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In computer graphics, image scaling is the process of resizing a digital image. Scaling is a non-trivial process that involves a trade-off between efficiency, smoothness and sharpness. With bitmap graphics, as the size of an image is reduced or enlarged, the pixels that form the image become increasingly visible, making the image appear "soft" if pixels are averaged, or jagged if not. With vector graphics the trade-off may be in processing power for re-rendering the image, which may be noticeable as slow re-rendering with still graphics, or slower frame rate and frame skipping in computer animation. Apart from fitting a smaller display area, image size is most commonly decreased (or subsampled or downsampled) in order to produce thumbnails. Enlarging an image (upsampling or interpolating) is generally common for making smaller imagery fit a bigger screen in fullscreen mode, for example. In “zooming” a bitmap image, it is not possible to discover any more information in the image than already exists, and image quality inevitably suffers. However, there are several methods of increasing the number of pixels that an image contains, which evens out the appearance of the original pixels. == Scaling methods == An image size can be changed in several ways. Consider quadrupling the size of the following photographic thumbnail image, and doubling the size of the following text based image: Thumbnail Image Original Image ;Nearest-neighbor interpolation One of the simpler ways of increasing the size is nearest-neighbor interpolation, replacing every pixel with a number of pixels of the same color: Nearest-neighbor interpolation The resulting image is larger than the original, and preserves all the original detail, but has (possibly undesirable) jaggedness. The diagonal lines of the W, for example, now show the characteristic "stairway" shape. Other scaling methods below are better at preserving smooth contours in the image: ;Bilinear interpolation For example, bilinear interpolation produces the following results: Bilinear interpolation Linear Interpolation Linear (or bilinear, in two dimensions) interpolation is typically good for changing the size of an image, but causes some undesirable softening of details and can still be somewhat jagged. Better scaling methods include bicubic interpolation ''(examples below)'' and Lanczos resampling. Bicubic Interpolation Cubic Interpolation ;Edge-directed interpolation algorithms Edge-directed interpolation algorithms aim to preserve edges in the image after scaling, unlike other algorithms which can produce staircase artifacts around diagonal lines or curves. Examples of algorithms for this task include New Edge-Directed Interpolation (NEDI), Edge-Guided Image Interpolation (EGGI), Iterative Curvature-Based Interpolation (ICBI), and Directional Cubic Convolution Interpolation (DCCI). An article from 2013 compared the four algorithms above, and found that DCCI had the best scores in PSNR and SSIM on a series of test images. File:40 by 40 thumbnail of 'Green Sea Shell' (x4 DCCI).png File:Wiki dcci 2x.png ;hqx For magnifying computer graphics with low resolution and/or few colors (usually from 2 to 256 colors), better results will be achieved by hqx or other pixel art scaling algorithms. These produce sharp edges and maintain high level of detail. hq4x scaling hq2x scaling ;Supersampling For scaling photos (and raster images with many colors), see also anti-aliasing algorithms called ''supersampling''. ;Vectorization Vectorization, (Vector Magic) An entirely different approach is vector extraction or vectorization. Vectorization first creates a resolution independent vector representation of the graphic to be scaled. Then the resolution-independent version is rendered as a raster image at the desired resolution. This technique is used by Adobe Live Trace, inkscape, and several recent papers. Scalable Vector Graphics are well suited to simple geometric images, while photographs do not fare well with vectorization due to their complexity. ;SFG conversion SFG conversion, (PhotoFunction) SFG conversion, (PhotoFunction) Another approach is scalable function graphic conversion. As with vectorization, a conversion process creates a resolution independent representation of the graphic to be scaled. The conversion requires a large amount of processing time, but the resulting function is capable of scaling complex images such as photographs.〔(【引用サイトリンク】url=http://hplusmagazine.com/2014/12/11/gain-superpowered-vision-scalable-function-graphics/ )〕 ;Mipmap An example of mipmap image storage. A unique problem occurs with downscaling. A scaling algorithm that relies on sampling a specific number of pixels would sample non-adjacent pixels when downscaling below a certain threshold, which can break the sampling and produce an unsmooth result. This can be avoided by using box sampling or a mipmap which contains many already geometrically downscaled copies. Any simple scaling algorithm can then be used on one of the prescaled copies and give an accurate result. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Image scaling」の詳細全文を読む スポンサード リンク
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