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.. py:module:: dit.rate_distortion

Rate Distortion Theory


We use p to denote fixed probability distributions, and q to denote probability distributions that are optimized.

Rate-distortion theory :cite:`Cover2006` is a framework for studying optimal lossy compression. Given a distribution p(x), we wish to find q(\hat{x}|x) which compresses X as much as possible while limiting the amount of user-defined distortion, d(x, \hat{x}). The minimum rate (effectively, code book size) at which X can be compressed while maintaining a fixed distortion is known as the rate-distortion curve:

R(D) = \min_{q(\hat{x}|x), \langle d(x, \hat{x}) \rangle = D} \I{X : \hat{X}}

By introducing a Lagrange multiplier, we can transform this constrained optimization into an unconstrained one:

\mathcal{L} = \I{X : \hat{X}} + \beta \langle d(x, \hat{x}) \rangle

where minimizing at each \beta produces a point on the curve.


It is known that under the Hamming distortion (d(x, \hat{x}) = \left[ x \neq \hat{x} \right]) the rate-distortion function for a biased coin has the following solution: R(D) = \H{p} - \H{D}:

.. ipython::

   In [1]: from dit.rate_distortion import RDCurve

   In [2]: d = dit.Distribution(['0', '1'], [1/2, 1/2])

   @savefig rate_distortion.png width=1000 align=center
   In [3]: RDCurve(d, beta_num=26).plot();

Information Bottleneck

The information bottleneck :cite:`tishby2000information` is a form of rate-distortion where the distortion measure is given by:

d(x, \hat{x}) = D\left[~p(Y | x)~\mid\mid~q(Y | \hat{x})~\right]

where D is an arbitrary divergence measure, and \hat{X} - X - Y form a Markov chain. Traditionally, D is the :doc:`measures/divergences/kullback_leibler_divergence`, in which case the average distortion takes a particular form:

\langle d(x, \hat{x}) \rangle &= \sum_{x, \hat{x}} q(x, \hat{x}) \DKL{ p(Y | x) || q(Y | \hat{x}) } \\
                              &= \sum_{x, \hat{x}} q(x, \hat{x}) \sum_{y} p(y | x) \log_2 \frac{p(y | x)}{q(y | \hat{x})} \\
                              &= \sum_{x, \hat{x}, y} q(x, \hat{x}, y) \log_2 \frac{p(y | x) p(x) p(y) q(\hat{x})}{q(y | \hat{x}) p(x) p(y) q(\hat{x})} \\
                              &= \sum_{x, \hat{x}, y} q(x, \hat{x}, y) \log_2 \frac{p(y | x) p(x)}{p(x) p(y)} \frac{p(y)q(\hat{x})}{q(y | \hat{x}) q(\hat{x})} \\
                              &= \I{X : Y} - \I{\hat{X} : Y}

Since \I{X : Y} is constant over q(\hat{x} | x), it can be removed from the optimization. Furthermore,

\I{X : Y} - \I{\hat{X} : Y} &= (\I{X : Y | \hat{X}} + \I{X : Y : \hat{X}}) - (\I{Y : \hat{X} | X} + \I{X : Y : \hat{X}}) \\
                            &= \I{X : Y | \hat{X}} - \I{Y : \hat{X} | X} \\
                            &= \I{X : Y | \hat{X}}

where the final equality is due to the Markov chain. Due to all this, Information Bottleneck utilizes a "relevance" term, \I{\hat{X} : Y}, which replaces the average distortion in the Lagrangian:

\mathcal{L} = \I{X : \hat{X}} - \beta \I{\hat{X} : Y}

Though \I{X : Y | \hat{X}} is the most simplified form of the average distortion, it is faster to compute \I{\hat{X} : Y} during optimization.


Consider this distribution:

.. ipython::

   In [4]: d = dit.Distribution(['00', '02', '12', '21', '22'], [1/5]*5)

There are effectively three features that the first index, X, has regarding the second index, Y. We can find them using the standard information bottleneck:

.. ipython::

   In [5]: from dit.rate_distortion import IBCurve

   In [6]: IBCurve(d, beta_num=26).plot();

The information bottleneck curve of the distribution.

We can also find them utilizing the total variation:

.. ipython::

   In [7]: from dit.divergences.pmf import variational_distance

   In [8]: IBCurve(d, divergence=variational_distance).plot();

The information bottleneck curve of the distribution, using the non-standard total variation divergence measure for distortion.


The spiky behavior at low \beta values is due to numerical imprecision.


.. autoclass:: RDCurve

.. autoclass:: IBCurve