the methods that are generally used to compress images, which will explain why some of them take up so much less space. Each algorithm is represented by a file format (PNG, JPG, GIF, etc.). There are different methods, each with a unique approach to a common problem, and each approach being used in different algorithms to reach a similar conclusion. It’s naive to think that there’s just one way to compress an image.
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