Non-orthogonal basis which offers better energy compaction in image compression
Given a signal $x(t)$ of energy $\mathcal E$, suppose that $x(t)$
can be expressed as $\sum_i x_i\psi_i(t)$. If the $\psi_i(t)$ are a
complete set of orthonormal signals, then 
$$\sum_i |x_i|^2 =\mathcal E$$
and one measure of energy compaction is the number of $x_i$ whose
squared magnitude is larger than some fraction of $\mathcal E$,
say $0.01\mathcal E$.  Another could be the smallest number of 
$x_i$ such that $\sum_i |x_i|^2$ captures $99\%$, say, of the energy.


Some sets of orthonormal signals can
be much better for representing a specific $x(t)$ than other sets of orthonormal
signals. For example, sinusoids and complex exponentials are commonly
used to represent periodic signals, and do a good job with respect to
energy compaction when used with low pass or bandpass signals.  But they
require infinitely many components to represent signals such as
sawtooth waves that can be represented using just one or two coefficients
using orthonormal Legendre polynomials.
So why are generations of electrical engineering students forced to
calculate Fourier series of sawtooth waves at all?  Well, Fourier
series simplify life
in other calculations such as what happens when the signal passes through
a linear time-invariant system while Legendre polynomials do not
If signal-dependent orthonormal bases are allowed, then if we start 
a Gram-Schmidt orthonormalization procedure beginning 
with taking $\psi_1(t)$ to be the unit energy signal
$x(t)/\sqrt{\mathcal E}$ as the first member of a
set of orthonormal signals, then this orthonormal set can be used to represent $x(t)$ with
$x_1 = \sqrt{\mathcal E}, x_i = 0, i &gt; 1$ for maximal compaction!  In fact,
we don't even need to bother to find $\psi_i(t)$ for $i &gt; 1$.
So, how much compaction can be achieved with signal-dependent
bases depends on the degree of dependence allowed between the
signal and the basis to be used, etc.
The representation of $x(t)$ with respect to a set of non-orthonormal signals
is more complicated to determine.  With orthonormal signals, the coefficient
$x_i$ of $\psi(t)$ is easily determined since
$$\langle x(t), \psi_i(t)\rangle = \langle \sum_k x_k\psi_k(t), \psi_i(t)\rangle
= \sum_k x_k \langle \psi_k(t), \psi_i(t)\rangle = x_i,$$
since 
$$ \langle \psi_k(t), \psi_i(t)\rangle =
\begin{cases}1, &amp; \text{if}~k = i,\\0,  &amp; \text{if}~k \neq i.,\end{cases}$$
while the simplification obtained via the orthonormality is not available
for non-orthonormal signals.  The question of how many of the $x_i$ are
needed to capture most of the energy is also difficult to determine
since the energy is not $\sum_i |x_i|^2$; it is necessary to take
the cross-products $x_ix_k$ into account as well.  


So, unless there are specific
reasons why a particular non-orthonormal basis is important to use
(e.g. it helps with things one needs to do in image processing
or video processing or audio processing etc), simply using such a 
basis because it provides better energy compaction might well be putting the cart
before the horse.  But, as always, your mileage may vary, and there may
be non-orthonormal signal bases that not only achieve better energy
compaction but also ease your life in other aspects.  It is just that
it is doubtful that a general theory exists saying that non-orthonormal
bases achieve better energy compaction than orthonormal bases.