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This gallery shows the results of a large number of image scaling algorithms which is too cumbersome to include in the main image scaling article.
Scaling methods
An image size can be changed in several ways. Consider quadrupling the size of the following photographic thumbnail image (40x40 pixel), and doubling the size of the following text based image:
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:
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 "stairway" shape characteristic of nearest-neighbor interpolation.
Other scaling methods below are better at preserving smooth contours in the image:
For example, bilinear interpolation produces the following results:
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 and Lanczos resampling.
Simple fourier based interpolation based on padding of the frequency domain with zero components (a smooth window based approach would reduce the ringing). Beside the good conservation (even recovering) of details, notable is the ringing and the circular bleeding of content from the left border to right border (and way around).
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.
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.
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 Illustrator 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.