Scalable Vector Graphics are well suited to simple geometric images, while photographs do not fare well with vectorization due to their complexity.ĭeep convolutional neural networks This technique is used by Adobe Illustrator, Live Trace, and Inkscape. Then the resolution-independent version is rendered as a raster image at the desired resolution. Vectorization first creates a resolution-independent vector representation of the graphic to be scaled. Vector extraction, or vectorization, offer another approach. These produce sharp edges and maintain high level of detail.
hqx įor magnifying computer graphics with low resolution and/or few colors (usually from 2 to 256 colors), better results can be achieved by hqx or other pixel-art scaling algorithms.
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A 2013 analysis found that DCCI had the best scores in PSNR and SSIM on a series of test images. Besides the good conservation (or recovery) of details, notable is the ringing and the circular bleeding of content from the left border to right border (and the other way around).Įdge-directed interpolation algorithms aim to preserve edges in the image after scaling, unlike other algorithms, which can introduce staircase artifacts.Įxamples 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). Simple interpolation based on Fourier transform pads the frequency domain with zero components (a smooth window-based approach would reduce the ringing). The cost is using more image memory, exactly one third more in the standard implementation. It is standard in many frameworks such as OpenGL. This algorithm is fast, and easy to optimize. When downscaling the nearest larger mipmap is used as the origin, to ensure no scaling below the useful threshold of bilinear scaling is used. A mipmap is a prescaled set of downscale copies. The major weakness of this algorithm is that it is hard to optimize.Īnother solution to the downscale problem of bi-sampling scaling are mipmaps. This ensures that all input pixels contribute to the output. The trivial solution to this issue is box sampling, which is to consider the target pixel a box on the original image, and sample all pixels inside the box. When down scaling below a certain threshold, such as more than twice for all bi-sampling algorithms, the algorithms will sample non-adjacent pixels, which results in both losing data, and causes rough results.
One weakness of bilinear, bicubic and related algorithms is that they sample a specific number of pixels. Bicubic interpolation can be regarded as a computationally efficient approximation to Lanczos resampling. Lanczos resampling, an approximation to the sinc method, yields better results. In practice, the assumptions behind sinc resampling are not completely met by real-world digital images. Sinc resampling in theory provides the best possible reconstruction for a perfectly bandlimited signal. Bicubic interpolation yields substantially better results, with an increase in computational cost. Although this is desirable for continuous-tone images, this algorithm reduces contrast (sharp edges) in a way that may be undesirable for line art. Rounding this way produces fewer artifacts and is faster to calculate.īilinear interpolation works by interpolating pixel color values, introducing a continuous transition into the output even where the original material has discrete transitions. One common implementation is to always round towards zero. 'Nearest' in nearest-neighbor doesn't have to be the mathematical nearest.
This can preserve sharp details in pixel art, but also introduce jaggedness in previously smooth images. One of the simpler ways of increasing image size is nearest-neighbor interpolation, replacing every pixel with the nearest pixel in the output for upscaling this means multiple pixels of the same color will be present. See also: Comparison gallery of image scaling algorithmsĪn image size can be changed in several ways.
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