# When to use bicubic interpolation in realtime?

## When to use bicubic interpolation in realtime?

Bicubic interpolation can also used in realtime rendering to make textures look nicer when scaled than standard bilinear texture interpolation. This technique works when making images larger as well as smaller, but when making images smaller, you can still have problems with aliasing.

**Which is better, nearest neighbor intepolation or bi cubic interpolation?**

Nearest neighbor intepolation gives us a square wave while linear interpolation gives us something that might be too smooth. Bi-Cubic interpolation achieves results between these two choices. It estimates how sharp and edge there should be by estimating the derivatives at each sample and then fitting a cubic curve between the samples.

**What is the process of linear interpolation called?**

Linear interpolation requires an extension into two dimensions. We linearly interpolate along each dimension, so the process is called bi-linear intepolation. For the doubling case above, the pixel e would be halfway between A and B (by linear interpolation). Similarly pixels f, h, and i can be found.

### Can a linear interpolation be implemented by a kernel?

The linear interpolation can be implemented by the kernel [.5 1 .5]. For other spacings, we just use other kernels. For example, the nearest neighbor kernel for tripling is [1 1], and the linear interpolation kernel is 1/3 [1 2 3 2 1]. Other kernels give different reconstructions.

**When to use a third degree polynomial in cubic interpolation?**

Please contact me if you find an error. If the values of a function f (x) and its derivative are known at x=0 and x=1, then the function can be interpolated on the interval [0,1] using a third degree polynomial. This is called cubic interpolation. The formula of this polynomial can be easily derived.

**What is the purpose of two dimensional interpolation?**

Two-dimensional interpolation finds its way in many applications like image processing and digital terrain modeling. Here we will compare different interpolation strategies. The purpose of the interpolation is to “densify” a sparse data set by creating intermediate points.