User Manual

Table Of Contents
Filter Method
When rescaling a pixel, surrounding pixels are often used to give a more realistic result. There
are various algorithms for combining these pixels, called filters. More complex filters can give
better results but are usually slower to calculate. The best filter for the job often depends on the
amount of scaling and on the contents of the image itself.
Box: This is a simple interpolation resize of the image.
Linear: This uses a simplistic filter, which produces relatively clean and fast results.
Quadratic: This filter produces a nominal result. It offers a good compromise between
speed and quality.
Cubic: This produces better results with continuous-tone images. If the images have
fine detail in them, the results may be blurrier than desired.
Catmull-Rom: This produces good results with continuous-tone images that are resized
down. This produces sharp results with finely detailed images.
Gaussian: This is very similar in speed and quality to Bi-Cubic.
Mitchell: This is similar to Catmull-Rom but produces better results with finely detailed
images. It is slower than Catmull-Rom.
Lanczos: This is very similar to Mitchell and Catmull-Rom but is a little cleaner and also
slower.
Sinc: This is an advanced filter that produces very sharp, detailed results; however, it
may produce visible “ringing” in some situations.
Bessel: This is similar to the Sinc filter but may be slightly faster.
Window Method (Sinc and Bessel Only)
Some filters, such as Sinc and Bessel, require an infinite number of pixels to calculate exactly.
To speed up this operation, a windowing function is used to approximate the filter and limit the
number of pixels required. This control appears when a filter that requires windowing
is selected.
Hanning: This is a simple tapered window.
Hamming: Hamming is a slightly tweaked version of Hanning that does not taper all the
way down to zero.
Blackman: A window with a more sharply tapered falloff.
Kaiser: A more complex window with results between Hamming and Blackman.
Most of these filters are useful only when making an image larger. When shrinking images, it is
common to use the Bi-Linear filter, however, the Catmull-Rom filter will apply some sharpening
to the results and may be useful for preserving detail when scaling down an image.
Example
Resize filters. From left to right: Nearest Neighbor, Box, Linear, Quadratic, Cubic,
Catmull-Rom, Gaussian, Mitchell, Lanczos, Sinc, and Bessel.
Chapter – 108 Transform Nodes 2441