pixbuf-transform-math.ltx

linux下电话本所依赖的一些图形库

\documentclass{article}

\begin{document}

\title{Some image transform math}
\author{Owen Taylor}
\date{18 February 2003}
\maketitle

\section{Basics}

The transform process is composed of three steps;
first we reconstruct a continuous image from the 
source data \(A_{i,j}\):
\[a(u,v) = \sum_{i = -\infty}^{\infty} \sum_{j = -\infty}^{\infty} A_{i,j}F\left( {u - i \atop v - j} \right) \]
Then we transform from destination coordinates to source coordinates:
\[b(x,y) = a\left(u(x,y) \atop v(x,y)\right)
         = a\left(t_{00}x + t_{01}y + t_{02} \atop t_{10}x + t_{11}y + t_{12} \right)\]
Finally, we resample using a sampling function \(G\):
\[B_{x_0,y_0} = \int_{-\infty}^{\infty}\int_{-\infty}^{\infty} b(x,y)G\left( {x - x_0 \atop y - y_0} \right) dxdy\]
Putting all of these together:
\[B_{x_0,y_0} = 
\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}
\sum_{i = -\infty}^{\infty} \sum_{j = -\infty}^{\infty} A_{i,j}
F\left( {u(x,y) - i \atop v(x,y) - j} \right)
G\left( {x - x_0 \atop y - y_0} \right) dxdy\]
We can reverse the order of the integrals and the sums:
\[B_{x_0,y_0} = 
\sum_{i = -\infty}^{\infty} \sum_{j = -\infty}^{\infty} A_{i,j}
\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}
F\left( {u(x,y) - i \atop v(x,y) - j} \right)
G\left( {x - x_0 \atop y - y_0} \right) dxdy\]
Which shows that the destination pixel values are a linear combination of the 
source pixel values. But the coefficents depend on \(x_0\) and \(y_0\). 
To simplify this a bit, define:
\[i_0 = \lfloor u(x_0,y_0) \rfloor = \lfloor {t_{00}x_0 + t_{01}y_0 + t_{02}} \rfloor \]
\[j_0 = \lfloor v(x_0,y_0) \rfloor = \lfloor {t_{10}x_0 + t_{11}y_0 + t_{12}} \rfloor \]
\[\Delta_u = u(x_0,y_0) - i_0 = t_{00}x_0 + t_{01}y_0 + t_{02} - \lfloor {t_{00}x_0 + t_{01}y_0 + t_{02}} \rfloor \]
\[\Delta_v = v(x_0,y_0) - j_0 = t_{10}x_0 + t_{11}y_0 + t_{12} - \lfloor {t_{10}x_0 + t_{11}y_0 + t_{12}} \rfloor \]
Then making the transforms \(x' = x - x_0\), \(y' = y - x_0\), \(i' = i - i_0\), \(j' = j - x_0\)
\begin{eqnarray*}
F(u,v) & = & F\left( {t_{00}x + t_{01}y + t_{02} - i \atop t_{10}x + t_{11}y + t_{12} - j} \right)\\
       & = & F\left( {t_{00}(x'+x_0) + t_{01}(y'+y_0) + t_{02} - (i'+i_0) \atop 
                      t_{10}(x'+x_0) + t_{11}(y'+y_0) + t_{12} - (j'+j_0)} \right) \\
       & = & F\left( {\Delta_u + t_{00}x' + t_{01}y' - i' \atop 
                      \Delta_v + t_{10}x' + t_{11}y' - j'} \right)
\end{eqnarray*}
Using that, we can then reparameterize the sums and integrals and
define coefficients that depend only on \((\Delta_u,\Delta_v)\),
which we'll call the \emph{phase} at the point \((x_0,y_0)\):
\[
B_{x_0,y_0} = 
\sum_{i = -\infty}^{\infty} \sum_{j = -\infty}^{\infty} A_{i_0+i,j_0+j} C_{i,j}(\Delta_u,\Delta_v)
\]
\[
C_{i,j}(\Delta_u,\Delta_v) =
\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}
F\left( {\Delta_u + t_{00}x + t_{01}y - i \atop 
         \Delta_v + t_{10}x + t_{11}y - j} \right)
G\left( {x \atop y} \right) dxdy
\]
\section{Separability}
A frequent special case is when the reconstruction and sampling functions
are of the form:
\[F(u,v) = f(u)f(v)\]
\[G(x,y) = g(x)g(y)\]
If we also have a transform that is purely a scale and translation;
(\(t_{10} = 0\), \(t_{01} = 0\)), then we can separate 
\(C_{i,j}(\Delta_u,\Delta_v)\) into the product of a \(x\) portion
and a \(y\) portion:
\[C_{i,j}(\Delta_u,\Delta_v) = c_{i}(\Delta_u) c_{j}(\Delta_v)\]
\[c_{i}(\Delta_u) = \int_{-\infty}^{\infty} f(\Delta_u + t_{00}x - i)g(x)dx\]
\[c_{j}(\Delta_v) = \int_{-\infty}^{\infty} f(\Delta_v + t_{11}y - j)g(y)dy\]

\section{Some filters}
gdk-pixbuf provides 4 standard filters for scaling, under the names ``NEAREST'',
``TILES'', ``BILINEAR'', and ``HYPER''. All of turn out to be separable
as discussed in the previous section. 
For ``NEAREST'' filter, the reconstruction function is simple replication
and the sampling function is a delta function\footnote{A delta function is an infinitely narrow spike, such that:
\[\int_{-\infty}^{\infty}\delta(x)f(x) = f(0)\]}:
\[f(t) = \cases{1, & if \(0 \le t \le 1\); \cr 
                0, & otherwise}\]
\[g(t) = \delta(t - 0.5)\]
For ``TILES'', the reconstruction function is again replication, but we
replace the delta-function for sampling with a box filter:
\[f(t) = \cases{1, & if \(0 \le t \le 1\); \cr 
                0, & otherwise}\]
\[g(t) = \cases{1, & if \(0 \le t \le 1\); \cr 
                0, & otherwise}\]
The ``HYPER'' filter (in practice, it was originally intended to be 
something else) uses bilinear interpolation for reconstruction and
a box filter for sampling:
\[f(t) = \cases{1 - |t - 0.5|, & if \(-0.5 \le t \le 1.5\); \cr 
                0, & otherwise}\]
\[g(t) = \cases{1, & if \(0 \le t \le 1\); \cr 
                0, & otherwise}\]
The ``BILINEAR'' filter is defined in a somewhat more complicated way;
the definition depends on the scale factor in the transform (\(t_{00}\)
or \(t_{01}]\). In the \(x\) direction, for \(t_{00} < 1\), it is
the same as for ``TILES'':
\[f_u(t) = \cases{1, & if \(0 \le t \le 1\); \cr 
                  0, & otherwise}\]
\[g_u(t) = \cases{1, & if \(0 \le t \le 1\); \cr 
                  0, & otherwise}\]
but for \(t_{10} > 1\), we use bilinear reconstruction and delta-function
sampling:
\[f_u(t) = \cases{1 - |t - 0.5|, & if \(-0.5 \le t \le 1.5\); \cr 
                  0, & otherwise}\]
\[g_u(t) = \delta(t - 0.5)\]
The behavior in the \(y\) direction depends in the same way on \(t_{11}\).
\end{document}