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  2. pngcrush - Wikipedia

    en.wikipedia.org/wiki/Pngcrush

    It reduces the size of the file losslessly – that is, the resulting "crushed" image will have the same quality as the source image. 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.

  3. Guetzli - Wikipedia

    en.wikipedia.org/wiki/Guetzli

    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.

  4. Image compression - Wikipedia

    en.wikipedia.org/wiki/Image_compression

    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.

  5. JPEGmini - Wikipedia

    en.wikipedia.org/wiki/JPEGmini

    Image optimization: Can compresses file size of JPEG images by up to 80% without compromising image quality. It also optimizes high-resolution photos, with a resolution of up to 256MP. It also optimizes high-resolution photos, with a resolution of up to 256MP.

  6. libjpeg - Wikipedia

    en.wikipedia.org/wiki/Libjpeg

    The original variant is maintained and published by the Independent JPEG Group (IJG). Meanwhile, there are several forks with additional features. JPEG JFIF images are widely used on the Web. The amount of compression can be adjusted to achieve the desired trade-off between file size and visual quality. [4]

  7. Quantization (image processing) - Wikipedia

    en.wikipedia.org/wiki/Quantization_(image...

    Quantization, involved in image processing, is a lossy compression technique achieved by compressing a range of values to a single quantum (discrete) value. When the number of discrete symbols in a given stream is reduced, the stream becomes more compressible.

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