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Instead, just eliminate JPEG artifacts as much as possible and convert to 24-bit PNG. Using the posterize option of your graphics editor with a high setting (like 40) may further reduce filesize and eliminate more artifacts without visibly reducing image quality. You can try using pngquant on the 24-bit result; this produces indexed mode images ...
The best image quality at a given compression rate (or bit rate) is the main goal of image compression, however, there are other important properties of image compression schemes: Scalability generally refers to a quality reduction achieved by manipulation of the bitstream or file (without decompression and re-compression).
Left side of the image is from a low-quality JPEG image, showing lossy artefacts; the right side is from a PNG image. In information technology, lossy compression or irreversible compression is the class of data compression methods that uses inexact approximations and partial data discarding to represent the content. These techniques are used ...
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.
Using PNG instead of a high-quality JPEG for such images would result in a large increase in file size with negligible gain in quality. In comparison, when storing images that contain text, line art, or graphics – images with sharp transitions and large areas of solid color – the PNG format can compress image data more than JPEG can.
The main purpose of pngcrush is to reduce the size of the PNG IDAT data stream by trying various combinations of compression methods and delta filters. It can also be used for various manipulations of PNG images, such as changing the bit depth , removing unwanted ancillary chunks , or adding certain chunks including gAMA, tRNS, iCCP, and ...
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Most lossless compression programs do two things in sequence: the first step generates a statistical model for the input data, and the second step uses this model to map input data to bit sequences in such a way that "probable" (i.e. frequently encountered) data will produce shorter output than "improbable" data.
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