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Guetzli optimizes the quantization step of encoding to achieve compression efficiency. It constructs custom quantization tables for each file, decides on color subsampling, [4] and quantizes adjacent DCT coefficients to zero, balancing benefits in the run-length encoding of coefficients and preservation of perceived image fidelity.
Image compression is a type of data compression applied to digital images, to reduce their cost for storage or transmission. Algorithms may take advantage of visual perception and the statistical properties of image data to provide superior results compared with generic data compression methods which are used for other digital data.
Compression of an image to reduce file size (in Kb) is usually "lossy" and is not advised for featured pictures. Image compression will reduce download times and save disk space, but it does so at the expense of fine detail and overall image quality. If in doubt, when saving JPEG files, always select the "maximum" quality setting.
Lossless compression of digitized data such as video, digitized film, and audio preserves all the information, but it does not generally achieve compression ratio much better than 2:1 because of the intrinsic entropy of the data. Compression algorithms which provide higher ratios either incur very large overheads or work only for specific data ...
Lossy compression algorithms preserve a representation of the original uncompressed image that may appear to be a perfect copy, but is not a perfect copy. Often lossy compression is able to achieve smaller file sizes than lossless compression. Most lossy compression algorithms allow for variable compression that trades image quality for file size.
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