It’s a big wide world of curves out there and I have to say that most of the time, I consider myself a Bezier man.

Well let me tell you… cubic Hermite splines are technically representable in Bezier form, but they have some really awesome properties that I never fully appreciated until recently.

**Usefulness For Interpolation**

If you have a set of data points on some fixed interval (like for audio data, but could be anything), you can use a cubic Hermite spline to interpolate between any two data points. It interpolates the value between those points (as in, it passes through both end points), but it also interpolates a derivative that is consistent if you approach the point from the left or the right.

In short, this means you can use cubic Hermite splines to interpolate data such that the result has continuity everywhere!

**Usefulness As Curves**

If you have any number control points on a fixed interval, you can treat it as a bunch of piece wise cubic Hermite splines and evaluate it that way.

The end result is that you have a curve that is continuous everywhere, it has local control (moving any control point only affects the two curve sections to the left and the two curve sections to the right), and best of all, the computational complexity doesn’t rise as you increase the number of control points!

The image below was taken as a screenshot from one of the HTML5 demos I made for you to play with. You can find links to them at the end of this post.

## Cubic Hermite Splines

Cubic Hermite splines have four control points but how it uses the control points is a bit different than you’d expect.

The curve itself passes only through the middle two control points, and the end control points are there to help calculate the tangent at the middle control points.

Let’s say you have control points . The curve at time 0 will be at point and the slope will be the same slope as a line would have if going from to . The curve at time 1 will be at point and the slope will be the same slope as a line would have if going from to .

Check out the picture below to see what I mean visually.

That sounds like a strange set of properties, but they are actually super useful.

What this means is that you can treat any group of 4 control points / data points as a separate cubic hermite spline, but when you put it all together, it is a single smooth curve.

Note that you can either interpolate 1d data, or you can interpolate 2d data points by doing this interpolation on each axis. You could also use this to make a surface, which will likely be the next blog post!

## The Math

I won’t go into how the formula is derived, but if you are interested you should check out Signal Processing: Bicubic Interpolation.

The formula is:

Where…

Note that t is a value that goes from 0 to 1. When t is 0, your curve will be at and when t is 1, your curve will be at . and are used to be able to make this interpolation continuous.

Here it is in some simple C++:

// t is a value that goes from 0 to 1 to interpolate in a C1 continuous way across uniformly sampled data points. // when t is 0, this will return B. When t is 1, this will return C. static float CubicHermite (float A, float B, float C, float D, float t) { float a = -A/2.0f + (3.0f*B)/2.0f - (3.0f*C)/2.0f + D/2.0f; float b = A - (5.0f*B)/2.0f + 2.0f*C - D / 2.0f; float c = -A/2.0f + C/2.0f; float d = B; return a*t*t*t + b*t*t + c*t + d; }

## Code

Here is an example C++ program that interpolates both 1D and 2D data.

#include <stdio.h> #include <vector> #include <array> typedef std::vector<float> TPointList1D; typedef std::vector<std::array<float,2>> TPointList2D; void WaitForEnter () { printf("Press Enter to quit"); fflush(stdin); getchar(); } // t is a value that goes from 0 to 1 to interpolate in a C1 continuous way across uniformly sampled data points. // when t is 0, this will return B. When t is 1, this will return C. float CubicHermite (float A, float B, float C, float D, float t) { float a = -A/2.0f + (3.0f*B)/2.0f - (3.0f*C)/2.0f + D/2.0f; float b = A - (5.0f*B)/2.0f + 2.0f*C - D / 2.0f; float c = -A/2.0f + C/2.0f; float d = B; return a*t*t*t + b*t*t + c*t + d; } template <typename T> inline T GetIndexClamped(const std::vector<T>& points, int index) { if (index < 0) return points[0]; else if (index >= int(points.size())) return points.back(); else return points[index]; } int main (int argc, char **argv) { const float c_numSamples = 13; // show some 1d interpolated values { const TPointList1D points = { 0.0f, 1.6f, 2.3f, 3.5f, 4.3f, 5.9f, 6.8f }; printf("1d interpolated values. y = f(t)\n"); for (int i = 0; i < c_numSamples; ++i) { float percent = ((float)i) / (float(c_numSamples - 1)); float x = (points.size()-1) * percent; int index = int(x); float t = x - floor(x); float A = GetIndexClamped(points, index - 1); float B = GetIndexClamped(points, index + 0); float C = GetIndexClamped(points, index + 1); float D = GetIndexClamped(points, index + 2); float y = CubicHermite(A, B, C, D, t); printf(" Value at %0.2f = %0.2f\n", x, y); } printf("\n"); } // show some 2d interpolated values { const TPointList2D points = { { 0.0f, 1.1f }, { 1.6f, 8.3f }, { 2.3f, 6.5f }, { 3.5f, 4.7f }, { 4.3f, 3.1f }, { 5.9f, 7.5f }, { 6.8f, 0.0f } }; printf("2d interpolated values. x = f(t), y = f(t)\n"); for (int i = 0; i < c_numSamples; ++i) { float percent = ((float)i) / (float(c_numSamples - 1)); float x = 0.0f; float y = 0.0f; float tx = (points.size() -1) * percent; int index = int(tx); float t = tx - floor(tx); std::array<float, 2> A = GetIndexClamped(points, index - 1); std::array<float, 2> B = GetIndexClamped(points, index + 0); std::array<float, 2> C = GetIndexClamped(points, index + 1); std::array<float, 2> D = GetIndexClamped(points, index + 2); x = CubicHermite(A[0], B[0], C[0], D[0], t); y = CubicHermite(A[1], B[1], C[1], D[1], t); printf(" Value at %0.2f = (%0.2f, %0.2f)\n", tx, x, y); } printf("\n"); } WaitForEnter(); return 0; }

The output of the program is below:

## Links

Here are some interactive HTML5 demos i made:

1D cubic hermite interpolation

2D cubic hermite interpolation

More info here:

Wikipedia: Cubic Hermite Spline

Closely related to cubic hermite splines, catmull-rom splines allow you to specify a “tension” parameter to make the result more or less curvy:

Catmull-Rom spline