Comparison gallery of image scaling algorithms

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:

Reference 160x160 pixel thumbnail

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:

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:

Bilinear interpolation

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.

Bicubic interpolation

Better scaling methods include bicubic interpolation and Lanczos resampling.

Fourier-based interpolation

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

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.

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.

Supersampling

File:Image-before-scaling(noise scale)(x2.0)(level2)(noise scale)(x2.0)(level2).png

For scaling photos (and raster images with many colors), see also anti-aliasing algorithms called supersampling.

Vectorization

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.

SFG conversion

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.

Mipmap

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.