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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.
The DjVu developers report that color magazine pages compress to 40–70 kB, black-and-white technical papers compress to 15–40 kB, and ancient manuscripts compress to around 100 kB; a satisfactory JPEG image typically requires 500 kB. [4] Like PDF, DjVu can contain an OCR text layer, making it easy to perform copy and paste and text search ...
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.
Model compression (e.g. quantization and pruning of model parameters) can be applied to a deep neural network after it has been trained. [19] In the SqueezeNet paper, the authors demonstrated that a model compression technique called Deep Compression can be applied to SqueezeNet to further reduce the size of the parameter file from 5 MB to 500 ...
In contrast, lossy compression (e.g. JPEG for images, or MP3 and Opus for audio) can achieve much higher compression ratios at the cost of a decrease in quality, such as Bluetooth audio streaming, as visual or audio compression artifacts from loss of important information are introduced.
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.