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  2. 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.

  3. Wikipedia : Featured picture criteria/Image size

    en.wikipedia.org/wiki/Wikipedia:Featured_picture...

    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.

  4. JPEG - Wikipedia

    en.wikipedia.org/wiki/JPEG

    This is an accepted version of this page This is the latest accepted revision, reviewed on 31 December 2024. Lossy compression method for reducing the size of digital images For other uses, see JPEG (disambiguation). "JPG" and "Jpg" redirect here. For other uses, see JPG (disambiguation). JPEG A photo of a European wildcat with the compression rate, and associated losses, decreasing from left ...

  5. SqueezeNet - Wikipedia

    en.wikipedia.org/wiki/SqueezeNet

    SqueezeNet was originally described in SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size. [1] AlexNet is a deep neural network that has 240 MB of parameters, and SqueezeNet has just 5 MB of parameters. This small model size can more easily fit into computer memory and can more easily be transmitted over a ...

  6. Data compression - Wikipedia

    en.wikipedia.org/wiki/Data_compression

    Data compression aims to reduce the size of data files, enhancing storage efficiency and speeding up data transmission. K-means clustering, an unsupervised machine learning algorithm, is employed to partition a dataset into a specified number of clusters, k, each represented by the centroid of its points. This process condenses extensive ...

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