18.06 Linear Algebra, Spring 2010
Transcript – Lecture 31
OK. So, coming nearer the end of the course, this lecture will be a mixture of the
linear algebra that comes with a change of basis.
And a change of basis from one basis to another basis is something you really do in
And, I would like to talk about those applications.
I got a little bit involved with compression.
Compressing a signal, compressing an image.
And that's exactly change-of-basis.
And then, the main theme in this chapter is th- the connection between a linear
transformation, which doesn't have to have coordinates, and the matrix that tells us
that transformation with respect to coordinates. So the matrix is the coordinate-
based description of the linear transformation. Let me start out with the nice part,
which is just to tell you something about image compression. Those of you -- well,
everybody's going to meet compression, because you know that the amount of data
that we're getting -- well, these lectures are compressed. So that, actually, probably
you see my motion as jerky? Shall I use that word? Have you looked on the web? I
should like to find a better word.
Compressed, let's say. So the complete signal is, of course, in those video cameras,
and in the videotape, but that goes to the bottom of building nine, and out of that
comes a jumpy motion because it uses a standard system for compressing images.
And, you'll notice that the stuff that sits on the board comes very clearly, but it's my
motion that needs a whole lot of bits, right? So, and if I were to run up and back up
there and back, that would need too many bits, and I'd be compressed even more.
So, what does compression mean? Let me just think of a still image.
And of course, satellites, and computations of the climate, computations of
combustion, the computers and sensors of all kinds are just giving us overwhelming
amounts of data. The Web is, too.
Now, some compression can be done with no loss.
Lossless compression is possible just using, sort of, the fact that there are
But I'm talking here about lossy compression.
So I'm talking about -- here's an image.