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An image size can be changed in several ways. Consider resizing a 160x160 pixel photo to the following 40x40 pixel thumbnail and then scaling the thumbnail to a 160x160 pixel image. Also consider doubling the size of the following image containing text.
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. [1]
Image scaling can be interpreted as a form of image resampling or image reconstruction from the view of the Nyquist sampling theorem.According to the theorem, downsampling to a smaller image from a higher-resolution original can only be carried out after applying a suitable 2D anti-aliasing filter to prevent aliasing artifacts.
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They are also known as Lempel-Ziv 1 (LZ1) and Lempel-Ziv 2 (LZ2) respectively. [3] These two algorithms form the basis for many variations including LZW , LZSS , LZMA and others. Besides their academic influence, these algorithms formed the basis of several ubiquitous compression schemes, including GIF and the DEFLATE algorithm used in PNG and ...
2 triangles, example to show how fractal compression works. Fractal compression is a lossy compression method for digital images, based on fractals.The method is best suited for textures and natural images, relying on the fact that parts of an image often resemble other parts of the same image. [1]
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Like any resampling operation, changing image size and bit depth are lossy in all cases of downsampling, such as 30-bit to 24-bit or 24-bit to 8-bit palette-based images. While increasing bit depth is usually lossless, increasing image size can introduce aliasing or other undesired artifacts.