# Range encoder and decoder

This package contains a range encoder and a range decoder, which can encode integer data into strings using cumulative distribution functions (CDF). It is used by the higher-level entropy bottleneck class described in the previous section.

## Data and CDF values

The data to be encoded should be non-negative integers in half-open interval `[0, m)`. Then a CDF is represented as an integral vector of length `m + 1` where `CDF(i) = f(Pr(X < i) * 2^precision)` for i = 0,1,…,m, and `precision` is an attribute in range `0 < precision <= 16`. The function `f` maps real values into integers, e.g., round or floor. It is important that to encode a number `i`, `CDF(i + 1) - CDF(i)` cannot be zero.

Note that we used `Pr(X < i)` not `Pr(X <= i)`, and therefore CDF(0) = 0 always.

## RangeEncode: data shapes and CDF shapes

For each data element, its CDF has to be provided. Therefore if the shape of CDF should be `data.shape + (m + 1,)` in NumPy-like notation. For example, if `data` is a 2-D tensor of shape (10, 10) and its elements are in `[0, 64)`, then the CDF tensor should have shape (10, 10, 65).

This may make CDF tensor too large, and in many applications all data elements may have the same probability distribution. To handle this, `RangeEncode` supports limited broadcasting CDF into data. Broadcasting is limited in the following sense:

• All CDF axes but the last one is broadcasted into data but not the other way around,
• The number of CDF axes does not extend, i.e., `CDF.ndim == data.ndim + 1`.

In the previous example where data has shape (10, 10), the following are acceptable CDF shapes:

• (10, 10, 65)
• (1, 10, 65)
• (10, 1, 65)
• (1, 1, 65)

## RangeDecode

`RangeEncode` encodes neither data shape nor termination character. Therefore the decoder should know how many characters are encoded into the string, and `RangeDecode` takes the encoded data shape as the second argument. The same shape restrictions as `RangeEncode` inputs apply here.

## Example

``````data = tf.random_uniform((128, 128), 0, 10, dtype=tf.int32)

histogram = tf.bincount(data, minlength=10, maxlength=10)
cdf = tf.cumsum(histogram, exclusive=False)
# CDF should have length m + 1.