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Lossless Image Compression: What You Should Know

We need to compress the data in a lossless format to speed it up. The image will look like one big blob of data that takes up more  space, but we only need a small portion of it. Compress the data in a Lossless format — Wikipedia We all know how to compress an image. We can either use a lossy compression algorithm to reduce the sizes of the image, lossless compression, where data is packed into a byte stream or  bitmap compression. This article explains the basics of each of those. JPEG Lossy Compression: Image Compression in JPEG — Wikipedia The information in JPEG image files can be divided into three basic characteristics : “color, white balance, contrast, hue, and saturation of the image.” We can compress these characteristics into a single  compression algorithm known as the Lossy Compression algorithm, which “lowers the file size by a factor of two for the same  quality.” The problem is that the compression algorithm uses “losing factors” in order to achieve the target level of a lossy compression.  There is a  lot of information in the JPEG format, and we want to compress it so that the lossy compression can work more efficiently. You can also use the Huffman algorithm to reduce the size of the image, and it will produce  better quality, but it's slower to process. These can cause image loss, and some users are unhappy when their  images are reduced in size. What is Lossless Image Compression? While lossless image compression usually refers to color images and lossless picture compression to reduce file sizes, we can  use these two types of compression methods to compress GIFs into smaller GIFs, and vice-versa, or even reduce the  size of WebP files to save bandwidth. There are other types of decompression techniques used here as well, such as Huffman,  and that's not the issue. The problem is that lossless image compression is not very accurate. Sometimes the compression is too slow at handling  the compression algorithm, and this can cause compression artifacts in your image or even distort the image completely. The  problem can be solved by improving your algorithm and making it more efficient.